Delete webscout/Provider

webscout/Provider/BasedGPT.py

@@ -1,226 +0,0 @@
-import time
-import uuid
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support import expected_conditions as EC
-from selenium.webdriver.support.ui import WebDriverWait
-import click
-import requests
-from requests import get
-from uuid import uuid4
-from re import findall
-from requests.exceptions import RequestException
-from curl_cffi.requests import get, RequestsError
-import g4f
-from random import randint
-from PIL import Image
-import io
-import re
-import json
-import yaml
-from ..AIutel import Optimizers
-from ..AIutel import Conversation
-from ..AIutel import AwesomePrompts, sanitize_stream
-from ..AIbase import  Provider, AsyncProvider
-from webscout import exceptions
-from typing import Any, AsyncGenerator, Dict
-import logging
-import httpx
-
-class BasedGPT(Provider):
-    def __init__(
-        self,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-        system_prompt: str = "Be Helpful and Friendly",
-    ):
-        """Instantiates BasedGPT
-
-        Args:
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True.
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-            system_prompt (str, optional): System prompt for BasedGPT. Defaults to "Be Helpful and Friendly".
-        """
-        self.session = requests.Session()
-        self.is_conversation = is_conversation
-        self.max_tokens_to_sample = max_tokens
-        self.chat_endpoint = "https://www.basedgpt.chat/api/chat"
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.system_prompt = system_prompt
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        self.session.headers.update(
-            {"Content-Type": "application/json"}
-        )
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session.proxies = proxies
-
-    def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict:
-        """Chat with AI
-
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            raw (bool, optional): Stream back raw response as received. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-           dict : {}
-        ```json
-        {
-            "id": "chatcmpl-TaREJpBZsRVQFRFic1wIA7Q7XfnaD",
-            "object": "chat.completion",
-            "created": 1704623244,
-            "model": "gpt-3.5-turbo",
-            "usage": {
-                "prompt_tokens": 0,
-                "completion_tokens": 0,
-                "total_tokens": 0
-                },
-            "choices": [
-                {
-                    "message": {
-                        "role": "assistant",
-                        "content": "Hello! How can I assist you today?"
-                },
-                "finish_reason": "stop",
-                "index": 0
-                }
-            ]
-        }
-        ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-
-        payload = {
-            "messages": [
-                {"role": "system", "content": self.system_prompt},
-                {"role": "user", "content": conversation_prompt},
-            ],
-        }
-
-        def for_stream():
-            response = self.session.post(
-                self.chat_endpoint, json=payload, stream=True, timeout=self.timeout
-            )
-            if not response.ok:
-                raise exceptions.FailedToGenerateResponseError(
-                    f"Failed to generate response - ({response.status_code}, {response.reason}) - {response.text}"
-                )
-
-            message_load = ""
-            for value in response.iter_lines(
-                decode_unicode=True,
-                delimiter="",
-                chunk_size=self.stream_chunk_size,
-            ):
-                try:
-                    message_load += value
-                    yield value if raw else dict(text=message_load)
-                except json.decoder.JSONDecodeError:
-                    pass
-            self.last_response.update(dict(text=message_load))
-            self.conversation.update_chat_history(
-                prompt, self.get_message(self.last_response)
-            )
-
-        def for_non_stream():
-            for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else for_non_stream()
-
-    def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str:
-        """Generate response `str`
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-        def for_stream():
-            for response in self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            ):
-                yield self.get_message(response)
-
-        def for_non_stream():
-            return self.get_message(
-                self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else for_non_stream()
-
-    def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        return response["text"]

webscout/Provider/Berlin4h.py

@@ -1,211 +0,0 @@
-import requests
-import json
-import uuid
-from typing import Any, Dict, Optional
-from ..AIutel import Optimizers
-from ..AIutel import Conversation
-from ..AIutel import AwesomePrompts, sanitize_stream
-from ..AIbase import Provider, AsyncProvider
-from webscout import exceptions
-
-class Berlin4h(Provider):
-    """
-    A class to interact with the Berlin4h AI API.
-    """
-
-    def __init__(
-        self,
-        api_token: str = "3bf369cd84339603f8a5361e964f9ebe",
-        api_endpoint: str = "https://ai.berlin4h.top/api/chat/completions",
-        model: str = "gpt-3.5-turbo",
-        temperature: float = 0.9,
-        presence_penalty: float = 0,
-        frequency_penalty: float = 0,
-        max_tokens: int = 4000,
-        is_conversation: bool = True,
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-    ) -> None:
-        """
-        Initializes the Berlin4h API with given parameters.
-
-        Args:
-            api_token (str): The API token for authentication.
-            api_endpoint (str): The API endpoint to use for requests.
-            model (str): The AI model to use for text generation.
-            temperature (float): The temperature parameter for the model.
-            presence_penalty (float): The presence penalty parameter for the model.
-            frequency_penalty (float): The frequency penalty parameter for the model.
-            max_tokens (int): The maximum number of tokens to generate.
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True.
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-        """
-        self.api_token = api_token
-        self.api_endpoint = api_endpoint
-        self.model = model
-        self.temperature = temperature
-        self.presence_penalty = presence_penalty
-        self.frequency_penalty = frequency_penalty
-        self.max_tokens = max_tokens
-        self.parent_message_id: Optional[str] = None
-        self.session = requests.Session()
-        self.is_conversation = is_conversation
-        self.max_tokens_to_sample = max_tokens
-        self.stream_chunk_size = 1
-        self.timeout = timeout
-        self.last_response = {}
-        self.headers = {"Content-Type": "application/json", "Token": self.api_token}
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        self.session.headers.update(self.headers)
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session.proxies = proxies
-
-    def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> Dict[str, Any]:
-        """
-        Sends a prompt to the Berlin4h AI API and returns the response.
-
-        Args:
-            prompt: The text prompt to generate text from.
-            stream (bool, optional): Whether to stream the response. Defaults to False.
-            raw (bool, optional): Whether to return the raw response. Defaults to False.
-            optimizer (str, optional): The name of the optimizer to use. Defaults to None.
-            conversationally (bool, optional): Whether to chat conversationally. Defaults to False.
-
-        Returns:
-            The response from the API.
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-
-        payload: Dict[str, any] = {
-            "prompt": conversation_prompt,
-            "parentMessageId": self.parent_message_id or str(uuid.uuid4()),
-            "options": {
-                "model": self.model,
-                "temperature": self.temperature,
-                "presence_penalty": self.presence_penalty,
-                "frequency_penalty": self.frequency_penalty,
-                "max_tokens": self.max_tokens,
-            },
-        }
-
-        def for_stream():
-            response = self.session.post(
-                self.api_endpoint, json=payload, headers=self.headers, stream=True, timeout=self.timeout
-            )
-
-            if not response.ok:
-                raise exceptions.FailedToGenerateResponseError(
-                    f"Failed to generate response - ({response.status_code}, {response.reason})"
-                )
-
-            streaming_response = ""
-            # Collect the entire line before processing
-            for line in response.iter_lines(decode_unicode=True): 
-                if line:
-                    try:
-                        json_data = json.loads(line)
-                        content = json_data['content']
-                        if ">" in content: break
-                        streaming_response += content
-                        yield content if raw else dict(text=streaming_response)  # Yield accumulated response
-                    except:
-                        continue
-            self.last_response.update(dict(text=streaming_response))
-            self.conversation.update_chat_history(
-                prompt, self.get_message(self.last_response)
-            )
-
-        def for_non_stream():
-            for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else for_non_stream()
-
-    def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str:
-        """Generate response `str`
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-        def for_stream():
-            for response in self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            ):
-                yield self.get_message(response)
-
-        def for_non_stream():
-            return self.get_message(
-                self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else for_non_stream()
-
-    def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        return response["text"]

webscout/Provider/Blackboxai.py

@@ -1,440 +0,0 @@
-import time
-import uuid
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support import expected_conditions as EC
-from selenium.webdriver.support.ui import WebDriverWait
-import click
-import requests
-from requests import get
-from uuid import uuid4
-from re import findall
-from requests.exceptions import RequestException
-from curl_cffi.requests import get, RequestsError
-import g4f
-from random import randint
-from PIL import Image
-import io
-import re
-import json
-import yaml
-from ..AIutel import Optimizers
-from ..AIutel import Conversation
-from ..AIutel import AwesomePrompts, sanitize_stream
-from ..AIbase import  Provider, AsyncProvider
-from Helpingai_T2 import Perplexity
-from webscout import exceptions
-from typing import Any, AsyncGenerator, Dict
-import logging
-import httpx
-
-#------------------------------------------------------BLACKBOXAI--------------------------------------------------------  
-class BLACKBOXAI:
-    def __init__(
-        self,
-        is_conversation: bool = True,
-        max_tokens: int = 8000,
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-        model: str = None,
-    ):
-        """Instantiates BLACKBOXAI
-
-        Args:
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-            model (str, optional): Model name. Defaults to "Phind Model".
-        """
-        self.session = requests.Session()
-        self.max_tokens_to_sample = max_tokens
-        self.is_conversation = is_conversation
-        self.chat_endpoint = "https://www.blackbox.ai/api/chat"
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.model = model
-        self.previewToken: str = None
-        self.userId: str = ""
-        self.codeModelMode: bool = True
-        self.id: str = ""
-        self.agentMode: dict = {}
-        self.trendingAgentMode: dict = {}
-        self.isMicMode: bool = False
-
-        self.headers = {
-            "Content-Type": "application/json",
-            "User-Agent": "",
-            "Accept": "*/*",
-            "Accept-Encoding": "Identity",
-        }
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        self.session.headers.update(self.headers)
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session.proxies = proxies
-
-    def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict:
-        """Chat with AI
-
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            raw (bool, optional): Stream back raw response as received. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-           dict : {}
-        ```json
-        {
-           "text" : "print('How may I help you today?')"
-        }
-        ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-
-        self.session.headers.update(self.headers)
-        payload = {
-            "messages": [
-                # json.loads(prev_messages),
-                {"content": conversation_prompt, "role": "user"}
-            ],
-            "id": self.id,
-            "previewToken": self.previewToken,
-            "userId": self.userId,
-            "codeModelMode": self.codeModelMode,
-            "agentMode": self.agentMode,
-            "trendingAgentMode": self.trendingAgentMode,
-            "isMicMode": self.isMicMode,
-        }
-
-        def for_stream():
-            response = self.session.post(
-                self.chat_endpoint, json=payload, stream=True, timeout=self.timeout
-            )
-            if (
-                not response.ok
-                or not response.headers.get("Content-Type")
-                == "text/plain; charset=utf-8"
-            ):
-                raise Exception(
-                    f"Failed to generate response - ({response.status_code}, {response.reason}) - {response.text}"
-                )
-            streaming_text = ""
-            for value in response.iter_lines(
-                decode_unicode=True,
-                chunk_size=self.stream_chunk_size,
-                delimiter="\n",
-            ):
-                try:
-                    if bool(value):
-                        streaming_text += value + ("\n" if stream else "")
-
-                        resp = dict(text=streaming_text)
-                        self.last_response.update(resp)
-                        yield value if raw else resp
-                except json.decoder.JSONDecodeError:
-                    pass
-            self.conversation.update_chat_history(
-                prompt, self.get_message(self.last_response)
-            )
-
-        def for_non_stream():
-            for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else for_non_stream()
-
-    def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str:
-        """Generate response `str`
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-        def for_stream():
-            for response in self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            ):
-                yield self.get_message(response)
-
-        def for_non_stream():
-            return self.get_message(
-                self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else for_non_stream()
-
-    def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        return response["text"]
-    @staticmethod
-    def chat_cli(prompt):
-        """Sends a request to the BLACKBOXAI API and processes the response."""
-        blackbox_ai = BLACKBOXAI()  # Initialize a BLACKBOXAI instance
-        response = blackbox_ai.ask(prompt)  # Perform a chat with the given prompt
-        processed_response = blackbox_ai.get_message(response)  # Process the response
-        print(processed_response)
-class AsyncBLACKBOXAI(AsyncProvider):
-    def __init__(
-        self,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-        model: str = None,
-    ):
-        """Instantiates BLACKBOXAI
-
-        Args:
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-            model (str, optional): Model name. Defaults to "Phind Model".
-        """
-        self.max_tokens_to_sample = max_tokens
-        self.is_conversation = is_conversation
-        self.chat_endpoint = "https://www.blackbox.ai/api/chat"
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.model = model
-        self.previewToken: str = None
-        self.userId: str = ""
-        self.codeModelMode: bool = True
-        self.id: str = ""
-        self.agentMode: dict = {}
-        self.trendingAgentMode: dict = {}
-        self.isMicMode: bool = False
-
-        self.headers = {
-            "Content-Type": "application/json",
-            "User-Agent": "",
-            "Accept": "*/*",
-            "Accept-Encoding": "Identity",
-        }
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session = httpx.AsyncClient(headers=self.headers, proxies=proxies)
-
-    async def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict | AsyncGenerator:
-        """Chat with AI asynchronously.
-
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            raw (bool, optional): Stream back raw response as received. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-           dict|AsyncGenerator : ai content
-        ```json
-        {
-           "text" : "print('How may I help you today?')"
-        }
-        ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-
-        payload = {
-            "messages": [
-                # json.loads(prev_messages),
-                {"content": conversation_prompt, "role": "user"}
-            ],
-            "id": self.id,
-            "previewToken": self.previewToken,
-            "userId": self.userId,
-            "codeModelMode": self.codeModelMode,
-            "agentMode": self.agentMode,
-            "trendingAgentMode": self.trendingAgentMode,
-            "isMicMode": self.isMicMode,
-        }
-
-        async def for_stream():
-            async with self.session.stream(
-                "POST", self.chat_endpoint, json=payload, timeout=self.timeout
-            ) as response:
-                if (
-                    not response.is_success
-                    or not response.headers.get("Content-Type")
-                    == "text/plain; charset=utf-8"
-                ):
-                    raise exceptions.FailedToGenerateResponseError(
-                        f"Failed to generate response - ({response.status_code}, {response.reason_phrase})"
-                    )
-                streaming_text = ""
-                async for value in response.aiter_lines():
-                    try:
-                        if bool(value):
-                            streaming_text += value + ("\n" if stream else "")
-                            resp = dict(text=streaming_text)
-                            self.last_response.update(resp)
-                            yield value if raw else resp
-                    except json.decoder.JSONDecodeError:
-                        pass
-            self.conversation.update_chat_history(
-                prompt, await self.get_message(self.last_response)
-            )
-
-        async def for_non_stream():
-            async for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else await for_non_stream()
-
-    async def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str | AsyncGenerator:
-        """Generate response `str` asynchronously.
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str|AsyncGenerator: Response generated
-        """
-
-        async def for_stream():
-            async_ask = await self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            )
-            async for response in async_ask:
-                yield await self.get_message(response)
-
-        async def for_non_stream():
-            return await self.get_message(
-                await self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else await for_non_stream()
-
-    async def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        return response["text"]

webscout/Provider/ChatGPTUK.py

@@ -1,214 +0,0 @@
-import requests
-from typing import Any, AsyncGenerator, Dict, Optional
-import json
-import re
-
-from ..AIutel import Optimizers
-from ..AIutel import Conversation
-from ..AIutel import AwesomePrompts, sanitize_stream
-from ..AIbase import  Provider, AsyncProvider
-from webscout import exceptions
-
-
-class ChatGPTUK(Provider):
-    """
-    A class to interact with the ChatGPT UK API.
-    """
-
-    def __init__(
-        self,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        temperature: float = 0.9,
-        presence_penalty: float = 0,
-        frequency_penalty: float = 0,
-        top_p: float = 1,
-        model: str = "google-gemini-pro",
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-    ) -> None:
-        """
-        Initializes the ChatGPTUK API with given parameters.
-
-        Args:
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True.
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            temperature (float, optional): Charge of the generated text's randomness. Defaults to 0.9.
-            presence_penalty (float, optional): Chances of topic being repeated. Defaults to 0.
-            frequency_penalty (float, optional): Chances of word being repeated. Defaults to 0.
-            top_p (float, optional): Sampling threshold during inference time. Defaults to 1.
-            model (str, optional): LLM model name. Defaults to "google-gemini-pro".
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-        """
-        self.session = requests.Session()
-        self.is_conversation = is_conversation
-        self.max_tokens_to_sample = max_tokens
-        self.api_endpoint = "https://free.chatgpt.org.uk/api/openai/v1/chat/completions"
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.model = model
-        self.temperature = temperature
-        self.presence_penalty = presence_penalty
-        self.frequency_penalty = frequency_penalty
-        self.top_p = top_p
-        self.headers = {"Content-Type": "application/json"}
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        self.session.headers.update(self.headers)
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session.proxies = proxies
-
-    def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict:
-        """Chat with AI
-
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            raw (bool, optional): Stream back raw response as received. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-           dict : {}
-        ```json
-        {
-           "text" : "How may I assist you today?"
-        }
-        ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-
-        self.session.headers.update(self.headers)
-        payload = {
-            "messages": [
-                {"role": "system", "content": "Keep your responses long and detailed"},
-                {"role": "user", "content": conversation_prompt}
-            ],
-            "stream": True,
-            "model": self.model,
-            "temperature": self.temperature,
-            "presence_penalty": self.presence_penalty,
-            "frequency_penalty": self.frequency_penalty,
-            "top_p": self.top_p,
-            "max_tokens": self.max_tokens_to_sample
-        }
-
-        def for_stream():
-            response = self.session.post(
-                self.api_endpoint, json=payload, stream=True, timeout=self.timeout
-            )
-            if not response.ok:
-                raise exceptions.FailedToGenerateResponseError(
-                    f"Failed to generate response - ({response.status_code}, {response.reason}) - {response.text}"
-                )
-
-            streaming_response = ""
-            for line in response.iter_lines(decode_unicode=True, chunk_size=1):
-                if line:
-                    modified_line = re.sub("data:", "", line)
-                    try:
-                        json_data = json.loads(modified_line)
-                        content = json_data['choices'][0]['delta']['content']
-                        streaming_response += content
-                        yield content if raw else dict(text=streaming_response)
-                    except:
-                        continue
-            self.last_response.update(dict(text=streaming_response))
-            self.conversation.update_chat_history(
-                prompt, self.get_message(self.last_response)
-            )
-
-        def for_non_stream():
-            for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else for_non_stream()
-
-    def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str:
-        """Generate response `str`
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-        def for_stream():
-            for response in self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            ):
-                yield self.get_message(response)
-
-        def for_non_stream():
-            return self.get_message(
-                self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else for_non_stream()
-
-    def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        return response["text"]

webscout/Provider/Cohere.py

@@ -1,223 +0,0 @@
-import time
-import uuid
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support import expected_conditions as EC
-from selenium.webdriver.support.ui import WebDriverWait
-import click
-import requests
-from requests import get
-from uuid import uuid4
-from re import findall
-from requests.exceptions import RequestException
-from curl_cffi.requests import get, RequestsError
-import g4f
-from random import randint
-from PIL import Image
-import io
-import re
-import json
-import yaml
-from ..AIutel import Optimizers
-from ..AIutel import Conversation
-from ..AIutel import AwesomePrompts, sanitize_stream
-from ..AIbase import  Provider, AsyncProvider
-from Helpingai_T2 import Perplexity
-from webscout import exceptions
-from typing import Any, AsyncGenerator, Dict
-import logging
-import httpx
-#-----------------------------------------------Cohere--------------------------------------------
-class Cohere(Provider):
-    def __init__(
-        self,
-        api_key: str,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        model: str = "command-r-plus",
-        temperature: float = 0.7,
-        system_prompt: str = "You are helpful AI",
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-        top_k: int = -1,
-        top_p: float = 0.999,
-    ):
-        """Initializes Cohere
-
-        Args:
-            api_key (str): Cohere API key.
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True.
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            model (str, optional): Model to use for generating text. Defaults to "command-r-plus".
-            temperature (float, optional): Diversity of the generated text. Higher values produce more diverse outputs.
-                            Defaults to 0.7.
-            system_prompt (str, optional): A system_prompt or context to set the style or tone of the generated text. 
-                            Defaults to "You are helpful AI".
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-        """
-        self.session = requests.Session()
-        self.is_conversation = is_conversation
-        self.max_tokens_to_sample = max_tokens
-        self.api_key = api_key
-        self.model = model
-        self.temperature = temperature
-        self.system_prompt = system_prompt
-        self.chat_endpoint = "https://production.api.os.cohere.ai/coral/v1/chat"
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.api_key}",
-        }
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        self.session.headers.update(self.headers)
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session.proxies = proxies
-
-    def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict:
-        """Chat with AI
-
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            raw (bool, optional): Stream back raw response as received. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-           dict : {}
-        ```json
-        {
-           "text" : "How may I assist you today?"
-        }
-        ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-        self.session.headers.update(self.headers)
-        payload = {
-            "message": conversation_prompt,
-            "model": self.model,
-            "temperature": self.temperature,
-            "preamble": self.system_prompt,
-        }
-
-        def for_stream():
-            response = self.session.post(
-                self.chat_endpoint, json=payload, stream=True, timeout=self.timeout
-            )
-            if not response.ok:
-                raise Exception(
-                    f"Failed to generate response - ({response.status_code}, {response.reason}) - {response.text}"
-                )
-
-            for value in response.iter_lines(
-                decode_unicode=True,
-                chunk_size=self.stream_chunk_size,
-            ):
-                try:
-                    resp = json.loads(value.strip().split("\n")[-1])
-                    self.last_response.update(resp)
-                    yield value if raw else resp
-                except json.decoder.JSONDecodeError:
-                    pass
-            self.conversation.update_chat_history(
-                prompt, self.get_message(self.last_response)
-            )
-
-        def for_non_stream():
-            # let's make use of stream
-            for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else for_non_stream()
-
-    def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str:
-        """Generate response `str`
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-        def for_stream():
-            for response in self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            ):
-                yield self.get_message(response)
-
-        def for_non_stream():
-            return self.get_message(
-                self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else for_non_stream()
-
-    def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        return response["result"]["chatStreamEndEvent"]["response"]["text"]

webscout/Provider/Gemini.py

@@ -1,217 +0,0 @@
-import time
-import uuid
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support import expected_conditions as EC
-from selenium.webdriver.support.ui import WebDriverWait
-import click
-import requests
-from requests import get
-from uuid import uuid4
-from re import findall
-from requests.exceptions import RequestException
-from curl_cffi.requests import get, RequestsError
-import g4f
-from random import randint
-from PIL import Image
-import io
-import re
-import json
-import yaml
-from ..AIutel import Optimizers
-from ..AIutel import Conversation
-from ..AIutel import AwesomePrompts, sanitize_stream
-from ..AIbase import  Provider, AsyncProvider
-from Helpingai_T2 import Perplexity
-from webscout import exceptions
-from typing import Any, AsyncGenerator, Dict
-import logging
-import httpx    
-from Bard import Chatbot
-import logging
-from os import path
-from json import load
-from json import dumps
-import warnings
-logging.getLogger("httpx").setLevel(logging.ERROR)
-warnings.simplefilter("ignore", category=UserWarning)
-class GEMINI(Provider):
-    def __init__(
-        self,
-        cookie_file: str,
-        proxy: dict = {},
-        timeout: int = 30,
-    ):
-        """Initializes GEMINI
-
-        Args:
-            cookie_file (str): Path to `bard.google.com.cookies.json` file
-            proxy (dict, optional): Http request proxy. Defaults to {}.
-            timeout (int, optional): Http request timeout. Defaults to 30.
-        """
-        self.conversation = Conversation(False)
-        self.session_auth1 = None
-        self.session_auth2 = None
-        assert isinstance(
-            cookie_file, str
-        ), f"cookie_file should be of {str} only not '{type(cookie_file)}'"
-        if path.isfile(cookie_file):
-            # let's assume auth is a path to exported .json cookie-file
-            with open(cookie_file) as fh:
-                entries = load(fh)
-            for entry in entries:
-                if entry["name"] == "__Secure-1PSID":
-                    self.session_auth1 = entry["value"]
-                elif entry["name"] == "__Secure-1PSIDTS":
-                    self.session_auth2 = entry["value"]
-
-            assert all(
-                [self.session_auth1, self.session_auth2]
-            ), f"Failed to extract the required cookie value from file '{cookie_file}'"
-        else:
-            raise Exception(f"{cookie_file} is not a valid file path")
-
-        self.session = Chatbot(self.session_auth1, self.session_auth2, proxy, timeout)
-        self.last_response = {}
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-
-    def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict:
-        """Chat with AI
-
-            Args:
-                prompt (str): Prompt to be send.
-                stream (bool, optional): Flag for streaming response. Defaults to False.
-                raw (bool, optional): Stream back raw response as received. Defaults to False.
-                optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defeaults to None
-                conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-            Returns:
-               dict : {}
-            ```json
-            {
-                "content": "General Kenobi! \n\n(I couldn't help but respond with the iconic Star Wars greeting since you used it first. )\n\nIs there anything I can help you with today?\n[Image of Hello there General Kenobi]",
-                "conversation_id": "c_f13f6217f9a997aa",
-                "response_id": "r_d3665f95975c368f",
-                "factualityQueries": null,
-                "textQuery": [
-                    "hello there",
-                    1
-                    ],
-                "choices": [
-                    {
-                        "id": "rc_ea075c9671bfd8cb",
-                        "content": [
-                            "General Kenobi! \n\n(I couldn't help but respond with the iconic Star Wars greeting since you used it first. )\n\nIs there anything I can help you with today?\n[Image of Hello there General Kenobi]"
-                        ]
-                    },
-                    {
-                        "id": "rc_de6dd3fb793a5402",
-                        "content": [
-                            "General Kenobi! (or just a friendly hello, whichever you prefer!). \n\nI see you're a person of culture as well. *Star Wars* references are always appreciated.  \n\nHow can I help you today?\n"
-                            ]
-                    },
-                {
-                    "id": "rc_a672ac089caf32db",
-                    "content": [
-                        "General Kenobi! (or just a friendly hello if you're not a Star Wars fan!). \n\nHow can I help you today? Feel free to ask me anything, or tell me what you'd like to chat about. I'm here to assist in any way I can.\n[Image of Obi-Wan Kenobi saying hello there]"
-                    ]
-                }
-            ],
-
-            "images": [
-                "https://i.pinimg.com/originals/40/74/60/407460925c9e419d82b93313f0b42f71.jpg"
-            ]
-        }
-
-            ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-
-        def for_stream():
-            response = self.session.ask(prompt)
-            self.last_response.update(response)
-            self.conversation.update_chat_history(
-                prompt, self.get_message(self.last_response)
-            )
-            yield dumps(response) if raw else response
-
-        def for_non_stream():
-            # let's make use of stream
-            for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else for_non_stream()
-
-    def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str:
-        """Generate response `str`
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-        def for_stream():
-            for response in self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            ):
-                yield self.get_message(response)
-
-        def for_non_stream():
-            return self.get_message(
-                self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else for_non_stream()
-
-    def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        return response["content"]
-
-    def reset(self):
-        """Reset the current conversation"""
-        self.session.async_chatbot.conversation_id = ""
-        self.session.async_chatbot.response_id = ""
-        self.session.async_chatbot.choice_id = ""

webscout/Provider/Groq.py

@@ -1,512 +0,0 @@
-import time
-import uuid
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support import expected_conditions as EC
-from selenium.webdriver.support.ui import WebDriverWait
-import click
-import requests
-from requests import get
-from uuid import uuid4
-from re import findall
-from requests.exceptions import RequestException
-from curl_cffi.requests import get, RequestsError
-import g4f
-from random import randint
-from PIL import Image
-import io
-import re
-import json
-import yaml
-from ..AIutel import Optimizers
-from ..AIutel import Conversation
-from ..AIutel import AwesomePrompts, sanitize_stream
-from ..AIbase import  Provider, AsyncProvider
-from Helpingai_T2 import Perplexity
-from webscout import exceptions
-from typing import Any, AsyncGenerator, Dict
-import logging
-import httpx
-
-class GROQ(Provider):
-    def __init__(
-        self,
-        api_key: str,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        temperature: float = 1,
-        presence_penalty: int = 0,
-        frequency_penalty: int = 0,
-        top_p: float = 1,
-        model: str = "mixtral-8x7b-32768",
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-    ):
-        """Instantiates GROQ
-
-        Args:
-            api_key (key): GROQ's API key.
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True.
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            temperature (float, optional): Charge of the generated text's randomness. Defaults to 1.
-            presence_penalty (int, optional): Chances of topic being repeated. Defaults to 0.
-            frequency_penalty (int, optional): Chances of word being repeated. Defaults to 0.
-            top_p (float, optional): Sampling threshold during inference time. Defaults to 0.999.
-            model (str, optional): LLM model name. Defaults to "gpt-3.5-turbo".
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-        """
-        self.session = requests.Session()
-        self.is_conversation = is_conversation
-        self.max_tokens_to_sample = max_tokens
-        self.api_key = api_key
-        self.model = model
-        self.temperature = temperature
-        self.presence_penalty = presence_penalty
-        self.frequency_penalty = frequency_penalty
-        self.top_p = top_p
-        self.chat_endpoint = "https://api.groq.com/openai/v1/chat/completions"
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.api_key}",
-        }
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        self.session.headers.update(self.headers)
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session.proxies = proxies
-
-    def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict:
-        """Chat with AI
-
-                Args:
-                    prompt (str): Prompt to be send.
-                    stream (bool, optional): Flag for streaming response. Defaults to False.
-                    raw (bool, optional): Stream back raw response as received. Defaults to False.
-                    optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-                    conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-                Returns:
-                   dict : {}
-                ```json
-        {
-            "id": "c0c8d139-d2b9-9909-8aa1-14948bc28404",
-            "object": "chat.completion",
-            "created": 1710852779,
-            "model": "mixtral-8x7b-32768",
-            "choices": [
-                {
-                    "index": 0,
-                    "message": {
-                        "role": "assistant",
-                        "content": "Hello! How can I assist you today? I'm here to help answer your questions and engage in conversation on a wide variety of topics. Feel free to ask me anything!"
-                    },
-                    "logprobs": null,
-                    "finish_reason": "stop"
-                }
-            ],
-            "usage": {
-                "prompt_tokens": 47,
-                "prompt_time": 0.03,
-                "completion_tokens": 37,
-                "completion_time": 0.069,
-                "total_tokens": 84,
-                "total_time": 0.099
-            },
-            "system_fingerprint": null
-        }
-                ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-        self.session.headers.update(self.headers)
-        payload = {
-            "frequency_penalty": self.frequency_penalty,
-            "messages": [{"content": conversation_prompt, "role": "user"}],
-            "model": self.model,
-            "presence_penalty": self.presence_penalty,
-            "stream": stream,
-            "temperature": self.temperature,
-            "top_p": self.top_p,
-        }
-
-        def for_stream():
-            response = self.session.post(
-                self.chat_endpoint, json=payload, stream=True, timeout=self.timeout
-            )
-            if not response.ok:
-                raise Exception(
-                    f"Failed to generate response - ({response.status_code}, {response.reason}) - {response.text}"
-                )
-
-            message_load = ""
-            for value in response.iter_lines(
-                decode_unicode=True,
-                delimiter="" if raw else "data:",
-                chunk_size=self.stream_chunk_size,
-            ):
-                try:
-                    resp = json.loads(value)
-                    incomplete_message = self.get_message(resp)
-                    if incomplete_message:
-                        message_load += incomplete_message
-                        resp["choices"][0]["delta"]["content"] = message_load
-                        self.last_response.update(resp)
-                        yield value if raw else resp
-                    elif raw:
-                        yield value
-                except json.decoder.JSONDecodeError:
-                    pass
-            self.conversation.update_chat_history(
-                prompt, self.get_message(self.last_response)
-            )
-
-        def for_non_stream():
-            response = self.session.post(
-                self.chat_endpoint, json=payload, stream=False, timeout=self.timeout
-            )
-            if not response.ok:
-                raise Exception(
-                    f"Failed to generate response - ({response.status_code}, {response.reason}) - {response.text}"
-                )
-            resp = response.json()
-            self.last_response.update(resp)
-            self.conversation.update_chat_history(
-                prompt, self.get_message(self.last_response)
-            )
-            return resp
-
-        return for_stream() if stream else for_non_stream()
-
-    def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str:
-        """Generate response `str`
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-        def for_stream():
-            for response in self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            ):
-                yield self.get_message(response)
-
-        def for_non_stream():
-            return self.get_message(
-                self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else for_non_stream()
-
-    def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        try:
-            if response["choices"][0].get("delta"):
-                return response["choices"][0]["delta"]["content"]
-            return response["choices"][0]["message"]["content"]
-        except KeyError:
-            return ""
-class AsyncGROQ(AsyncProvider):
-    def __init__(
-        self,
-        api_key: str,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        temperature: float = 1,
-        presence_penalty: int = 0,
-        frequency_penalty: int = 0,
-        top_p: float = 1,
-        model: str = "mixtral-8x7b-32768",
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-    ):
-        """Instantiates GROQ
-
-        Args:
-            api_key (key): GROQ's API key.
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True.
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            temperature (float, optional): Charge of the generated text's randomness. Defaults to 1.
-            presence_penalty (int, optional): Chances of topic being repeated. Defaults to 0.
-            frequency_penalty (int, optional): Chances of word being repeated. Defaults to 0.
-            top_p (float, optional): Sampling threshold during inference time. Defaults to 0.999.
-            model (str, optional): LLM model name. Defaults to "gpt-3.5-turbo".
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-        """
-        self.is_conversation = is_conversation
-        self.max_tokens_to_sample = max_tokens
-        self.api_key = api_key
-        self.model = model
-        self.temperature = temperature
-        self.presence_penalty = presence_penalty
-        self.frequency_penalty = frequency_penalty
-        self.top_p = top_p
-        self.chat_endpoint = "https://api.groq.com/openai/v1/chat/completions"
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.api_key}",
-        }
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session = httpx.AsyncClient(headers=self.headers, proxies=proxies)
-
-    async def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict | AsyncGenerator:
-        """Chat with AI asynchronously.
-
-                Args:
-                    prompt (str): Prompt to be send.
-                    stream (bool, optional): Flag for streaming response. Defaults to False.
-                    raw (bool, optional): Stream back raw response as received. Defaults to False.
-                    optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-                    conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-                Returns:
-                   dict|AsyncGenerator : ai content
-                ```json
-        {
-            "id": "c0c8d139-d2b9-9909-8aa1-14948bc28404",
-            "object": "chat.completion",
-            "created": 1710852779,
-            "model": "mixtral-8x7b-32768",
-            "choices": [
-                {
-                    "index": 0,
-                    "message": {
-                        "role": "assistant",
-                        "content": "Hello! How can I assist you today? I'm here to help answer your questions and engage in conversation on a wide variety of topics. Feel free to ask me anything!"
-                    },
-                    "logprobs": null,
-                    "finish_reason": "stop"
-                }
-            ],
-            "usage": {
-                "prompt_tokens": 47,
-                "prompt_time": 0.03,
-                "completion_tokens": 37,
-                "completion_time": 0.069,
-                "total_tokens": 84,
-                "total_time": 0.099
-            },
-            "system_fingerprint": null
-        }
-                ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-        payload = {
-            "frequency_penalty": self.frequency_penalty,
-            "messages": [{"content": conversation_prompt, "role": "user"}],
-            "model": self.model,
-            "presence_penalty": self.presence_penalty,
-            "stream": stream,
-            "temperature": self.temperature,
-            "top_p": self.top_p,
-        }
-
-        async def for_stream():
-            async with self.session.stream(
-                "POST", self.chat_endpoint, json=payload, timeout=self.timeout
-            ) as response:
-                if not response.is_success:
-                    raise Exception(
-                        f"Failed to generate response - ({response.status_code}, {response.reason_phrase})"
-                    )
-
-                message_load = ""
-                intro_value = "data:"
-                async for value in response.aiter_lines():
-                    try:
-                        if value.startswith(intro_value):
-                            value = value[len(intro_value) :]
-                        resp = json.loads(value)
-                        incomplete_message = await self.get_message(resp)
-                        if incomplete_message:
-                            message_load += incomplete_message
-                            resp["choices"][0]["delta"]["content"] = message_load
-                            self.last_response.update(resp)
-                            yield value if raw else resp
-                        elif raw:
-                            yield value
-                    except json.decoder.JSONDecodeError:
-                        pass
-            self.conversation.update_chat_history(
-                prompt, await self.get_message(self.last_response)
-            )
-
-        async def for_non_stream():
-            response = httpx.post(
-                self.chat_endpoint, json=payload, timeout=self.timeout
-            )
-            if not response.is_success:
-                raise Exception(
-                    f"Failed to generate response - ({response.status_code}, {response.reason_phrase})"
-                )
-            resp = response.json()
-            self.last_response.update(resp)
-            self.conversation.update_chat_history(
-                prompt, await self.get_message(self.last_response)
-            )
-            return resp
-
-        return for_stream() if stream else await for_non_stream()
-
-    async def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str | AsyncGenerator:
-        """Generate response `str` asynchronously.
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str|AsyncGenerator: Response generated
-        """
-
-        async def for_stream():
-            async_ask = await self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            )
-            async for response in async_ask:
-                yield await self.get_message(response)
-
-        async def for_non_stream():
-            return await self.get_message(
-                await self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else await for_non_stream()
-
-    async def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        try:
-            if response["choices"][0].get("delta"):
-                return response["choices"][0]["delta"]["content"]
-            return response["choices"][0]["message"]["content"]
-        except KeyError:
-            return ""

webscout/Provider/Koboldai.py

@@ -1,402 +0,0 @@
-import time
-import uuid
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support import expected_conditions as EC
-from selenium.webdriver.support.ui import WebDriverWait
-import click
-import requests
-from requests import get
-from uuid import uuid4
-from re import findall
-from requests.exceptions import RequestException
-from curl_cffi.requests import get, RequestsError
-import g4f
-from random import randint
-from PIL import Image
-import io
-import re
-import json
-import yaml
-from ..AIutel import Optimizers
-from ..AIutel import Conversation
-from ..AIutel import AwesomePrompts, sanitize_stream
-from ..AIbase import  Provider, AsyncProvider
-from Helpingai_T2 import Perplexity
-from webscout import exceptions
-from typing import Any, AsyncGenerator, Dict
-import logging
-import httpx
-#------------------------------------------------------KOBOLDAI-----------------------------------------------------------
-class KOBOLDAI(Provider):
-    def __init__(
-        self,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        temperature: float = 1,
-        top_p: float = 1,
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-    ):
-        """Instantiate TGPT
-
-        Args:
-            is_conversation (str, optional): Flag for chatting conversationally. Defaults to True.
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            temperature (float, optional): Charge of the generated text's randomness. Defaults to 0.2.
-            top_p (float, optional): Sampling threshold during inference time. Defaults to 0.999.
-            timeout (int, optional): Http requesting timeout. Defaults to 30
-            intro (str, optional): Conversation introductory prompt. Defaults to `Conversation.intro`.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional) : Http reqiuest proxies (socks). Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-        """
-        self.session = requests.Session()
-        self.is_conversation = is_conversation
-        self.max_tokens_to_sample = max_tokens
-        self.temperature = temperature
-        self.top_p = top_p
-        self.chat_endpoint = (
-            "https://koboldai-koboldcpp-tiefighter.hf.space/api/extra/generate/stream"
-        )
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.headers = {
-            "Content-Type": "application/json",
-            "Accept": "application/json",
-        }
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        self.session.headers.update(self.headers)
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session.proxies = proxies
-
-    def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict:
-        """Chat with AI
-
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            raw (bool, optional): Stream back raw response as received. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-           dict : {}
-        ```json
-        {
-           "token" : "How may I assist you today?"
-        }
-        ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-
-        self.session.headers.update(self.headers)
-        payload = {
-            "prompt": conversation_prompt,
-            "temperature": self.temperature,
-            "top_p": self.top_p,
-        }
-
-        def for_stream():
-            response = self.session.post(
-                self.chat_endpoint, json=payload, stream=True, timeout=self.timeout
-            )
-            if not response.ok:
-                raise Exception(
-                    f"Failed to generate response - ({response.status_code}, {response.reason}) - {response.text}"
-                )
-
-            message_load = ""
-            for value in response.iter_lines(
-                decode_unicode=True,
-                delimiter="" if raw else "event: message\ndata:",
-                chunk_size=self.stream_chunk_size,
-            ):
-                try:
-                    resp = json.loads(value)
-                    message_load += self.get_message(resp)
-                    resp["token"] = message_load
-                    self.last_response.update(resp)
-                    yield value if raw else resp
-                except json.decoder.JSONDecodeError:
-                    pass
-            self.conversation.update_chat_history(
-                prompt, self.get_message(self.last_response)
-            )
-
-        def for_non_stream():
-            # let's make use of stream
-            for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else for_non_stream()
-
-    def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str:
-        """Generate response `str`
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-        def for_stream():
-            for response in self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            ):
-                yield self.get_message(response)
-
-        def for_non_stream():
-            return self.get_message(
-                self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else for_non_stream()
-
-    def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        return response.get("token")
-class AsyncKOBOLDAI(AsyncProvider):
-    def __init__(
-        self,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        temperature: float = 1,
-        top_p: float = 1,
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-    ):
-        """Instantiate TGPT
-
-        Args:
-            is_conversation (str, optional): Flag for chatting conversationally. Defaults to True.
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            temperature (float, optional): Charge of the generated text's randomness. Defaults to 0.2.
-            top_p (float, optional): Sampling threshold during inference time. Defaults to 0.999.
-            timeout (int, optional): Http requesting timeout. Defaults to 30
-            intro (str, optional): Conversation introductory prompt. Defaults to `Conversation.intro`.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional) : Http reqiuest proxies (socks). Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-        """
-        self.is_conversation = is_conversation
-        self.max_tokens_to_sample = max_tokens
-        self.temperature = temperature
-        self.top_p = top_p
-        self.chat_endpoint = (
-            "https://koboldai-koboldcpp-tiefighter.hf.space/api/extra/generate/stream"
-        )
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.headers = {
-            "Content-Type": "application/json",
-            "Accept": "application/json",
-        }
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session = httpx.AsyncClient(headers=self.headers, proxies=proxies)
-
-    async def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict | AsyncGenerator:
-        """Chat with AI asynchronously.
-
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            raw (bool, optional): Stream back raw response as received. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-           dict|AsyncGenerator : ai content
-        ```json
-        {
-           "token" : "How may I assist you today?"
-        }
-        ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-
-        payload = {
-            "prompt": conversation_prompt,
-            "temperature": self.temperature,
-            "top_p": self.top_p,
-        }
-
-        async def for_stream():
-            async with self.session.stream(
-                "POST", self.chat_endpoint, json=payload, timeout=self.timeout
-            ) as response:
-                if not response.is_success:
-                    raise exceptions.FailedToGenerateResponseError(
-                        f"Failed to generate response - ({response.status_code}, {response.reason_phrase})"
-                    )
-
-                message_load = ""
-                async for value in response.aiter_lines():
-                    try:
-                        resp = sanitize_stream(value)
-                        message_load += await self.get_message(resp)
-                        resp["token"] = message_load
-                        self.last_response.update(resp)
-                        yield value if raw else resp
-                    except json.decoder.JSONDecodeError:
-                        pass
-
-            self.conversation.update_chat_history(
-                prompt, await self.get_message(self.last_response)
-            )
-
-        async def for_non_stream():
-            # let's make use of stream
-            async for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else await for_non_stream()
-
-    async def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str | AsyncGenerator:
-        """Generate response `str` asynchronously.
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-        async def for_stream():
-            async_ask = await self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            )
-            async for response in async_ask:
-                yield await self.get_message(response)
-
-        async def for_non_stream():
-            return await self.get_message(
-                await self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else await for_non_stream()
-
-    async def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        return response.get("token")

webscout/Provider/Leo.py

@@ -1,469 +0,0 @@
-import time
-import uuid
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support import expected_conditions as EC
-from selenium.webdriver.support.ui import WebDriverWait
-import click
-import requests
-from requests import get
-from uuid import uuid4
-from re import findall
-from requests.exceptions import RequestException
-from curl_cffi.requests import get, RequestsError
-import g4f
-from random import randint
-from PIL import Image
-import io
-import re
-import json
-import yaml
-from ..AIutel import Optimizers
-from ..AIutel import Conversation
-from ..AIutel import AwesomePrompts, sanitize_stream
-from ..AIbase import  Provider, AsyncProvider
-from Helpingai_T2 import Perplexity
-from webscout import exceptions
-from typing import Any, AsyncGenerator, Dict
-import logging
-import httpx
-#--------------------------------------LEO-----------------------------------------
-class LEO(Provider):
-    
-    def __init__(
-        self,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        temperature: float = 0.2,
-        top_k: int = -1,
-        top_p: float = 0.999,
-        model: str = "llama-2-13b-chat",
-        brave_key: str = "qztbjzBqJueQZLFkwTTJrieu8Vw3789u",
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-    ):
-        """Instantiate TGPT
-
-        Args:
-            is_conversation (str, optional): Flag for chatting conversationally. Defaults to True.
-            brave_key (str, optional): Brave API access key. Defaults to "qztbjzBqJueQZLFkwTTJrieu8Vw3789u".
-            model (str, optional): Text generation model name. Defaults to "llama-2-13b-chat".
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            temperature (float, optional): Charge of the generated text's randomness. Defaults to 0.2.
-            top_k (int, optional): Chance of topic being repeated. Defaults to -1.
-            top_p (float, optional): Sampling threshold during inference time. Defaults to 0.999.
-            timeput (int, optional): Http requesting timeout. Defaults to 30
-            intro (str, optional): Conversation introductory prompt. Defaults to `Conversation.intro`.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional) : Http reqiuest proxies (socks). Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-        """
-        self.session = requests.Session()
-        self.is_conversation = is_conversation
-        self.max_tokens_to_sample = max_tokens
-        self.model = model
-        self.stop_sequences = ["</response>", "</s>"]
-        self.temperature = temperature
-        self.top_k = top_k
-        self.top_p = top_p
-        self.chat_endpoint = "https://ai-chat.bsg.brave.com/v1/complete"
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.headers = {
-            "Content-Type": "application/json",
-            "accept": "text/event-stream",
-            "x-brave-key": brave_key,
-            "accept-language": "en-US,en;q=0.9",
-            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:99.0) Gecko/20100101 Firefox/110.0",
-        }
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        self.session.headers.update(self.headers)
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session.proxies = proxies
-        self.system_prompt = (
-            "\n\nYour name is Leo, a helpful"
-            "respectful and honest AI assistant created by the company Brave. You will be replying to a user of the Brave browser. "
-            "Always respond in a neutral tone. Be polite and courteous. Answer concisely in no more than 50-80 words."
-            "\n\nPlease ensure that your responses are socially unbiased and positive in nature."
-            "If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. "
-            "If you don't know the answer to a question, please don't share false information.\n"
-        )
-
-    def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict:
-        """Chat with AI
-
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            raw (bool, optional): Stream back raw response as received. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-           dict : {}
-        ```json
-        {
-            "completion": "\nNext: domestic cat breeds with short hair >>",
-            "stop_reason": null,
-            "truncated": false,
-            "stop": null,
-            "model": "llama-2-13b-chat",
-            "log_id": "cmpl-3kYiYxSNDvgMShSzFooz6t",
-            "exception": null
-        }
-        ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-
-        self.session.headers.update(self.headers)
-        payload = {
-            "max_tokens_to_sample": self.max_tokens_to_sample,
-            "model": self.model,
-            "prompt": f"<s>[INST] <<SYS>>{self.system_prompt}<</SYS>>{conversation_prompt} [/INST]",
-            "self.stop_sequence": self.stop_sequences,
-            "stream": stream,
-            "top_k": self.top_k,
-            "top_p": self.top_p,
-        }
-
-        def for_stream():
-            response = self.session.post(
-                self.chat_endpoint, json=payload, stream=True, timeout=self.timeout
-            )
-            if (
-                not response.ok
-                or not response.headers.get("Content-Type")
-                == "text/event-stream; charset=utf-8"
-            ):
-                raise Exception(
-                    f"Failed to generate response - ({response.status_code}, {response.reason}) - {response.text}"
-                )
-
-            for value in response.iter_lines(
-                decode_unicode=True,
-                delimiter="" if raw else "data:",
-                chunk_size=self.stream_chunk_size,
-            ):
-                try:
-                    resp = json.loads(value)
-                    self.last_response.update(resp)
-                    yield value if raw else resp
-                except json.decoder.JSONDecodeError:
-                    pass
-            self.conversation.update_chat_history(
-                prompt, self.get_message(self.last_response)
-            )
-
-        def for_non_stream():
-            response = self.session.post(
-                self.chat_endpoint, json=payload, stream=False, timeout=self.timeout
-            )
-            if (
-                not response.ok
-                or not response.headers.get("Content-Type", "") == "application/json"
-            ):
-                raise Exception(
-                    f"Failed to generate response - ({response.status_code}, {response.reason}) - {response.text}"
-                )
-            resp = response.json()
-            self.last_response.update(resp)
-            self.conversation.update_chat_history(
-                prompt, self.get_message(self.last_response)
-            )
-            return resp
-
-        return for_stream() if stream else for_non_stream()
-
-    def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str:
-        """Generate response `str`
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-        def for_stream():
-            for response in self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            ):
-                yield self.get_message(response)
-
-        def for_non_stream():
-            return self.get_message(
-                self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else for_non_stream()
-
-    def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        return response.get("completion")
-class AsyncLEO(AsyncProvider):
-    def __init__(
-        self,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        temperature: float = 0.2,
-        top_k: int = -1,
-        top_p: float = 0.999,
-        model: str = "llama-2-13b-chat",
-        brave_key: str = "qztbjzBqJueQZLFkwTTJrieu8Vw3789u",
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-    ):
-        """Instantiate TGPT
-
-        Args:
-            is_conversation (str, optional): Flag for chatting conversationally. Defaults to True.
-            brave_key (str, optional): Brave API access key. Defaults to "qztbjzBqJueQZLFkwTTJrieu8Vw3789u".
-            model (str, optional): Text generation model name. Defaults to "llama-2-13b-chat".
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            temperature (float, optional): Charge of the generated text's randomness. Defaults to 0.2.
-            top_k (int, optional): Chance of topic being repeated. Defaults to -1.
-            top_p (float, optional): Sampling threshold during inference time. Defaults to 0.999.
-            timeput (int, optional): Http requesting timeout. Defaults to 30
-            intro (str, optional): Conversation introductory prompt. Defaults to `Conversation.intro`.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional) : Http reqiuest proxies (socks). Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-        """
-        self.is_conversation = is_conversation
-        self.max_tokens_to_sample = max_tokens
-        self.model = model
-        self.stop_sequences = ["</response>", "</s>"]
-        self.temperature = temperature
-        self.top_k = top_k
-        self.top_p = top_p
-        self.chat_endpoint = "https://ai-chat.bsg.brave.com/v1/complete"
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.headers = {
-            "Content-Type": "application/json",
-            "accept": "text/event-stream",
-            "x-brave-key": brave_key,
-            "accept-language": "en-US,en;q=0.9",
-            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:99.0) Gecko/20100101 Firefox/110.0",
-        }
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.system_prompt = (
-            "\n\nYour name is Leo, a helpful"
-            "respectful and honest AI assistant created by the company Brave. You will be replying to a user of the Brave browser. "
-            "Always respond in a neutral tone. Be polite and courteous. Answer concisely in no more than 50-80 words."
-            "\n\nPlease ensure that your responses are socially unbiased and positive in nature."
-            "If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. "
-            "If you don't know the answer to a question, please don't share false information.\n"
-        )
-        self.session = httpx.AsyncClient(headers=self.headers, proxies=proxies)
-
-    async def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict | AsyncGenerator:
-        """Chat with AI asynchronously.
-
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            raw (bool, optional): Stream back raw response as received. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-           dict|AsyncGenerator : ai content
-        ```json
-        {
-            "completion": "\nNext: domestic cat breeds with short hair >>",
-            "stop_reason": null,
-            "truncated": false,
-            "stop": null,
-            "model": "llama-2-13b-chat",
-            "log_id": "cmpl-3kYiYxSNDvgMShSzFooz6t",
-            "exception": null
-        }
-        ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-
-        payload = {
-            "max_tokens_to_sample": self.max_tokens_to_sample,
-            "model": self.model,
-            "prompt": f"<s>[INST] <<SYS>>{self.system_prompt}<</SYS>>{conversation_prompt} [/INST]",
-            "self.stop_sequence": self.stop_sequences,
-            "stream": stream,
-            "top_k": self.top_k,
-            "top_p": self.top_p,
-        }
-
-        async def for_stream():
-            async with self.session.stream(
-                "POST", self.chat_endpoint, json=payload, timeout=self.timeout
-            ) as response:
-                if (
-                    not response.is_success
-                    or not response.headers.get("Content-Type")
-                    == "text/event-stream; charset=utf-8"
-                ):
-                    raise exceptions.FailedToGenerateResponseError(
-                        f"Failed to generate response - ({response.status_code}, {response.reason_phrase})"
-                    )
-                async for value in response.aiter_lines():
-                    try:
-                        resp = sanitize_stream(value)
-                        self.last_response.update(resp)
-                        yield value if raw else resp
-                    except json.decoder.JSONDecodeError:
-                        pass
-
-            self.conversation.update_chat_history(
-                prompt, await self.get_message(self.last_response)
-            )
-
-        async def for_non_stream():
-            async for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else await for_non_stream()
-
-    async def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str | AsyncGenerator:
-        """Generate response `str` asynchronously.
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str|AsyncGenerator: Response generated
-        """
-
-        async def for_stream():
-            async_ask = await self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            )
-            async for response in async_ask:
-                yield await self.get_message(response)
-
-        async def for_non_stream():
-            return await self.get_message(
-                await self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else await for_non_stream()
-
-    async def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        return response.get("completion")

webscout/Provider/Llama2.py

@@ -1,437 +0,0 @@
-import time
-import uuid
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support import expected_conditions as EC
-from selenium.webdriver.support.ui import WebDriverWait
-import click
-import requests
-from requests import get
-from uuid import uuid4
-from re import findall
-from requests.exceptions import RequestException
-from curl_cffi.requests import get, RequestsError
-import g4f
-from random import randint
-from PIL import Image
-import io
-import re
-import json
-import yaml
-from ..AIutel import Optimizers
-from ..AIutel import Conversation
-from ..AIutel import AwesomePrompts, sanitize_stream
-from ..AIbase import  Provider, AsyncProvider
-from Helpingai_T2 import Perplexity
-from webscout import exceptions
-from typing import Any, AsyncGenerator, Dict
-import logging
-import httpx
-
-class AsyncLLAMA2(AsyncProvider):
-    def __init__(
-        self,
-        is_conversation: bool = True,
-        max_tokens: int = 800,
-        temperature: float = 0.75,
-        presence_penalty: int = 0,
-        frequency_penalty: int = 0,
-        top_p: float = 0.9,
-        model: str = "meta/meta-llama-3-70b-instruct",
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-    ):
-        """Instantiates LLAMA2
-
-        Args:
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True.
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 800.
-            temperature (float, optional): Charge of the generated text's randomness. Defaults to 0.75.
-            presence_penalty (int, optional): Chances of topic being repeated. Defaults to 0.
-            frequency_penalty (int, optional): Chances of word being repeated. Defaults to 0.
-            top_p (float, optional): Sampling threshold during inference time. Defaults to 0.9.
-            model (str, optional): LLM model name. Defaults to "meta/llama-2-70b-chat".
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-        """
-        self.is_conversation = is_conversation
-        self.max_tokens_to_sample = max_tokens
-        self.model = model
-        self.temperature = temperature
-        self.presence_penalty = presence_penalty
-        self.frequency_penalty = frequency_penalty
-        self.top_p = top_p
-        self.chat_endpoint = "https://www.llama2.ai/api"
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.headers = {
-            "Content-Type": "application/json",
-            "Referer": "https://www.llama2.ai/",
-            "Content-Type": "text/plain;charset=UTF-8",
-            "Origin": "https://www.llama2.ai",
-        }
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session = httpx.AsyncClient(
-            headers=self.headers,
-            proxies=proxies,
-        )
-
-    async def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict | AsyncGenerator:
-        """Chat with AI asynchronously.
-
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            raw (bool, optional): Stream back raw response as received. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-           dict|AsyncGeneraror[dict] : ai content
-        ```json
-        {
-           "text" : "How may I help you today?"
-        }
-        ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-
-        payload = {
-            "prompt": f"{conversation_prompt}<s>[INST] {prompt} [/INST]",
-            "model": self.model,
-            "systemPrompt": "You are a helpful assistant.",
-            "temperature": self.temperature,
-            "topP": self.top_p,
-            "maxTokens": self.max_tokens_to_sample,
-            "image": None,
-            "audio": None,
-        }
-
-        async def for_stream():
-            async with self.session.stream(
-                "POST", self.chat_endpoint, json=payload, timeout=self.timeout
-            ) as response:
-                if (
-                    not response.is_success
-                    or not response.headers.get("Content-Type")
-                    == "text/plain; charset=utf-8"
-                ):
-                    raise exceptions.FailedToGenerateResponseError(
-                        f"Failed to generate response - ({response.status_code}, {response.reason_phrase})"
-                    )
-                message_load: str = ""
-                async for value in response.aiter_lines():
-                    try:
-                        if bool(value.strip()):
-                            message_load += value + "\n"
-                            resp: dict = dict(text=message_load)
-                            yield value if raw else resp
-                            self.last_response.update(resp)
-                    except json.decoder.JSONDecodeError:
-                        pass
-            self.conversation.update_chat_history(
-                prompt, await self.get_message(self.last_response)
-            )
-
-        async def for_non_stream():
-            async for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else await for_non_stream()
-
-    async def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str | AsyncGenerator:
-        """Generate response `str` asynchronously.
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str|AsyncGenerator: Response generated
-        """
-
-        async def for_stream():
-            async_ask = await self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            )
-            async for response in async_ask:
-                yield await self.get_message(response)
-
-        async def for_non_stream():
-            return await self.get_message(
-                await self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else await for_non_stream()
-
-    async def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (str): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        return response["text"]
-class LLAMA2(Provider):
-    def __init__(
-        self,
-        is_conversation: bool = True,
-        max_tokens: int = 800,
-        temperature: float = 0.75,
-        presence_penalty: int = 0,
-        frequency_penalty: int = 0,
-        top_p: float = 0.9,
-        model: str = "meta/meta-llama-3-70b-instruct",
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-    ):
-        """Instantiates LLAMA2
-
-        Args:
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True.
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 800.
-            temperature (float, optional): Charge of the generated text's randomness. Defaults to 0.75.
-            presence_penalty (int, optional): Chances of topic being repeated. Defaults to 0.
-            frequency_penalty (int, optional): Chances of word being repeated. Defaults to 0.
-            top_p (float, optional): Sampling threshold during inference time. Defaults to 0.9.
-            model (str, optional): LLM model name. Defaults to "meta/llama-2-70b-chat".
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-        """
-        self.session = requests.Session()
-        self.is_conversation = is_conversation
-        self.max_tokens_to_sample = max_tokens
-        self.model = model
-        self.temperature = temperature
-        self.presence_penalty = presence_penalty
-        self.frequency_penalty = frequency_penalty
-        self.top_p = top_p
-        self.chat_endpoint = "https://www.llama2.ai/api"
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.headers = {
-            "Content-Type": "application/json",
-            "Referer": "https://www.llama2.ai/",
-            "Content-Type": "text/plain;charset=UTF-8",
-            "Origin": "https://www.llama2.ai",
-        }
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        self.session.headers.update(self.headers)
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session.proxies = proxies
-
-    def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict:
-        """Chat with AI
-
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            raw (bool, optional): Stream back raw response as received. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-           dict : {}
-        ```json
-        {
-           "text" : "How may I help you today?"
-        }
-        ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-        self.session.headers.update(self.headers)
-
-        payload = {
-            "prompt": f"{conversation_prompt}<s>[INST] {prompt} [/INST]",
-            "model": self.model,
-            "systemPrompt": "You are a helpful assistant.",
-            "temperature": self.temperature,
-            "topP": self.top_p,
-            "maxTokens": self.max_tokens_to_sample,
-            "image": None,
-            "audio": None,
-        }
-
-        def for_stream():
-            response = self.session.post(
-                self.chat_endpoint, json=payload, stream=True, timeout=self.timeout
-            )
-            if (
-                not response.ok
-                or not response.headers.get("Content-Type")
-                == "text/plain; charset=utf-8"
-            ):
-                raise exceptions.FailedToGenerateResponseError(
-                    f"Failed to generate response - ({response.status_code}, {response.reason})"
-                )
-
-            message_load: str = ""
-            for value in response.iter_lines(
-                decode_unicode=True,
-                delimiter="\n",
-                chunk_size=self.stream_chunk_size,
-            ):
-                try:
-                    if bool(value.strip()):
-                        message_load += value + "\n"
-                        resp: dict = dict(text=message_load)
-                        yield value if raw else resp
-                        self.last_response.update(resp)
-                except json.decoder.JSONDecodeError:
-                    pass
-            self.conversation.update_chat_history(
-                prompt, self.get_message(self.last_response)
-            )
-
-        def for_non_stream():
-            for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else for_non_stream()
-
-    def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str:
-        """Generate response `str`
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-        def for_stream():
-            for response in self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            ):
-                yield self.get_message(response)
-
-        def for_non_stream():
-            return self.get_message(
-                self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else for_non_stream()
-
-    def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (str): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        return response["text"]

webscout/Provider/OpenGPT.py

@@ -1,487 +0,0 @@
-import time
-import uuid
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support import expected_conditions as EC
-from selenium.webdriver.support.ui import WebDriverWait
-import click
-import requests
-from requests import get
-from uuid import uuid4
-from re import findall
-from requests.exceptions import RequestException
-from curl_cffi.requests import get, RequestsError
-import g4f
-from random import randint
-from PIL import Image
-import io
-import re
-import json
-import yaml
-from ..AIutel import Optimizers
-from ..AIutel import Conversation
-from ..AIutel import AwesomePrompts, sanitize_stream
-from ..AIbase import  Provider, AsyncProvider
-from Helpingai_T2 import Perplexity
-from webscout import exceptions
-from typing import Any, AsyncGenerator, Dict
-import logging
-import httpx
-#------------------------------------------------------OpenGPT-----------------------------------------------------------
-class OPENGPT:
-    def __init__(
-        self,
-        assistant_id,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-    ):
-        """Instantiates OPENGPT
-
-        Args:
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-        """
-        self.session = requests.Session()
-        self.max_tokens_to_sample = max_tokens
-        self.is_conversation = is_conversation
-        self.chat_endpoint = (
-            "https://opengpts-example-vz4y4ooboq-uc.a.run.app/runs/stream"
-        )
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.assistant_id = assistant_id
-        self.authority = "opengpts-example-vz4y4ooboq-uc.a.run.app"
-
-        self.headers = {
-            "authority": self.authority,
-            "accept": "text/event-stream",
-            "accept-language": "en-US,en;q=0.7",
-            "cache-control": "no-cache",
-            "content-type": "application/json",
-            "origin": "https://opengpts-example-vz4y4ooboq-uc.a.run.app",
-            "pragma": "no-cache",
-            "referer": "https://opengpts-example-vz4y4ooboq-uc.a.run.app/",
-            "sec-fetch-site": "same-origin",
-            "sec-gpc": "1",
-            "user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
-        }
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        self.session.headers.update(self.headers)
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session.proxies = proxies
-
-    def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict:
-        """Chat with AI
-
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            raw (bool, optional): Stream back raw response as received. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-           dict : {}
-        ```json
-        {
-            "messages": [
-                {
-                    "content": "Hello there",
-                    "additional_kwargs": {},
-                    "type": "human",
-                    "example": false
-                },
-                {
-                    "content": "Hello! How can I assist you today?",
-                    "additional_kwargs": {
-                    "agent": {
-                        "return_values": {
-                            "output": "Hello! How can I assist you today?"
-                            },
-                        "log": "Hello! How can I assist you today?",
-                        "type": "AgentFinish"
-                    }
-                },
-                "type": "ai",
-                "example": false
-                }]
-        }
-        ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-
-        self.session.headers.update(self.headers)
-        self.session.headers.update(
-            dict(
-                cookie=f"opengpts_user_id={uuid4().__str__()}",
-            )
-        )
-        payload = {
-            "input": [
-                {
-                    "content": conversation_prompt,
-                    "additional_kwargs": {},
-                    "type": "human",
-                    "example": False,
-                },
-            ],
-            "assistant_id": self.assistant_id,
-            "thread_id": "",
-        }
-
-        def for_stream():
-            response = self.session.post(
-                self.chat_endpoint, json=payload, stream=True, timeout=self.timeout
-            )
-            if (
-                not response.ok
-                or not response.headers.get("Content-Type")
-                == "text/event-stream; charset=utf-8"
-            ):
-                raise Exception(
-                    f"Failed to generate response - ({response.status_code}, {response.reason}) - {response.text}"
-                )
-
-            for value in response.iter_lines(
-                decode_unicode=True,
-                chunk_size=self.stream_chunk_size,
-            ):
-                try:
-                    modified_value = re.sub("data:", "", value)
-                    resp = json.loads(modified_value)
-                    if len(resp) == 1:
-                        continue
-                    self.last_response.update(resp[1])
-                    yield value if raw else resp[1]
-                except json.decoder.JSONDecodeError:
-                    pass
-            self.conversation.update_chat_history(
-                prompt, self.get_message(self.last_response)
-            )
-
-        def for_non_stream():
-            for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else for_non_stream()
-
-    def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str:
-        """Generate response `str`
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-        def for_stream():
-            for response in self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            ):
-                yield self.get_message(response)
-
-        def for_non_stream():
-            return self.get_message(
-                self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else for_non_stream()
-
-    def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        return response["content"]
-class AsyncOPENGPT(AsyncProvider):
-    def __init__(
-        self,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-    ):
-        """Instantiates OPENGPT
-
-        Args:
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-        """
-        self.max_tokens_to_sample = max_tokens
-        self.is_conversation = is_conversation
-        self.chat_endpoint = (
-            "https://opengpts-example-vz4y4ooboq-uc.a.run.app/runs/stream"
-        )
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.assistant_id = "bca37014-6f97-4f2b-8928-81ea8d478d88"
-        self.authority = "opengpts-example-vz4y4ooboq-uc.a.run.app"
-
-        self.headers = {
-            "authority": self.authority,
-            "accept": "text/event-stream",
-            "accept-language": "en-US,en;q=0.7",
-            "cache-control": "no-cache",
-            "content-type": "application/json",
-            "origin": "https://opengpts-example-vz4y4ooboq-uc.a.run.app",
-            "pragma": "no-cache",
-            "referer": "https://opengpts-example-vz4y4ooboq-uc.a.run.app/",
-            "sec-fetch-site": "same-origin",
-            "sec-gpc": "1",
-            "user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
-        }
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session = httpx.AsyncClient(headers=self.headers, proxies=proxies)
-
-    async def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict | AsyncGenerator:
-        """Chat with AI asynchronously
-
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            raw (bool, optional): Stream back raw response as received. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-           dict|AsyncGenerator : ai content.
-        ```json
-        {
-            "messages": [
-                {
-                    "content": "Hello there",
-                    "additional_kwargs": {},
-                    "type": "human",
-                    "example": false
-                },
-                {
-                    "content": "Hello! How can I assist you today?",
-                    "additional_kwargs": {
-                    "agent": {
-                        "return_values": {
-                            "output": "Hello! How can I assist you today?"
-                            },
-                        "log": "Hello! How can I assist you today?",
-                        "type": "AgentFinish"
-                    }
-                },
-                "type": "ai",
-                "example": false
-                }]
-        }
-        ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-        self.headers.update(
-            dict(
-                cookie=f"opengpts_user_id={uuid4().__str__()}",
-            )
-        )
-        payload = {
-            "input": [
-                {
-                    "content": conversation_prompt,
-                    "additional_kwargs": {},
-                    "type": "human",
-                    "example": False,
-                },
-            ],
-            "assistant_id": self.assistant_id,
-            "thread_id": "",
-        }
-
-        async def for_stream():
-            async with self.session.stream(
-                "POST",
-                self.chat_endpoint,
-                json=payload,
-                timeout=self.timeout,
-                headers=self.headers,
-            ) as response:
-                if (
-                    not response.is_success
-                    or not response.headers.get("Content-Type")
-                    == "text/event-stream; charset=utf-8"
-                ):
-                    raise exceptions.FailedToGenerateResponseError(
-                        f"Failed to generate response - ({response.status_code}, {response.reason_phrase}) - {response.text}"
-                    )
-
-                async for value in response.aiter_lines():
-                    try:
-                        modified_value = re.sub("data:", "", value)
-                        resp = json.loads(modified_value)
-                        if len(resp) == 1:
-                            continue
-                        self.last_response.update(resp[1])
-                        yield value if raw else resp[1]
-                    except json.decoder.JSONDecodeError:
-                        pass
-
-            self.conversation.update_chat_history(
-                prompt, await self.get_message(self.last_response)
-            )
-
-        async def for_non_stream():
-            async for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else await for_non_stream()
-
-    async def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str | AsyncGenerator:
-        """Generate response `str` asynchronously.
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str|AsyncGenerator: Response generated
-        """
-
-        async def for_stream():
-            async_ask = await self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            )
-            async for response in async_ask:
-                yield await self.get_message(response)
-
-        async def for_non_stream():
-            return await self.get_message(
-                await self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else await for_non_stream()
-
-    async def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        return response["content"]

webscout/Provider/Openai.py

@@ -1,511 +0,0 @@
-import time
-import uuid
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support import expected_conditions as EC
-from selenium.webdriver.support.ui import WebDriverWait
-import click
-import requests
-from requests import get
-from uuid import uuid4
-from re import findall
-from requests.exceptions import RequestException
-from curl_cffi.requests import get, RequestsError
-import g4f
-from random import randint
-from PIL import Image
-import io
-import re
-import json
-import yaml
-from ..AIutel import Optimizers
-from ..AIutel import Conversation
-from ..AIutel import AwesomePrompts, sanitize_stream
-from ..AIbase import  Provider, AsyncProvider
-from Helpingai_T2 import Perplexity
-from webscout import exceptions
-from typing import Any, AsyncGenerator, Dict
-import logging
-import httpx
-#----------------------------------------------------------OpenAI-----------------------------------
-class OPENAI(Provider):
-    def __init__(
-        self,
-        api_key: str,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        temperature: float = 1,
-        presence_penalty: int = 0,
-        frequency_penalty: int = 0,
-        top_p: float = 1,
-        model: str = "gpt-3.5-turbo",
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-    ):
-        """Instantiates OPENAI
-
-        Args:
-            api_key (key): OpenAI's API key.
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True.
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            temperature (float, optional): Charge of the generated text's randomness. Defaults to 1.
-            presence_penalty (int, optional): Chances of topic being repeated. Defaults to 0.
-            frequency_penalty (int, optional): Chances of word being repeated. Defaults to 0.
-            top_p (float, optional): Sampling threshold during inference time. Defaults to 0.999.
-            model (str, optional): LLM model name. Defaults to "gpt-3.5-turbo".
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-        """
-        self.is_conversation = is_conversation
-        self.max_tokens_to_sample = max_tokens
-        self.api_key = api_key
-        self.model = model
-        self.temperature = temperature
-        self.presence_penalty = presence_penalty
-        self.frequency_penalty = frequency_penalty
-        self.top_p = top_p
-        self.chat_endpoint = "https://api.openai.com/v1/chat/completions"
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.api_key}",
-        }
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        self.session.headers.update(self.headers)
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session.proxies = proxies
-
-    def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict:
-        """Chat with AI
-
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            raw (bool, optional): Stream back raw response as received. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-           dict : {}
-        ```json
-        {
-            "id": "chatcmpl-TaREJpBZsRVQFRFic1wIA7Q7XfnaD",
-            "object": "chat.completion",
-            "created": 1704623244,
-            "model": "gpt-3.5-turbo",
-            "usage": {
-                "prompt_tokens": 0,
-                "completion_tokens": 0,
-                "total_tokens": 0
-                },
-            "choices": [
-                {
-                    "message": {
-                        "role": "assistant",
-                        "content": "Hello! How can I assist you today?"
-                },
-                "finish_reason": "stop",
-                "index": 0
-                }
-            ]
-        }
-        ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise exceptions.FailedToGenerateResponseError(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-        self.session.headers.update(self.headers)
-        payload = {
-            "frequency_penalty": self.frequency_penalty,
-            "messages": [{"content": conversation_prompt, "role": "user"}],
-            "model": self.model,
-            "presence_penalty": self.presence_penalty,
-            "stream": stream,
-            "temperature": self.temperature,
-            "top_p": self.top_p,
-        }
-
-        def for_stream():
-            response = self.session.post(
-                self.chat_endpoint, json=payload, stream=True, timeout=self.timeout
-            )
-            if not response.ok:
-                raise exceptions.FailedToGenerateResponseError(
-                    f"Failed to generate response - ({response.status_code}, {response.reason}) - {response.text}"
-                )
-
-            message_load = ""
-            for value in response.iter_lines(
-                decode_unicode=True,
-                delimiter="" if raw else "data:",
-                chunk_size=self.stream_chunk_size,
-            ):
-                try:
-                    resp = json.loads(value)
-                    incomplete_message = self.get_message(resp)
-                    if incomplete_message:
-                        message_load += incomplete_message
-                        resp["choices"][0]["delta"]["content"] = message_load
-                        self.last_response.update(resp)
-                        yield value if raw else resp
-                    elif raw:
-                        yield value
-                except json.decoder.JSONDecodeError:
-                    pass
-            self.conversation.update_chat_history(
-                prompt, self.get_message(self.last_response)
-            )
-
-        def for_non_stream():
-            response = self.session.post(
-                self.chat_endpoint, json=payload, stream=False, timeout=self.timeout
-            )
-            if (
-                not response.ok
-                or not response.headers.get("Content-Type", "") == "application/json"
-            ):
-                raise exceptions.FailedToGenerateResponseError(
-                    f"Failed to generate response - ({response.status_code}, {response.reason}) - {response.text}"
-                )
-            resp = response.json()
-            self.last_response.update(resp)
-            self.conversation.update_chat_history(
-                prompt, self.get_message(self.last_response)
-            )
-            return resp
-
-        return for_stream() if stream else for_non_stream()
-
-    def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str:
-        """Generate response `str`
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-        def for_stream():
-            for response in self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            ):
-                yield self.get_message(response)
-
-        def for_non_stream():
-            return self.get_message(
-                self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else for_non_stream()
-
-    def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        try:
-            if response["choices"][0].get("delta"):
-                return response["choices"][0]["delta"]["content"]
-            return response["choices"][0]["message"]["content"]
-        except KeyError:
-            return ""
-class AsyncOPENAI(AsyncProvider):
-    def __init__(
-        self,
-        api_key: str,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        temperature: float = 1,
-        presence_penalty: int = 0,
-        frequency_penalty: int = 0,
-        top_p: float = 1,
-        model: str = "gpt-3.5-turbo",
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-    ):
-        """Instantiates OPENAI
-
-        Args:
-            api_key (key): OpenAI's API key.
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True.
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            temperature (float, optional): Charge of the generated text's randomness. Defaults to 1.
-            presence_penalty (int, optional): Chances of topic being repeated. Defaults to 0.
-            frequency_penalty (int, optional): Chances of word being repeated. Defaults to 0.
-            top_p (float, optional): Sampling threshold during inference time. Defaults to 0.999.
-            model (str, optional): LLM model name. Defaults to "gpt-3.5-turbo".
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-        """
-        self.is_conversation = is_conversation
-        self.max_tokens_to_sample = max_tokens
-        self.api_key = api_key
-        self.model = model
-        self.temperature = temperature
-        self.presence_penalty = presence_penalty
-        self.frequency_penalty = frequency_penalty
-        self.top_p = top_p
-        self.chat_endpoint = "https://api.openai.com/v1/chat/completions"
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.api_key}",
-        }
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session = httpx.AsyncClient(
-            headers=self.headers,
-            proxies=proxies,
-        )
-
-    async def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict | AsyncGenerator:
-        """Chat with AI asynchronously.
-
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            raw (bool, optional): Stream back raw response as received. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-           dict|AsyncGenerator : ai content.
-        ```json
-        {
-            "id": "chatcmpl-TaREJpBZsRVQFRFic1wIA7Q7XfnaD",
-            "object": "chat.completion",
-            "created": 1704623244,
-            "model": "gpt-3.5-turbo",
-            "usage": {
-                "prompt_tokens": 0,
-                "completion_tokens": 0,
-                "total_tokens": 0
-                },
-            "choices": [
-                {
-                    "message": {
-                        "role": "assistant",
-                        "content": "Hello! How can I assist you today?"
-                },
-                "finish_reason": "stop",
-                "index": 0
-                }
-            ]
-        }
-        ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-        payload = {
-            "frequency_penalty": self.frequency_penalty,
-            "messages": [{"content": conversation_prompt, "role": "user"}],
-            "model": self.model,
-            "presence_penalty": self.presence_penalty,
-            "stream": stream,
-            "temperature": self.temperature,
-            "top_p": self.top_p,
-        }
-
-        async def for_stream():
-            async with self.session.stream(
-                "POST", self.chat_endpoint, json=payload, timeout=self.timeout
-            ) as response:
-                if not response.is_success:
-                    raise Exception(
-                        f"Failed to generate response - ({response.status_code}, {response.reason_phrase})"
-                    )
-
-                message_load = ""
-                async for value in response.aiter_lines():
-                    try:
-
-                        resp = sanitize_stream(value)
-                        incomplete_message = await self.get_message(resp)
-                        if incomplete_message:
-                            message_load += incomplete_message
-                            resp["choices"][0]["delta"]["content"] = message_load
-                            self.last_response.update(resp)
-                            yield value if raw else resp
-                        elif raw:
-                            yield value
-                    except json.decoder.JSONDecodeError:
-                        pass
-            self.conversation.update_chat_history(
-                prompt, await self.get_message(self.last_response)
-            )
-
-        async def for_non_stream():
-            response = httpx.post(
-                self.chat_endpoint,
-                json=payload,
-                timeout=self.timeout,
-                headers=self.headers,
-            )
-            if (
-                not response.is_success
-                or not response.headers.get("Content-Type", "") == "application/json"
-            ):
-                raise Exception(
-                    f"Failed to generate response - ({response.status_code}, {response.reason_phrase})"
-                )
-            resp = response.json()
-            self.last_response.update(resp)
-            self.conversation.update_chat_history(
-                prompt, await self.get_message(self.last_response)
-            )
-            return resp
-
-        return for_stream() if stream else await for_non_stream()
-
-    async def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str | AsyncGenerator:
-        """Generate response `str` asynchronously.
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str|AsyncGenerator: Response generated
-        """
-
-        async def for_stream():
-            async_ask = await self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            )
-            async for response in async_ask:
-                yield await self.get_message(response)
-
-        async def for_non_stream():
-            return await self.get_message(
-                await self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else await for_non_stream()
-
-    async def get_message(self, response: dict) -> str:
-        """Retrieves message only from response asynchronously.
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        try:
-            if response["choices"][0].get("delta"):
-                return response["choices"][0]["delta"]["content"]
-            return response["choices"][0]["message"]["content"]
-        except KeyError:
-            return ""

webscout/Provider/Perplexity.py

@@ -1,230 +0,0 @@
-import time
-import uuid
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support import expected_conditions as EC
-from selenium.webdriver.support.ui import WebDriverWait
-import click
-import requests
-from requests import get
-from uuid import uuid4
-from re import findall
-from requests.exceptions import RequestException
-from curl_cffi.requests import get, RequestsError
-import g4f
-from random import randint
-from PIL import Image
-import io
-import re
-import json
-import yaml
-from ..AIutel import Optimizers
-from ..AIutel import Conversation
-from ..AIutel import AwesomePrompts, sanitize_stream
-from ..AIbase import  Provider, AsyncProvider
-from Helpingai_T2 import Perplexity
-from webscout import exceptions
-from typing import Any, AsyncGenerator, Dict
-import logging
-import httpx
-#------------------------------------------------------PERPLEXITY--------------------------------------------------------  
-class PERPLEXITY(Provider):
-    def __init__(
-        self,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-        quiet: bool = False,
-    ):
-        """Instantiates PERPLEXITY
-
-        Args:
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-            quiet (bool, optional): Ignore web search-results and yield final response only. Defaults to False.
-        """
-        self.max_tokens_to_sample = max_tokens
-        self.is_conversation = is_conversation
-        self.last_response = {}
-        self.web_results: dict = {}
-        self.quiet = quiet
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-
-    def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict:
-        """Chat with AI
-
-                Args:
-                    prompt (str): Prompt to be send.
-                    stream (bool, optional): Flag for streaming response. Defaults to False.
-                    raw (bool, optional): Stream back raw response as received. Defaults to False.
-                    optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-                    conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-                Returns:
-                   dict : {}
-                ```json
-        {
-            "status": "pending",
-            "uuid": "3604dfcc-611f-4b7d-989d-edca2a7233c7",
-            "read_write_token": null,
-            "frontend_context_uuid": "f6d43119-5231-481d-b692-f52e1f52d2c6",
-            "final": false,
-            "backend_uuid": "a6d6ec9e-da69-4841-af74-0de0409267a8",
-            "media_items": [],
-            "widget_data": [],
-            "knowledge_cards": [],
-            "expect_search_results": "false",
-            "mode": "concise",
-            "search_focus": "internet",
-            "gpt4": false,
-            "display_model": "turbo",
-            "attachments": null,
-            "answer": "",
-            "web_results": [],
-            "chunks": [],
-            "extra_web_results": []
-        }
-                ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-
-        def for_stream():
-            for response in Perplexity().generate_answer(conversation_prompt):
-                yield json.dumps(response) if raw else response
-                self.last_response.update(response)
-
-            self.conversation.update_chat_history(
-                prompt,
-                self.get_message(self.last_response),
-            )
-
-        def for_non_stream():
-            for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else for_non_stream()
-
-    def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str:
-        """Generate response `str`
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-        def for_stream():
-            for response in self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            ):
-                yield self.get_message(response)
-
-        def for_non_stream():
-            return self.get_message(
-                self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else for_non_stream()
-
-    def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        text_str: str = response.get("answer", "")
-
-        def update_web_results(web_results: list) -> None:
-            for index, results in enumerate(web_results, start=1):
-                self.web_results[str(index) + ". " + results["name"]] = dict(
-                    url=results.get("url"), snippet=results.get("snippet")
-                )
-
-        if response.get("text"):
-            # last chunk
-            target: dict[str, Any] = json.loads(response.get("text"))
-            text_str = target.get("answer")
-            web_results: list[dict] = target.get("web_results")
-            self.web_results.clear()
-            update_web_results(web_results)
-
-            return (
-                text_str
-                if self.quiet or not self.web_results
-                else text_str + "\n\n# WEB-RESULTS\n\n" + yaml.dump(self.web_results)
-            )
-
-        else:
-            if str(response.get("expect_search_results")).lower() == "true":
-                return (
-                    text_str
-                    if self.quiet
-                    else text_str
-                    + "\n\n# WEB-RESULTS\n\n"
-                    + yaml.dump(response.get("web_results"))
-                )
-            else:
-                return text_str

webscout/Provider/Phind.py

@@ -1,518 +0,0 @@
-import time
-import uuid
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support import expected_conditions as EC
-from selenium.webdriver.support.ui import WebDriverWait
-import click
-import requests
-from requests import get
-from uuid import uuid4
-from re import findall
-from requests.exceptions import RequestException
-from curl_cffi.requests import get, RequestsError
-import g4f
-from random import randint
-from PIL import Image
-import io
-import re
-import json
-import yaml
-from ..AIutel import Optimizers
-from ..AIutel import Conversation
-from ..AIutel import AwesomePrompts, sanitize_stream
-from ..AIbase import  Provider, AsyncProvider
-from Helpingai_T2 import Perplexity
-from webscout import exceptions
-from typing import Any, AsyncGenerator, Dict
-import logging
-import httpx
-
-#------------------------------------------------------phind-------------------------------------------------------------
-class PhindSearch:
-    # default_model = "Phind Model"
-    def __init__(
-        self,
-        is_conversation: bool = True,
-        max_tokens: int = 8000,
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-        model: str = "Phind Model",
-        quiet: bool = False,
-    ):
-        """Instantiates PHIND
-
-        Args:
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-            model (str, optional): Model name. Defaults to "Phind Model".
-            quiet (bool, optional): Ignore web search-results and yield final response only. Defaults to False.
-        """
-        self.session = requests.Session()
-        self.max_tokens_to_sample = max_tokens
-        self.is_conversation = is_conversation
-        self.chat_endpoint = "https://https.extension.phind.com/agent/"
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.model = model
-        self.quiet = quiet
-
-        self.headers = {
-            "Content-Type": "application/json",
-            "User-Agent": "",
-            "Accept": "*/*",
-            "Accept-Encoding": "Identity",
-        }
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        self.session.headers.update(self.headers)
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session.proxies = proxies
-
-    def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict:
-        """Chat with AI
-
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            raw (bool, optional): Stream back raw response as received. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-           dict : {}
-        ```json
-        {
-            "id": "chatcmpl-r0wujizf2i2xb60mjiwt",
-            "object": "chat.completion.chunk",
-            "created": 1706775384,
-            "model": "trt-llm-phind-model-serving",
-            "choices": [
-                {
-                    "index": 0,
-                    "delta": {
-                        "content": "Hello! How can I assist you with your programming today?"
-                        },
-                    "finish_reason": null
-                }
-            ]
-        }
-        ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-
-        self.session.headers.update(self.headers)
-        payload = {
-            "additional_extension_context": "",
-            "allow_magic_buttons": True,
-            "is_vscode_extension": True,
-            "message_history": [
-                {"content": conversation_prompt, "metadata": {}, "role": "user"}
-            ],
-            "requested_model": self.model,
-            "user_input": prompt,
-        }
-
-        def for_stream():
-            response = self.session.post(
-                self.chat_endpoint, json=payload, stream=True, timeout=self.timeout
-            )
-            if (
-                not response.ok
-                or not response.headers.get("Content-Type")
-                == "text/event-stream; charset=utf-8"
-            ):
-                raise exceptions.FailedToGenerateResponseError(
-                    f"Failed to generate response - ({response.status_code}, {response.reason}) - {response.text}"
-                )
-            streaming_text = ""
-            for value in response.iter_lines(
-                decode_unicode=True,
-                chunk_size=self.stream_chunk_size,
-            ):
-                try:
-                    modified_value = re.sub("data:", "", value)
-                    json_modified_value = json.loads(modified_value)
-                    retrieved_text = self.get_message(json_modified_value)
-                    if not retrieved_text:
-                        continue
-                    streaming_text += retrieved_text
-                    json_modified_value["choices"][0]["delta"][
-                        "content"
-                    ] = streaming_text
-                    self.last_response.update(json_modified_value)
-                    yield value if raw else json_modified_value
-                except json.decoder.JSONDecodeError:
-                    pass
-            self.conversation.update_chat_history(
-                prompt, self.get_message(self.last_response)
-            )
-
-        def for_non_stream():
-            for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else for_non_stream()
-
-    def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str:
-        """Generate response `str`
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-        def for_stream():
-            for response in self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            ):
-                yield self.get_message(response)
-
-        def for_non_stream():
-            return self.get_message(
-                self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else for_non_stream()
-
-    def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        if response.get("type", "") == "metadata":
-            return
-
-        delta: dict = response["choices"][0]["delta"]
-
-        if not delta:
-            return ""
-
-        elif delta.get("function_call"):
-            if self.quiet:
-                return ""
-
-            function_call: dict = delta["function_call"]
-            if function_call.get("name"):
-                return function_call["name"]
-            elif function_call.get("arguments"):
-                return function_call.get("arguments")
-
-        elif delta.get("metadata"):
-            if self.quiet:
-                return ""
-            return yaml.dump(delta["metadata"])
-
-        else:
-            return (
-                response["choices"][0]["delta"].get("content")
-                if response["choices"][0].get("finish_reason") is None
-                else ""
-            )
-class AsyncPhindSearch(AsyncProvider):
-    def __init__(
-        self,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-        model: str = "Phind Model",
-        quiet: bool = False,
-    ):
-        """Instantiates PHIND
-
-        Args:
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-            model (str, optional): Model name. Defaults to "Phind Model".
-            quiet (bool, optional): Ignore web search-results and yield final response only. Defaults to False.
-        """
-        self.max_tokens_to_sample = max_tokens
-        self.is_conversation = is_conversation
-        self.chat_endpoint = "https://https.extension.phind.com/agent/"
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.model = model
-        self.quiet = quiet
-
-        self.headers = {
-            "Content-Type": "application/json",
-            "User-Agent": "",
-            "Accept": "*/*",
-            "Accept-Encoding": "Identity",
-        }
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session = httpx.AsyncClient(headers=self.headers, proxies=proxies)
-
-    async def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-        synchronous_generator=False,
-    ) -> dict | AsyncGenerator:
-        """Asynchronously Chat with AI
-
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            raw (bool, optional): Stream back raw response as received. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-           dict|AsyncGenerator : ai content.
-        ```json
-        {
-            "id": "chatcmpl-r0wujizf2i2xb60mjiwt",
-            "object": "chat.completion.chunk",
-            "created": 1706775384,
-            "model": "trt-llm-phind-model-serving",
-            "choices": [
-                {
-                    "index": 0,
-                    "delta": {
-                        "content": "Hello! How can I assist you with your programming today?"
-                        },
-                    "finish_reason": null
-                }
-            ]
-        }
-        ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-
-        payload = {
-            "additional_extension_context": "",
-            "allow_magic_buttons": True,
-            "is_vscode_extension": True,
-            "message_history": [
-                {"content": conversation_prompt, "metadata": {}, "role": "user"}
-            ],
-            "requested_model": self.model,
-            "user_input": prompt,
-        }
-
-        async def for_stream():
-            async with self.session.stream(
-                "POST",
-                self.chat_endpoint,
-                json=payload,
-                timeout=self.timeout,
-            ) as response:
-                if (
-                    not response.is_success
-                    or not response.headers.get("Content-Type")
-                    == "text/event-stream; charset=utf-8"
-                ):
-                    raise exceptions.FailedToGenerateResponseError(
-                        f"Failed to generate response - ({response.status_code}, {response.reason_phrase})"
-                    )
-                streaming_text = ""
-                async for value in response.aiter_lines():
-                    try:
-                        modified_value = re.sub("data:", "", value)
-                        json_modified_value = json.loads(modified_value)
-                        retrieved_text = await self.get_message(json_modified_value)
-                        if not retrieved_text:
-                            continue
-                        streaming_text += retrieved_text
-                        json_modified_value["choices"][0]["delta"][
-                            "content"
-                        ] = streaming_text
-                        self.last_response.update(json_modified_value)
-                        yield value if raw else json_modified_value
-                    except json.decoder.JSONDecodeError:
-                        pass
-                self.conversation.update_chat_history(
-                    prompt, await self.get_message(self.last_response)
-                )
-
-        async def for_non_stream():
-            async for _ in for_stream():
-                pass
-            return self.last_response
-
-        return (
-            for_stream()
-            if stream and not synchronous_generator
-            else await for_non_stream()
-        )
-
-    async def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str | AsyncGenerator:
-        """Generate response `str`
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str|AsyncGenerator: Response generated
-        """
-
-        async def for_stream():
-            ask_resp = await self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            )
-            async for response in ask_resp:
-                yield await self.get_message(response)
-
-        async def for_non_stream():
-            return await self.get_message(
-                await self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else await for_non_stream()
-
-    async def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        if response.get("type", "") == "metadata":
-            return
-
-        delta: dict = response["choices"][0]["delta"]
-
-        if not delta:
-            return ""
-
-        elif delta.get("function_call"):
-            if self.quiet:
-                return ""
-
-            function_call: dict = delta["function_call"]
-            if function_call.get("name"):
-                return function_call["name"]
-            elif function_call.get("arguments"):
-                return function_call.get("arguments")
-
-        elif delta.get("metadata"):
-            if self.quiet:
-                return ""
-            return yaml.dump(delta["metadata"])
-
-        else:
-            return (
-                response["choices"][0]["delta"].get("content")
-                if response["choices"][0].get("finish_reason") is None
-                else ""
-            )

webscout/Provider/Poe.py

@@ -1,208 +0,0 @@
-from poe_api_wrapper import PoeApi
-from poe_api_wrapper.api import BOTS_LIST
-from ..AIbase import Provider
-from ..AIutel import Conversation
-from ..AIutel  import Optimizers
-from ..AIutel  import AwesomePrompts
-from pathlib import Path
-from json import loads
-from json import dumps
-from loguru import logger
-import logging
-
-logger.remove()
-
-
-class POE(Provider):
-    def __init__(
-        self,
-        cookie: str,
-        model: str = "Assistant",
-        proxy: bool = False,
-        timeout: int = 30,
-        filepath: str = None,
-        update_file: str = True,
-        intro: str = None,
-        act: str = None,
-        init: bool = True,
-    ):
-        """Initializes POE
-
-        Args:
-            cookie (str): Path to `poe.com.cookies.json` file or 'p-b' cookie-value.
-            model (str, optional): Model name. Default to Assistant.
-            proxy (bool, optional): Flag for Httpx request proxy. Defaults to False.
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            filepath (str, optional): Path to save the chat history. Defaults to None.
-            update_file (str, optional): Flag for controlling chat history updates. Defaults to True.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-            init (bool, optional): Resend the intro prompt. Defaults to True.
-        """
-        assert isinstance(
-            cookie, str
-        ), f"Cookie must be of {str} datatype only not {type(cookie)}"
-        assert (
-            model in BOTS_LIST.keys()
-        ), f"model name '{model}' is not one of {', '.join(list(BOTS_LIST.keys()))}"
-        cookie_path = Path(cookie)
-
-        if cookie_path.exists() or any(["/" in cookie, ".json" in cookie]):
-            cookie = None
-            all_cookies = loads(cookie_path.read_text())
-            for entry in all_cookies:
-                if entry["name"] == "p-b":
-                    cookie = entry["value"]
-            assert (
-                cookie
-            ), f'Required cookie value cannot be retrieved from the path  "{cookie_path.as_posix()}"'
-
-        if proxy:
-            import poe_api_wrapper.proxies as proxies
-
-            proxies.PROXY = True
-
-        self.bot = BOTS_LIST[model]
-        self.session = PoeApi(cookie)
-        self.last_response = {}
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            status=False, filepath=filepath, update_file=update_file
-        )
-        if init:
-            self.ask(self.conversation.intro)  # Init
-
-    def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict:
-        """Chat with AI
-
-            Args:
-                prompt (str): Prompt to be send.
-                stream (bool, optional): Flag for streaming response. Defaults to False.
-                raw (bool, optional): Stream back raw response as received. Defaults to False.
-                optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defeaults to None
-                conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-            Returns:
-               dict : {}
-        ```json
-        {
-            "id": "TWVzc2FnZToxMTU0MzgyNDQ1ODU=",
-            "messageId": 115438244585,
-            "creationTime": 1707777376544407,
-            "clientNonce": null,
-            "state": "complete",
-            "text": "Hello! How can I assist you today?",
-            "author": "capybara",
-            "contentType": "text_markdown",
-            "sourceType": "chat_input",
-            "attachmentTruncationState": "not_truncated",
-            "attachments": [],
-            "vote": null,
-            "suggestedReplies": [],
-            "hasCitations": false,
-            "__isNode": "Message",
-            "textLengthOnCancellation": null,
-            "chatCode": "21a2jn0yrq9phxiy478",
-            "chatId": 328236777,
-            "title": null,
-            "response": ""
-        }
-        ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-
-        def for_stream():
-            for response in self.session.send_message(self.bot, conversation_prompt):
-                if raw:
-                    yield dumps(response)
-                else:
-                    yield response
-
-                self.last_response.update(response)
-
-            self.conversation.update_chat_history(
-                prompt,
-                self.get_message(self.last_response),
-                force=True,
-            )
-
-        def for_non_stream():
-            # let's make use of stream
-            for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else for_non_stream()
-
-    def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str:
-        """Generate response `str`
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-        def for_stream():
-            for response in self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            ):
-                yield self.get_message(response)
-
-        def for_non_stream():
-            return self.get_message(
-                self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else for_non_stream()
-
-    def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        return response["text"]

webscout/Provider/Reka.py

@@ -1,226 +0,0 @@
-import time
-import uuid
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support import expected_conditions as EC
-from selenium.webdriver.support.ui import WebDriverWait
-import click
-import requests
-from requests import get
-from uuid import uuid4
-from re import findall
-from requests.exceptions import RequestException
-from curl_cffi.requests import get, RequestsError
-import g4f
-from random import randint
-from PIL import Image
-import io
-import re
-import json
-import yaml
-from ..AIutel import Optimizers
-from ..AIutel import Conversation
-from ..AIutel import AwesomePrompts, sanitize_stream
-from ..AIbase import  Provider, AsyncProvider
-from Helpingai_T2 import Perplexity
-from webscout import exceptions
-from typing import Any, AsyncGenerator, Dict
-import logging
-import httpx
-#-----------------------------------------------REKA-----------------------------------------------
-class REKA(Provider):
-    def __init__(
-        self,
-        api_key: str,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-        model: str = "reka-core",
-        system_prompt: str = "Be Helpful and Friendly. Keep your response straightforward, short and concise",
-        use_search_engine: bool = False,
-        use_code_interpreter: bool = False,
-    ):
-        """Instantiates REKA
-
-        Args:
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-            model (str, optional): REKA model name. Defaults to "reka-core".
-            system_prompt (str, optional): System prompt for REKA. Defaults to "Be Helpful and Friendly. Keep your response straightforward, short and concise".
-            use_search_engine (bool, optional): Whether to use the search engine. Defaults to False.
-            use_code_interpreter (bool, optional): Whether to use the code interpreter. Defaults to False.
-        """
-        self.session = requests.Session()
-        self.is_conversation = is_conversation
-        self.max_tokens_to_sample = max_tokens
-        self.api_endpoint = "https://chat.reka.ai/api/chat"
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.model = model
-        self.system_prompt = system_prompt
-        self.use_search_engine = use_search_engine
-        self.use_code_interpreter = use_code_interpreter
-        self.access_token = api_key
-        self.headers = {
-            "Authorization": f"Bearer {self.access_token}",
-        }
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        self.session.headers.update(self.headers)
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session.proxies = proxies
-
-    def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict:
-        """Chat with AI
-
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            raw (bool, optional): Stream back raw response as received. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-           dict : {}
-        ```json
-        {
-           "text" : "How may I assist you today?"
-        }
-        ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-
-        self.session.headers.update(self.headers)
-        payload = {
-
-            "conversation_history": [
-                {"type": "human", "text": f"## SYSTEM PROMPT: {self.system_prompt}\n\n## QUERY: {conversation_prompt}"},
-            ],
-
-            "stream": stream,
-            "use_search_engine": self.use_search_engine,
-            "use_code_interpreter": self.use_code_interpreter,
-            "model_name": self.model,
-            # "model_name": "reka-flash",
-            # "model_name": "reka-edge",
-        }
-
-        def for_stream():
-            response = self.session.post(self.api_endpoint, json=payload, stream=True, timeout=self.timeout)
-            if not response.ok:
-                raise Exception(
-                    f"Failed to generate response - ({response.status_code}, {response.reason}) - {response.text}"
-                )
-
-            for value in response.iter_lines(
-                decode_unicode=True,
-                chunk_size=self.stream_chunk_size,
-            ):
-                try:
-                    resp = json.loads(value)
-                    self.last_response.update(resp)
-                    yield value if raw else resp
-                except json.decoder.JSONDecodeError:
-                    pass
-            self.conversation.update_chat_history(
-                prompt, self.get_message(self.last_response)
-            )
-
-        def for_non_stream():
-            # let's make use of stream
-            for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else for_non_stream()
-
-    def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str:
-        """Generate response `str`
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-        def for_stream():
-            for response in self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            ):
-                yield self.get_message(response)
-
-        def for_non_stream():
-            return self.get_message(
-                self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else for_non_stream()
-
-    def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        return response.get("text")

webscout/Provider/ThinkAnyAI.py

@@ -1,280 +0,0 @@
-import time
-import uuid
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support import expected_conditions as EC
-from selenium.webdriver.support.ui import WebDriverWait
-import click
-import requests
-from requests import get
-from uuid import uuid4
-from re import findall
-from requests.exceptions import RequestException
-from curl_cffi.requests import get, RequestsError
-import g4f
-from random import randint
-from PIL import Image
-import io
-import re
-import json
-import yaml
-from ..AIutel import Optimizers
-from ..AIutel import Conversation
-from ..AIutel import AwesomePrompts, sanitize_stream
-from ..AIbase import  Provider, AsyncProvider
-from Helpingai_T2 import Perplexity
-from webscout import exceptions
-from typing import Any, AsyncGenerator, Dict
-import logging
-import httpx
-#------------------------------------ThinkAnyAI------------
-class ThinkAnyAI(Provider):
-    def __init__(
-        self,
-        model: str = "claude-3-haiku",
-        locale: str = "en",
-        web_search: bool = False,
-        chunk_size: int = 1,
-        streaming: bool = True,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-    ):
-        """Initializes ThinkAnyAI
-
-        Args:
-            model (str): The AI model to be used for generating responses. Defaults to "claude-3-haiku".
-            locale (str): The language locale. Defaults to "en" (English).
-            web_search (bool): Whether to include web search results in the response. Defaults to False.
-            chunk_size (int): The size of data chunks when streaming responses. Defaults to 1.
-            streaming (bool): Whether to stream response data. Defaults to True.
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True.
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-        """
-        self.base_url = "https://thinkany.ai/api"
-        self.model = model
-        self.locale = locale
-        self.web_search = web_search
-        self.chunk_size = chunk_size
-        self.streaming = streaming
-        self.last_response = {}
-        self.session = requests.Session()
-        self.session.proxies = proxies
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, max_tokens, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-
-    def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict | AsyncGenerator:
-        """Chat with AI asynchronously.
-
-            Args:
-                prompt (str): Prompt to be send.
-                stream (bool, optional): Flag for streaming response. Defaults to False.
-                raw (bool, optional): Stream back raw response as received. Defaults to False.
-                optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defeaults to None
-                conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-            Returns:
-                dict : {}
-            ```json
-            {
-                "content": "General Kenobi! \n\n(I couldn't help but respond with the iconic Star Wars greeting since you used it first. )\n\nIs there anything I can help you with today?\n[Image of Hello there General Kenobi]",
-                "conversation_id": "c_f13f6217f9a997aa",
-                "response_id": "r_d3665f95975c368f",
-                "factualityQueries": null,
-                "textQuery": [
-                    "hello there",
-                    1
-                    ],
-                "choices": [
-                    {
-                        "id": "rc_ea075c9671bfd8cb",
-                        "content": [
-                            "General Kenobi! \n\n(I couldn't help but respond with the iconic Star Wars greeting since you used it first. )\n\nIs there anything I can help you with today?\n[Image of Hello there General Kenobi]"
-                        ]
-                    },
-                    {
-                        "id": "rc_de6dd3fb793a5402",
-                        "content": [
-                            "General Kenobi! (or just a friendly hello, whichever you prefer!). \n\nI see you're a person of culture as well. *Star Wars* references are always appreciated.  \n\nHow can I help you today?\n"
-                            ]
-                    },
-                {
-                    "id": "rc_a672ac089caf32db",
-                    "content": [
-                        "General Kenobi! (or just a friendly hello if you're not a Star Wars fan!). \n\nHow can I help you today? Feel free to ask me anything, or tell me what you'd like to chat about. I'm here to assist in any way I can.\n[Image of Obi-Wan Kenobi saying hello there]"
-                    ]
-                }
-            ],
-
-            "images": [
-                "https://i.pinimg.com/originals/40/74/60/407460925c9e419d82b93313f0b42f71.jpg"
-            ]
-        }
-
-            ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-
-        def initiate_conversation(query: str) -> str:
-            """
-            Initiates a new conversation with the ThinkAny AI API.
-
-            Args:
-                query (str): The initial query to start the conversation.
-
-            Returns:
-                str: The UUID (Unique Identifier) of the conversation.
-            """
-            url = f"{self.base_url}/new-conversation"
-            payload = {
-                "content": query,
-                "locale": self.locale,
-                "mode": "search" if self.web_search else "chat",
-                "model": self.model,
-                "source": "all",
-            }
-            response = self.session.post(url, json=payload)
-            return response.json().get("data", {}).get("uuid", "DevsDoCode")
-
-        def RAG_search(uuid: str) -> tuple[bool, list]:
-            """
-            Performs a web search using the Retrieve And Generate (RAG) model.
-
-            Args:
-                uuid (str): The UUID of the conversation.
-
-            Returns:
-                tuple: A tuple containing a boolean indicating the success of the search
-                        and a list of search result links.
-            """
-            if not self.web_search:
-                return True, []
-            url = f"{self.base_url}/rag-search"
-            payload = {"conv_uuid": uuid}
-            response = self.session.post(url, json=payload)
-            links = [source["link"] for source in response.json().get("data", [])]
-            return response.json().get("message", "").strip(), links
-
-        def for_stream():
-            conversation_uuid = initiate_conversation(conversation_prompt)
-            web_search_result, links = RAG_search(conversation_uuid)
-            if not web_search_result:
-                print("Failed to generate WEB response. Making normal Query...")
-
-            url = f"{self.base_url}/chat"
-            payload = {
-                "role": "user",
-                "content": prompt,
-                "conv_uuid": conversation_uuid,
-                "model": self.model,
-            }
-            response = self.session.post(url, json=payload, stream=True)
-            complete_content = ""
-            for content in response.iter_content(
-                decode_unicode=True, chunk_size=self.chunk_size
-            ):
-                complete_content += content
-                yield content if raw else dict(text=complete_content)
-            self.last_response.update(dict(text=complete_content, links=links))
-            self.conversation.update_chat_history(
-                prompt, self.get_message(self.last_response)
-            )
-
-        def for_non_stream():
-            for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else for_non_stream()
-
-    def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str:
-        """Generate response `str`
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-        def for_stream():
-            for response in self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            ):
-                yield self.get_message(response)
-
-        def for_non_stream():
-            return self.get_message(
-                self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else for_non_stream()
-
-    def get_message(self, response: Dict[str, Any]) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        return response["text"]

webscout/Provider/Xjai.py

@@ -1,230 +0,0 @@
-import time
-import uuid
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support import expected_conditions as EC
-from selenium.webdriver.support.ui import WebDriverWait
-import click
-import requests
-from requests import get
-from uuid import uuid4
-from re import findall
-from requests.exceptions import RequestException
-from curl_cffi.requests import get, RequestsError
-import g4f
-from random import randint
-from PIL import Image
-import io
-import re
-import json
-import yaml
-from ..AIutel import Optimizers
-from ..AIutel import Conversation
-from ..AIutel import AwesomePrompts, sanitize_stream
-from ..AIbase import  Provider, AsyncProvider
-from webscout import exceptions
-from typing import Any, AsyncGenerator, Dict
-import logging
-import httpx
-#-----------------------------------------------xjai-------------------------------------------
-class Xjai(Provider):
-    def __init__(
-        self,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        temperature: float = 0.8,
-        top_p: float = 1,
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-    ):
-        """
-        Initializes the Xjai class for interacting with the Xjai AI chat API.
-
-        Args:
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True.
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            temperature (float, optional): The creativity level of the AI's response. Defaults to 0.8.
-            top_p (float, optional): The probability threshold for token selection. Defaults to 1.
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-        """
-        self.session = requests.Session()
-        self.is_conversation = is_conversation
-        self.max_tokens_to_sample = max_tokens
-        self.temperature = temperature
-        self.top_p = top_p
-        self.chat_endpoint = "https://p1api.xjai.pro/freeapi/chat-process"
-        self.stream_chunk_size = 1  # Process response line by line
-        self.timeout = timeout
-        self.last_response = {}
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session.proxies = proxies
-
-    def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> Any:
-        """
-        Sends a chat request to the Xjai AI chat API and returns the response.
-
-        Args:
-            prompt (str): The query to send to the AI.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            raw (bool, optional): Stream back raw response as received. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-
-        Returns:
-            Any: The response from the AI, either as a dictionary or a generator 
-                 depending on the `stream` and `raw` parameters.
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-
-        headers = {
-            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
-                          "(KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36"
-        }
-
-        payload = {
-            "prompt": conversation_prompt + "\n\nReply in English Only",
-            "systemMessage": "Reply in English Only",
-            "temperature": self.temperature,
-            "top_p": self.top_p
-        }
-
-        def generate_response():
-            response = self.session.post(
-                self.chat_endpoint, headers=headers, json=payload, stream=True, timeout=self.timeout
-            )
-            output = ""
-            print_next = False
-
-            for line in response.iter_lines(decode_unicode=True, chunk_size=self.stream_chunk_size):
-                line_content = line.decode("utf-8")
-                # Filter out irrelevant content
-                if '[ChatAI](https://srv.aiflarepro.com/#/?cid=4111)' in line_content:
-                    continue
-                if '&KFw6loC9Qvy&' in line_content:
-                    parts = line_content.split('&KFw6loC9Qvy&')
-                    if print_next:
-                        output += parts[0]
-                        print_next = False
-                    else:
-                        output += parts[1]
-                        print_next = True
-                        if len(parts) > 2:
-                            print_next = False
-                elif print_next:
-                    output += line_content + '\n'
-
-            # Update chat history
-            self.conversation.update_chat_history(prompt, output)
-
-            return output
-
-        def for_stream():
-            response = generate_response()
-            for line in response.splitlines():
-                yield line if raw else dict(text=line)
-
-        def for_non_stream():
-            response = generate_response()
-            return response if raw else dict(text=response)
-
-        return for_stream() if stream else for_non_stream()
-
-    def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> Any:
-        """
-        Generates a response from the Xjai AI chat API.
-
-        Args:
-            prompt (str): The query to send to the AI.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-
-        Returns:
-            Any: The response from the AI, either as a string or a generator 
-                 depending on the `stream` parameter. 
-        """
-
-        def for_stream():
-            for response in self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            ):
-                yield self.get_message(response)
-
-        def for_non_stream():
-            return self.get_message(
-                self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else for_non_stream()
-
-    def get_message(self, response: Any) -> str:
-        """
-        Retrieves the message from the AI's response. 
-
-        Args:
-            response (Any): The response from the AI, either a dictionary 
-                            or a raw string.
-
-        Returns:
-            str: The extracted message from the AI's response. 
-        """
-        if isinstance(response, dict): 
-            return response["text"]
-        else:  # Assume raw string
-            return response
-

webscout/Provider/Yepchat.py

@@ -1,478 +0,0 @@
-import time
-import uuid
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support import expected_conditions as EC
-from selenium.webdriver.support.ui import WebDriverWait
-import click
-import requests
-from requests import get
-from uuid import uuid4
-from re import findall
-from requests.exceptions import RequestException
-from curl_cffi.requests import get, RequestsError
-import g4f
-from random import randint
-from PIL import Image
-import io
-import re
-import json
-import yaml
-from ..AIutel import Optimizers
-from ..AIutel import Conversation
-from ..AIutel import AwesomePrompts, sanitize_stream
-from ..AIbase import  Provider, AsyncProvider
-from Helpingai_T2 import Perplexity
-from webscout import exceptions
-from typing import Any, AsyncGenerator, Dict
-import logging
-import httpx
-
-#-------------------------------------------------------yep.com--------------------------------------------------------   
-class YEPCHAT(Provider):
-    def __init__(
-        self,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        temperature: float = 0.6,
-        presence_penalty: int = 0,
-        frequency_penalty: int = 0,
-        top_p: float = 0.7,
-        model: str = "Mixtral-8x7B-Instruct-v0.1",
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-    ):
-        """Instantiates YEPCHAT
-
-        Args:
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True.
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            temperature (float, optional): Charge of the generated text's randomness. Defaults to 0.6.
-            presence_penalty (int, optional): Chances of topic being repeated. Defaults to 0.
-            frequency_penalty (int, optional): Chances of word being repeated. Defaults to 0.
-            top_p (float, optional): Sampling threshold during inference time. Defaults to 0.7.
-            model (str, optional): LLM model name. Defaults to "gpt-3.5-turbo".
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-        """
-        self.session = requests.Session()
-        self.is_conversation = is_conversation
-        self.max_tokens_to_sample = max_tokens
-        self.model = model
-        self.temperature = temperature
-        self.presence_penalty = presence_penalty
-        self.frequency_penalty = frequency_penalty
-        self.top_p = top_p
-        self.chat_endpoint = "https://api.yep.com/v1/chat/completions"
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.headers = {
-            "Accept": "*/*",
-            "Accept-Encoding": "gzip, deflate",
-            "Accept-Language": "en-US,en;q=0.9",
-            "Content-Type": "application/json; charset=utf-8",
-            "Origin": "https://yep.com",
-            "Referer": "https://yep.com/",
-            "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
-        }
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        self.session.headers.update(self.headers)
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session.proxies = proxies
-
-    def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict:
-        """Chat with AI
-
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            raw (bool, optional): Stream back raw response as received. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-           dict : {}
-        ```json
-        {
-            "id": "cmpl-c61c1c88de4e4ad3a79134775d17ea0c",
-            "object": "chat.completion.chunk",
-            "created": 1713876886,
-            "model": "Mixtral-8x7B-Instruct-v0.1",
-            "choices": [
-                {
-                    "index": 0,
-                    "delta": {
-                        "role": null,
-                        "content": " Sure, I can help with that. Are you looking for information on how to start coding, or do you need help with a specific coding problem? We can discuss various programming languages like Python, JavaScript, Java, C++, or others. Please provide more details so I can assist you better."
-                        },
-                    "finish_reason": null
-                }
-            ]
-        }
-        ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-        self.session.headers.update(self.headers)
-        payload = {
-            "stream": True,
-            "max_tokens": 1280,
-            "top_p": self.top_p,
-            "temperature": self.temperature,
-            "messages": [{"content": conversation_prompt, "role": "user"}],
-            "model": self.model,
-        }
-
-        def for_stream():
-            response = self.session.post(
-                self.chat_endpoint, json=payload, stream=True, timeout=self.timeout
-            )
-            if not response.ok:
-                raise exceptions.FailedToGenerateResponseError(
-                    f"Failed to generate response - ({response.status_code}, {response.reason}) - {response.text}"
-                )
-
-            message_load = ""
-            for value in response.iter_lines(
-                decode_unicode=True,
-                delimiter="" if raw else "data:",
-                chunk_size=self.stream_chunk_size,
-            ):
-                try:
-                    resp = json.loads(value)
-                    incomplete_message = self.get_message(resp)
-                    if incomplete_message:
-                        message_load += incomplete_message
-                        resp["choices"][0]["delta"]["content"] = message_load
-                        self.last_response.update(resp)
-                        yield value if raw else resp
-                    elif raw:
-                        yield value
-                except json.decoder.JSONDecodeError:
-                    pass
-            self.conversation.update_chat_history(
-                prompt, self.get_message(self.last_response)
-            )
-
-        def for_non_stream():
-            for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else for_non_stream()
-
-    def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str:
-        """Generate response `str`
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-        def for_stream():
-            for response in self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            ):
-                yield self.get_message(response)
-
-        def for_non_stream():
-            return self.get_message(
-                self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else for_non_stream()
-
-    def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        try:
-            if response["choices"][0].get("delta"):
-                return response["choices"][0]["delta"]["content"]
-            return response["choices"][0]["message"]["content"]
-        except KeyError:
-            return ""
-class AsyncYEPCHAT(AsyncProvider):
-    def __init__(
-        self,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        temperature: float = 0.6,
-        presence_penalty: int = 0,
-        frequency_penalty: int = 0,
-        top_p: float = 0.7,
-        model: str = "Mixtral-8x7B-Instruct-v0.1",
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-    ):
-        """Instantiates YEPCHAT
-
-        Args:
-            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True.
-            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
-            temperature (float, optional): Charge of the generated text's randomness. Defaults to 0.6.
-            presence_penalty (int, optional): Chances of topic being repeated. Defaults to 0.
-            frequency_penalty (int, optional): Chances of word being repeated. Defaults to 0.
-            top_p (float, optional): Sampling threshold during inference time. Defaults to 0.7.
-            model (str, optional): LLM model name. Defaults to "gpt-3.5-turbo".
-            timeout (int, optional): Http request timeout. Defaults to 30.
-            intro (str, optional): Conversation introductory prompt. Defaults to None.
-            filepath (str, optional): Path to file containing conversation history. Defaults to None.
-            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
-            proxies (dict, optional): Http request proxies. Defaults to {}.
-            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
-            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
-        """
-        self.session = requests.Session()
-        self.is_conversation = is_conversation
-        self.max_tokens_to_sample = max_tokens
-        self.model = model
-        self.temperature = temperature
-        self.presence_penalty = presence_penalty
-        self.frequency_penalty = frequency_penalty
-        self.top_p = top_p
-        self.chat_endpoint = "https://api.yep.com/v1/chat/completions"
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-        self.headers = {
-            "Accept": "*/*",
-            "Accept-Encoding": "gzip, deflate",
-            "Accept-Language": "en-US,en;q=0.9",
-            "Content-Type": "application/json; charset=utf-8",
-            "Origin": "https://yep.com",
-            "Referer": "https://yep.com/",
-            "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
-        }
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session = httpx.AsyncClient(
-            headers=self.headers,
-            proxies=proxies,
-        )
-
-    async def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict:
-        """Chat with AI asynchronously.
-
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            raw (bool, optional): Stream back raw response as received. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-           dict : {}
-        ```json
-        {
-            "id": "cmpl-c61c1c88de4e4ad3a79134775d17ea0c",
-            "object": "chat.completion.chunk",
-            "created": 1713876886,
-            "model": "Mixtral-8x7B-Instruct-v0.1",
-            "choices": [
-                {
-                    "index": 0,
-                    "delta": {
-                        "role": null,
-                        "content": " Sure, I can help with that. Are you looking for information on how to start coding, or do you need help with a specific coding problem? We can discuss various programming languages like Python, JavaScript, Java, C++, or others. Please provide more details so I can assist you better."
-                        },
-                    "finish_reason": null
-                }
-            ]
-        }
-        ```
-        """
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-        payload = {
-            "stream": True,
-            "max_tokens": 1280,
-            "top_p": self.top_p,
-            "temperature": self.temperature,
-            "messages": [{"content": conversation_prompt, "role": "user"}],
-            "model": self.model,
-        }
-
-        async def for_stream():
-            async with self.session.stream(
-                "POST", self.chat_endpoint, json=payload, timeout=self.timeout
-            ) as response:
-                if not response.is_success:
-                    raise exceptions.FailedToGenerateResponseError(
-                        f"Failed to generate response - ({response.status_code}, {response.reason_phrase}) - {response.text}"
-                    )
-
-                message_load = ""
-                async for value in response.aiter_lines():
-                    try:
-                        resp = sanitize_stream(value)
-                        incomplete_message = await self.get_message(resp)
-                        if incomplete_message:
-                            message_load += incomplete_message
-                            resp["choices"][0]["delta"]["content"] = message_load
-                            self.last_response.update(resp)
-                            yield value if raw else resp
-                        elif raw:
-                            yield value
-                    except json.decoder.JSONDecodeError:
-                        pass
-
-            self.conversation.update_chat_history(
-                prompt, await self.get_message(self.last_response)
-            )
-
-        async def for_non_stream():
-            async for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else await for_non_stream()
-
-    async def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str:
-        """Generate response `str` asynchronously.
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-        async def for_stream():
-            async_ask = await self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            )
-
-            async for response in async_ask:
-                yield await self.get_message(response)
-
-        async def for_non_stream():
-            return await self.get_message(
-                await self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else await for_non_stream()
-
-    async def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        try:
-            if response["choices"][0].get("delta"):
-                return response["choices"][0]["delta"]["content"]
-            return response["choices"][0]["message"]["content"]
-        except KeyError:
-            return ""

webscout/Provider/Youchat.py

@@ -1,221 +0,0 @@
-import time
-import uuid
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support import expected_conditions as EC
-from selenium.webdriver.support.ui import WebDriverWait
-import click
-import requests
-from requests import get
-from uuid import uuid4
-from re import findall
-from requests.exceptions import RequestException
-from curl_cffi.requests import get, RequestsError
-import g4f
-from random import randint
-from PIL import Image
-import io
-import re
-import json
-import yaml
-from ..AIutel import Optimizers
-from ..AIutel import Conversation
-from ..AIutel import AwesomePrompts, sanitize_stream
-from ..AIbase import  Provider, AsyncProvider
-from Helpingai_T2 import Perplexity
-from webscout import exceptions
-from typing import Any, AsyncGenerator, Dict
-import logging
-import httpx
-
-#-------------------------------------------------------youchat--------------------------------------------------------   
-class YouChat(Provider):
-    def __init__(
-        self,
-        is_conversation: bool = True,
-        max_tokens: int = 600,
-        timeout: int = 30,
-        intro: str = None,
-        filepath: str = None,
-        update_file: bool = True,
-        proxies: dict = {},
-        history_offset: int = 10250,
-        act: str = None,
-    ):
-        self.session = requests.Session()
-        self.is_conversation = is_conversation
-        self.max_tokens_to_sample = max_tokens
-        self.chat_endpoint = "https://you.com/api/streamingSearch"
-        self.stream_chunk_size = 64
-        self.timeout = timeout
-        self.last_response = {}
-
-        self.payload = {
-            "q": "",
-            "page": 1,
-            "count": 10,
-            "safeSearch": "Off",
-            "onShoppingPage": False,
-            "mkt": "",
-            "responseFilter": "WebPages,Translations,TimeZone,Computation,RelatedSearches",
-            "domain": "youchat",
-            "queryTraceId": uuid.uuid4(),
-            "conversationTurnId": uuid.uuid4(),
-            "pastChatLength": 0,
-            "selectedChatMode": "default",
-            "chat": "[]",
-        }
-
-        self.headers = {
-            "cache-control": "no-cache",
-            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36',
-            'Referer': f'https://you.com/search?q={self.payload["q"]}&fromSearchBar=true&tbm=youchat&chatMode=default'
-        }
-
-        self.__available_optimizers = (
-            method
-            for method in dir(Optimizers)
-            if callable(getattr(Optimizers, method)) and not method.startswith("__")
-        )
-        self.session.headers.update(self.headers)
-        Conversation.intro = (
-            AwesomePrompts().get_act(
-                act, raise_not_found=True, default=None, case_insensitive=True
-            )
-            if act
-            else intro or Conversation.intro
-        )
-        self.conversation = Conversation(
-            is_conversation, self.max_tokens_to_sample, filepath, update_file
-        )
-        self.conversation.history_offset = history_offset
-        self.session.proxies = proxies
-
-    def ask(
-        self,
-        prompt: str,
-        stream: bool = False,
-        raw: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> dict:
-        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
-        if optimizer:
-            if optimizer in self.__available_optimizers:
-                conversation_prompt = getattr(Optimizers, optimizer)(
-                    conversation_prompt if conversationally else prompt
-                )
-            else:
-                raise Exception(
-                    f"Optimizer is not one of {self.__available_optimizers}"
-                )
-        self.session.headers.update(self.headers)
-        self.session.headers.update(
-            dict(
-                cookie=f"safesearch_guest=Off; uuid_guest={str(uuid4())}",
-            )
-        )
-        self.payload["q"] = prompt
-
-        def for_stream():
-            response = self.session.get(
-                self.chat_endpoint,
-                params=self.payload,
-                stream=True,
-                timeout=self.timeout,
-            )
-
-            if not response.ok:
-                raise exceptions.FailedToGenerateResponseError(
-                    f"Failed to generate response - ({response.status_code}, {response.reason})"
-                )
-
-            streaming_response = ""
-            for line in response.iter_lines(decode_unicode=True, chunk_size=64):
-                if line:
-                    modified_value = re.sub("data:", "", line)
-                    try:
-                        json_modified_value = json.loads(modified_value)
-                        if "youChatToken" in json_modified_value:
-                            streaming_response += json_modified_value["youChatToken"]
-                            if print:
-                                print(json_modified_value["youChatToken"], end="")
-                    except:
-                        continue
-            self.last_response.update(dict(text=streaming_response))
-            self.conversation.update_chat_history(
-                prompt, self.get_message(self.last_response)
-            )
-            return streaming_response
-
-        def for_non_stream():
-            for _ in for_stream():
-                pass
-            return self.last_response
-
-        return for_stream() if stream else for_non_stream()
-
-    def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str:
-        """Generate response `str`
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-    def chat(
-        self,
-        prompt: str,
-        stream: bool = False,
-        optimizer: str = None,
-        conversationally: bool = False,
-    ) -> str:
-        """Generate response `str`
-        Args:
-            prompt (str): Prompt to be send.
-            stream (bool, optional): Flag for streaming response. Defaults to False.
-            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
-            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
-        Returns:
-            str: Response generated
-        """
-
-        def for_stream():
-            for response in self.ask(
-                prompt, True, optimizer=optimizer, conversationally=conversationally
-            ):
-                yield self.get_message(response)
-
-        def for_non_stream():
-            return self.get_message(
-                self.ask(
-                    prompt,
-                    False,
-                    optimizer=optimizer,
-                    conversationally=conversationally,
-                )
-            )
-
-        return for_stream() if stream else for_non_stream()
-
-    def get_message(self, response: dict) -> str:
-        """Retrieves message only from response
-
-        Args:
-            response (dict): Response generated by `self.ask`
-
-        Returns:
-            str: Message extracted
-        """
-        assert isinstance(response, dict), "Response should be of dict data-type only"
-        return response["text"]

webscout/Provider/__init__.py

@@ -1,61 +0,0 @@
-# webscout/providers/__init__.py
-
-from .ThinkAnyAI import ThinkAnyAI
-from .Xjai import Xjai
-from .Llama2 import LLAMA2
-from .Llama2 import AsyncLLAMA2
-from .Cohere import Cohere
-from .Reka import REKA
-from .Groq import GROQ
-from .Groq import AsyncGROQ
-from .Openai import OPENAI
-from .Openai import AsyncOPENAI
-from .Leo import LEO
-from .Leo import AsyncLEO
-from .Koboldai import KOBOLDAI
-from .Koboldai import AsyncKOBOLDAI
-from .OpenGPT import OPENGPT 
-from .OpenGPT import AsyncOPENGPT
-from .Perplexity import PERPLEXITY
-from .Blackboxai import BLACKBOXAI 
-from .Blackboxai import AsyncBLACKBOXAI
-from .Phind import PhindSearch 
-from .Phind import AsyncPhindSearch
-from .Yepchat import YEPCHAT
-from .Yepchat import AsyncYEPCHAT
-from .Youchat import YouChat
-from .Gemini import GEMINI
-from .Berlin4h import Berlin4h
-from .ChatGPTUK import ChatGPTUK
-from .Poe import POE
-from .BasedGPT import *
-__all__ = [
-    'ThinkAnyAI',
-    'Xjai',
-    'LLAMA2', 
-    'AsyncLLAMA2',
-    'Cohere',
-    'REKA',
-    'GROQ',
-    'AsyncGROQ',
-    'OPENAI',
-    'AsyncOPENAI',
-    'LEO',
-    'AsyncLEO',
-    'KOBOLDAI',
-    'AsyncKOBOLDAI',
-    'OPENGPT', 
-    'AsyncOPENGPT',
-    'PERPLEXITY',
-    'BLACKBOXAI', 
-    'AsyncBLACKBOXAI',
-    'PhindSearch', 
-    'AsyncPhindSearch',
-    'YEPCHAT',
-    'AsyncYEPCHAT',
-    'YouChat',
-    'GEMINI',
-    'Berlin4h',
-    'ChatGPTUK',
-    'POE'
-]