Delete api

api/__pycache__/dummy.txt

@@ -1 +0,0 @@
-

api/app.py

@@ -1,40 +0,0 @@
-from fastapi import FastAPI, Request
-from starlette.middleware.cors import CORSMiddleware
-from fastapi.responses import JSONResponse
-from api.logger import setup_logger
-from api.routes import router
-
-logger = setup_logger(__name__)
-
-def create_app():
-    app = FastAPI(
-        title="NiansuhAI API Gateway",
-        docs_url=None,          # Disable Swagger UI
-        redoc_url=None,         # Disable ReDoc
-        openapi_url=None,       # Disable OpenAPI schema
-    )
-
-    # CORS settings
-    app.add_middleware(
-        CORSMiddleware,
-        allow_origins=["*"],                   # Adjust as needed for security
-        allow_credentials=True,
-        allow_methods=["*"],
-        allow_headers=["*"],
-    )
-
-    # Include routes
-    app.include_router(router)
-
-    # Global exception handler for better error reporting
-    @app.exception_handler(Exception)
-    async def global_exception_handler(request: Request, exc: Exception):
-        logger.error(f"An error occurred: {str(exc)}")
-        return JSONResponse(
-            status_code=500,
-            content={"message": "An internal server error occurred."},
-        )
-
-    return app
-
-app = create_app()

api/auth.py

@@ -1,10 +0,0 @@
-from fastapi import Depends, HTTPException
-from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer
-from api.config import APP_SECRET
-
-security = HTTPBearer()
-
-def verify_app_secret(credentials: HTTPAuthorizationCredentials = Depends(security)):
-    if credentials.credentials != APP_SECRET:
-        raise HTTPException(status_code=403, detail="Invalid APP_SECRET")
-    return credentials.credentials

api/config.py

@@ -1,153 +0,0 @@
-import os
-from dotenv import load_dotenv
-
-load_dotenv()
-
-BASE_URL = "https://www.blackbox.ai"
-headers = {
-    'accept': '*/*',
-    'accept-language': 'en-US,en;q=0.9',
-    'origin': 'https://www.blackbox.ai',
-    'priority': 'u=1, i',
-    'sec-ch-ua': '"Chromium";v="130", "Google Chrome";v="130", "Not?A_Brand";v="99"',
-    'sec-ch-ua-mobile': '?0',
-    'sec-ch-ua-platform': '"Windows"',
-    'sec-fetch-dest': 'empty',
-    'sec-fetch-mode': 'cors',
-    'sec-fetch-site': 'same-origin',
-    'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) '
-                  'AppleWebKit/537.36 (KHTML, like Gecko) '
-                  'Chrome/130.0.0.0 Safari/537.36',
-}
-APP_SECRET = os.getenv("APP_SECRET")
-
-ALLOWED_MODELS = [
-    {"id": "blackboxai", "name": "blackboxai"},
-    {"id": "blackboxai-pro", "name": "blackboxai-pro"},
-    {"id": "flux", "name": "flux"},
-    {"id": "llama-3.1-8b", "name": "llama-3.1-8b"},
-    {"id": "llama-3.1-70b", "name": "llama-3.1-70b"},
-    {"id": "llama-3.1-405b", "name": "llama-3.1-405b"},
-    {"id": "gpt-4o", "name": "gpt-4o"},
-    {"id": "gemini-pro", "name": "gemini-pro"},
-    {"id": "gemini-1.5-flash", "name": "gemini-1.5-flash"},
-    {"id": "claude-sonnet-3.5", "name": "claude-sonnet-3.5"},
-    {"id": "PythonAgent", "name": "PythonAgent"},
-    {"id": "JavaAgent", "name": "JavaAgent"},
-    {"id": "JavaScriptAgent", "name": "JavaScriptAgent"},
-    {"id": "HTMLAgent", "name": "HTMLAgent"},
-    {"id": "GoogleCloudAgent", "name": "GoogleCloudAgent"},
-    {"id": "AndroidDeveloper", "name": "AndroidDeveloper"},
-    {"id": "SwiftDeveloper", "name": "SwiftDeveloper"},
-    {"id": "Next.jsAgent", "name": "Next.jsAgent"},
-    {"id": "MongoDBAgent", "name": "MongoDBAgent"},
-    {"id": "PyTorchAgent", "name": "PyTorchAgent"},
-    {"id": "ReactAgent", "name": "ReactAgent"},
-    {"id": "XcodeAgent", "name": "XcodeAgent"},
-    {"id": "AngularJSAgent", "name": "AngularJSAgent"},
-    {"id": "RepoMap", "name": "RepoMap"},
-    {"id": "gemini-1.5-pro-latest", "name": "gemini-pro"},
-    {"id": "gemini-1.5-pro", "name": "gemini-1.5-pro"},
-    {"id": "claude-3-5-sonnet-20240620", "name": "claude-sonnet-3.5"},
-    {"id": "claude-3-5-sonnet", "name": "claude-sonnet-3.5"},
-    {"id": "Niansuh", "name": "Niansuh"},
-]
-
-MODEL_MAPPING = {
-    "blackboxai": "blackboxai",
-    "blackboxai-pro": "blackboxai-pro",
-    "flux": "flux",
-    "ImageGeneration": "flux",
-    "llama-3.1-8b": "llama-3.1-8b",
-    "llama-3.1-70b": "llama-3.1-70b",
-    "llama-3.1-405b": "llama-3.1-405b",
-    "gpt-4o": "gpt-4o",
-    "gemini-pro": "gemini-pro",
-    "gemini-1.5-flash": "gemini-1.5-flash",
-    "claude-sonnet-3.5": "claude-sonnet-3.5",
-    "PythonAgent": "PythonAgent",
-    "JavaAgent": "JavaAgent",
-    "JavaScriptAgent": "JavaScriptAgent",
-    "HTMLAgent": "HTMLAgent",
-    "GoogleCloudAgent": "GoogleCloudAgent",
-    "AndroidDeveloper": "AndroidDeveloper",
-    "SwiftDeveloper": "SwiftDeveloper",
-    "Next.jsAgent": "Next.jsAgent",
-    "MongoDBAgent": "MongoDBAgent",
-    "PyTorchAgent": "PyTorchAgent",
-    "ReactAgent": "ReactAgent",
-    "XcodeAgent": "XcodeAgent",
-    "AngularJSAgent": "AngularJSAgent",
-    "RepoMap": "RepoMap",
-    # Additional mappings
-    "gemini-flash": "gemini-1.5-flash",
-    "claude-3.5-sonnet": "claude-sonnet-3.5",
-    "flux": "flux",
-    "gemini-1.5-pro-latest": "gemini-pro",
-    "gemini-1.5-pro": "gemini-1.5-pro",
-    "claude-3-5-sonnet-20240620": "claude-sonnet-3.5",
-    "claude-3-5-sonnet": "claude-sonnet-3.5",
-    "Niansuh": "Niansuh",
-}
-
-# Agent modes
-AGENT_MODE = {
-    'flux': {'mode': True, 'id': "ImageGenerationLV45LJp", 'name': "flux"},
-    'Niansuh': {'mode': True, 'id': "NiansuhAIk1HgESy", 'name': "Niansuh"},
-
-}
-
-TRENDING_AGENT_MODE = {
-    "blackboxai": {},
-    "gemini-1.5-flash": {'mode': True, 'id': 'Gemini'},
-    "llama-3.1-8b": {'mode': True, 'id': "llama-3.1-8b"},
-    'llama-3.1-70b': {'mode': True, 'id': "llama-3.1-70b"},
-    'llama-3.1-405b': {'mode': True, 'id': "llama-3.1-405b"},
-    'blackboxai-pro': {'mode': True, 'id': "BLACKBOXAI-PRO"},
-    'PythonAgent': {'mode': True, 'id': "Python Agent"},
-    'JavaAgent': {'mode': True, 'id': "Java Agent"},
-    'JavaScriptAgent': {'mode': True, 'id': "JavaScript Agent"},
-    'HTMLAgent': {'mode': True, 'id': "HTML Agent"},
-    'GoogleCloudAgent': {'mode': True, 'id': "Google Cloud Agent"},
-    'AndroidDeveloper': {'mode': True, 'id': "Android Developer"},
-    'SwiftDeveloper': {'mode': True, 'id': "Swift Developer"},
-    'Next.jsAgent': {'mode': True, 'id': "Next.js Agent"},
-    'MongoDBAgent': {'mode': True, 'id': "MongoDB Agent"},
-    'PyTorchAgent': {'mode': True, 'id': "PyTorch Agent"},
-    'ReactAgent': {'mode': True, 'id': "React Agent"},
-    'XcodeAgent': {'mode': True, 'id': "Xcode Agent"},
-    'AngularJSAgent': {'mode': True, 'id': "AngularJS Agent"},
-    'RepoMap': {'mode': True, 'id': "repomap"},
-}
-
-# Model prefixes
-MODEL_PREFIXES = {
-    'gpt-4o': '@GPT-4o',
-    'gemini-pro': '@Gemini-PRO',
-    'claude-sonnet-3.5': '@Claude-Sonnet-3.5',
-    'PythonAgent': '@Python Agent',
-    'JavaAgent': '@Java Agent',
-    'JavaScriptAgent': '@JavaScript Agent',
-    'HTMLAgent': '@HTML Agent',
-    'GoogleCloudAgent': '@Google Cloud Agent',
-    'AndroidDeveloper': '@Android Developer',
-    'SwiftDeveloper': '@Swift Developer',
-    'Next.jsAgent': '@Next.js Agent',
-    'MongoDBAgent': '@MongoDB Agent',
-    'PyTorchAgent': '@PyTorch Agent',
-    'ReactAgent': '@React Agent',
-    'XcodeAgent': '@Xcode Agent',
-    'AngularJSAgent': '@AngularJS Agent',
-    'blackboxai-pro': '@BLACKBOXAI-PRO',
-    'flux': '@Image Generation',
-    # Add any additional prefixes if necessary
-}
-
-# Model referers
-MODEL_REFERERS = {
-    "blackboxai": "/?model=blackboxai",
-    "gpt-4o": "/?model=gpt-4o",
-    "gemini-pro": "/?model=gemini-pro",
-    "claude-sonnet-3.5": "/?model=claude-sonnet-3.5"
-    # Add any additional referers if necessary
-}

api/gizai.py

@@ -1,169 +0,0 @@
-# api/gizai.py
-
-from __future__ import annotations
-
-import json
-from aiohttp import ClientSession
-from typing import AsyncGenerator, Union
-
-from .models import AsyncResult, Messages, ImageResponse
-from .utils import strip_model_prefix
-
-class AsyncGeneratorProvider:
-    @classmethod
-    async def create_async_generator(cls, *args, **kwargs) -> AsyncGenerator:
-        """Abstract method to create an asynchronous generator."""
-        raise NotImplementedError
-
-class ProviderModelMixin:
-    @classmethod
-    def get_model(cls, model: str) -> str:
-        """Abstract method to get the actual model name."""
-        raise NotImplementedError
-
-class GizAI(AsyncGeneratorProvider, ProviderModelMixin):
-    url = "https://app.giz.ai/assistant/"
-    api_endpoint = "https://app.giz.ai/api/data/users/inferenceServer.infer"
-    working = True
-    
-    supports_system_message = True
-    supports_message_history = True
-    
-    # Chat models
-    default_model = 'chat-gemini-flash'
-    chat_models = [
-        default_model,
-        'chat-gemini-pro',
-        'chat-gpt4m',
-        'chat-gpt4',
-        'claude-sonnet',
-        'claude-haiku',
-        'llama-3-70b',
-        'llama-3-8b',
-        'mistral-large',
-        'chat-o1-mini'
-    ]
-
-    # Image models
-    image_models = [
-        'flux1',
-        'sdxl',
-        'sd',
-        'sd35',
-    ]
-
-    models = [*chat_models, *image_models]
-    
-    model_aliases = {
-        # Chat model aliases
-        "gemini-flash": "chat-gemini-flash",
-        "gemini-pro": "chat-gemini-pro",
-        "gpt-4o-mini": "chat-gpt4m",
-        "gpt-4o": "chat-gpt4",
-        "claude-3.5-sonnet": "claude-sonnet",
-        "claude-3-haiku": "claude-haiku",
-        "llama-3.1-70b": "llama-3-70b",
-        "llama-3.1-8b": "llama-3-8b",
-        "o1-mini": "chat-o1-mini",
-        # Image model aliases
-        "sd-1.5": "sd",
-        "sd-3.5": "sd35",
-        "flux-schnell": "flux1",
-    }
-
-    @classmethod
-    def get_model(cls, model: str) -> str:
-        """Retrieve the actual model name, handling aliases."""
-        if model in cls.models:
-            return model
-        elif model in cls.model_aliases:
-            return cls.model_aliases[model]
-        else:
-            return cls.default_model
-
-    @classmethod
-    def is_image_model(cls, model: str) -> bool:
-        """Determine if the given model is an image generation model."""
-        return model in cls.image_models
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: Messages,
-        proxy: str = None,
-        **kwargs
-    ) -> AsyncResult:
-        """Create an asynchronous generator for processing requests."""
-        model = cls.get_model(model)
-        
-        headers = {
-            'Accept': 'application/json, text/plain, */*',
-            'Accept-Language': 'en-US,en;q=0.9',
-            'Cache-Control': 'no-cache',
-            'Connection': 'keep-alive',
-            'Content-Type': 'application/json',
-            'Origin': 'https://app.giz.ai',
-            'Pragma': 'no-cache',
-            'Sec-Fetch-Dest': 'empty',
-            'Sec-Fetch-Mode': 'cors',
-            'Sec-Fetch-Site': 'same-origin',
-            'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36',
-            'sec-ch-ua': '"Not?A_Brand";v="99", "Chromium";v="130"',
-            'sec-ch-ua-mobile': '?0',
-            'sec-ch-ua-platform': '"Linux"'
-        }
-
-        async with ClientSession() as session:
-            if cls.is_image_model(model):
-                # Image generation
-                prompt = messages[-1]["content"]
-                data = {
-                    "model": model,
-                    "input": {
-                        "width": "1024",
-                        "height": "1024",
-                        "steps": 4,
-                        "output_format": "webp",
-                        "batch_size": 1,
-                        "mode": "plan",
-                        "prompt": prompt
-                    }
-                }
-                async with session.post(
-                    cls.api_endpoint,
-                    headers=headers,
-                    data=json.dumps(data),
-                    proxy=proxy
-                ) as response:
-                    response.raise_for_status()
-                    response_data = await response.json()
-                    if response_data.get('status') == 'completed' and response_data.get('output'):
-                        for url in response_data['output']:
-                            yield ImageResponse(images=url, alt="Generated Image")
-            else:
-                # Chat completion
-                # Directly format the prompt without using a separate helper
-                prompt = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
-                data = {
-                    "model": model,
-                    "input": {
-                        "messages": [
-                            {
-                                "type": "human",
-                                "content": prompt
-                            }
-                        ],
-                        "mode": "plan"
-                    },
-                    "noStream": True
-                }
-                async with session.post(
-                    cls.api_endpoint,
-                    headers=headers,
-                    data=json.dumps(data),
-                    proxy=proxy
-                ) as response:
-                    response.raise_for_status()
-                    result = await response.json()
-                    yield result.get('output', '')

api/logger.py

@@ -1,20 +0,0 @@
-import logging
-
-def setup_logger(name):
-    logger = logging.getLogger(name)
-    if not logger.handlers:
-        logger.setLevel(logging.INFO)
-        formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
-        
-        # Console handler
-        console_handler = logging.StreamHandler()
-        console_handler.setFormatter(formatter)
-        logger.addHandler(console_handler)
-        
-        # File Handler - Error Level
-        # error_file_handler = logging.FileHandler('error.log')
-        # error_file_handler.setFormatter(formatter)
-        # error_file_handler.setLevel(logging.ERROR)
-        # logger.addHandler(error_file_handler)
-    
-    return logger

api/models.py

@@ -1,14 +0,0 @@
-from typing import List, Optional
-from pydantic import BaseModel
-
-class Message(BaseModel):
-    role: str
-    content: str | list
-
-class ChatRequest(BaseModel):
-    model: str
-    messages: List[Message]
-    stream: Optional[bool] = False
-    temperature: Optional[float] = 0.7
-    top_p: Optional[float] = 0.9
-    max_tokens: Optional[int] = 99999999

api/provider/blackboxai.py

@@ -1,128 +0,0 @@
-from __future__ import annotations
-
-import json
-from aiohttp import ClientSession
-
-from ..typing import AsyncResult, Messages
-from ..image import ImageResponse
-from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
-from .helper import format_prompt
-
-class BlackBoxAI(AsyncGeneratorProvider, ProviderModelMixin):
-    url = "https://api.blackboxai.com/assistant/"
-    api_endpoint = "https://api.blackboxai.com/api/data/users/inferenceServer.infer"
-    working = True
-
-    supports_system_message = True
-    supports_message_history = True
-
-    # Chat models
-    default_model = 'chat-blackboxai-basic'
-    chat_models = [
-        default_model,
-        # Add other chat models as needed
-    ]
-
-    # Image models
-    image_models = [
-        'bbai-sdxl',
-        # Add other image models as needed
-    ]
-
-    models = [*chat_models, *image_models]
-
-    model_aliases = {
-        # Define aliases as needed
-    }
-
-    @classmethod
-    def get_model(cls, model: str) -> str:
-        if model in cls.models:
-            return model
-        elif model in cls.model_aliases:
-            return cls.model_aliases[model]
-        else:
-            return cls.default_model
-
-    @classmethod
-    def is_image_model(cls, model: str) -> bool:
-        return model in cls.image_models
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: Messages,
-        proxy: str = None,
-        **kwargs
-    ) -> AsyncResult:
-        model = cls.get_model(model)
-
-        headers = {
-            'Accept': 'application/json, text/plain, */*',
-            'Accept-Language': 'en-US,en;q=0.9',
-            'Cache-Control': 'no-cache',
-            'Connection': 'keep-alive',
-            'Content-Type': 'application/json',
-            'Origin': 'https://api.blackboxai.com',
-            'Pragma': 'no-cache',
-            'Sec-Fetch-Dest': 'empty',
-            'Sec-Fetch-Mode': 'cors',
-            'Sec-Fetch-Site': 'same-origin',
-            'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36',
-            'sec-ch-ua': '"Not?A_Brand";v="99", "Chromium";v="130"',
-            'sec-ch-ua-mobile': '?0',
-            'sec-ch-ua-platform': '"Linux"'
-        }
-
-        async with ClientSession() as session:
-            if cls.is_image_model(model):
-                # Image generation
-                prompt = messages[-1]["content"]
-                data = {
-                    "model": model,
-                    "input": {
-                        "width": "1024",
-                        "height": "1024",
-                        "steps": 4,
-                        "output_format": "webp",
-                        "batch_size": 1,
-                        "mode": "plan",
-                        "prompt": prompt
-                    }
-                }
-                async with session.post(
-                    cls.api_endpoint,
-                    headers=headers,
-                    data=json.dumps(data),
-                    proxy=proxy
-                ) as response:
-                    response.raise_for_status()
-                    response_data = await response.json()
-                    if response_data.get('status') == 'completed' and response_data.get('output'):
-                        for url in response_data['output']:
-                            yield ImageResponse(images=url, alt="Generated Image")
-            else:
-                # Chat completion
-                data = {
-                    "model": model,
-                    "input": {
-                        "messages": [
-                            {
-                                "type": "human",
-                                "content": format_prompt(messages)
-                            }
-                        ],
-                        "mode": "plan"
-                    },
-                    "noStream": True
-                }
-                async with session.post(
-                    cls.api_endpoint,
-                    headers=headers,
-                    data=json.dumps(data),
-                    proxy=proxy
-                ) as response:
-                    response.raise_for_status()
-                    result = await response.json()
-                    yield result.get('output', '')

api/provider/gizai.py

@@ -1,151 +0,0 @@
-from __future__ import annotations
-
-import json
-from aiohttp import ClientSession
-
-from ..typing import AsyncResult, Messages
-from ..image import ImageResponse
-from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
-from .helper import format_prompt
-
-class GizAI(AsyncGeneratorProvider, ProviderModelMixin):
-    url = "https://app.giz.ai/assistant/"
-    api_endpoint = "https://app.giz.ai/api/data/users/inferenceServer.infer"
-    working = True
-
-    supports_system_message = True
-    supports_message_history = True
-
-    # Chat models
-    default_model = 'chat-gemini-flash'
-    chat_models = [
-        default_model,
-        'chat-gemini-pro',
-        'chat-gpt4m',
-        'chat-gpt4',
-        'claude-sonnet',
-        'claude-haiku',
-        'llama-3-70b',
-        'llama-3-8b',
-        'mistral-large',
-        'chat-o1-mini'
-    ]
-
-    # Image models
-    image_models = [
-        'flux1',
-        'sdxl',
-        'sd',
-        'sd35',
-    ]
-
-    models = [*chat_models, *image_models]
-
-    model_aliases = {
-        # Chat model aliases
-        "gemini-flash": "chat-gemini-flash",
-        "gemini-pro": "chat-gemini-pro",
-        "gpt-4o-mini": "chat-gpt4m",
-        "gpt-4o": "chat-gpt4",
-        "claude-3.5-sonnet": "claude-sonnet",
-        "claude-3-haiku": "claude-haiku",
-        "llama-3.1-70b": "llama-3-70b",
-        "llama-3.1-8b": "llama-3-8b",
-        "o1-mini": "chat-o1-mini",
-        # Image model aliases
-        "sd-1.5": "sd",
-        "sd-3.5": "sd35",
-        "flux-schnell": "flux1",
-    }
-
-    @classmethod
-    def get_model(cls, model: str) -> str:
-        if model in cls.models:
-            return model
-        elif model in cls.model_aliases:
-            return cls.model_aliases[model]
-        else:
-            return cls.default_model
-
-    @classmethod
-    def is_image_model(cls, model: str) -> bool:
-        return model in cls.image_models
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: Messages,
-        proxy: str = None,
-        **kwargs
-    ) -> AsyncResult:
-        model = cls.get_model(model)
-
-        headers = {
-            'Accept': 'application/json, text/plain, */*',
-            'Accept-Language': 'en-US,en;q=0.9',
-            'Cache-Control': 'no-cache',
-            'Connection': 'keep-alive',
-            'Content-Type': 'application/json',
-            'Origin': 'https://app.giz.ai',
-            'Pragma': 'no-cache',
-            'Sec-Fetch-Dest': 'empty',
-            'Sec-Fetch-Mode': 'cors',
-            'Sec-Fetch-Site': 'same-origin',
-            'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36',
-            'sec-ch-ua': '"Not?A_Brand";v="99", "Chromium";v="130"',
-            'sec-ch-ua-mobile': '?0',
-            'sec-ch-ua-platform': '"Linux"'
-        }
-
-        async with ClientSession() as session:
-            if cls.is_image_model(model):
-                # Image generation
-                prompt = messages[-1]["content"]
-                data = {
-                    "model": model,
-                    "input": {
-                        "width": "1024",
-                        "height": "1024",
-                        "steps": 4,
-                        "output_format": "webp",
-                        "batch_size": 1,
-                        "mode": "plan",
-                        "prompt": prompt
-                    }
-                }
-                async with session.post(
-                    cls.api_endpoint,
-                    headers=headers,
-                    data=json.dumps(data),
-                    proxy=proxy
-                ) as response:
-                    response.raise_for_status()
-                    response_data = await response.json()
-                    if response_data.get('status') == 'completed' and response_data.get('output'):
-                        for url in response_data['output']:
-                            yield ImageResponse(images=url, alt="Generated Image")
-            else:
-                # Chat completion
-                data = {
-                    "model": model,
-                    "input": {
-                        "messages": [
-                            {
-                                "type": "human",
-                                "content": format_prompt(messages)
-                            }
-                        ],
-                        "mode": "plan"
-                    },
-                    "noStream": True
-                }
-                async with session.post(
-                    cls.api_endpoint,
-                    headers=headers,
-                    data=json.dumps(data),
-                    proxy=proxy
-                ) as response:
-                    response.raise_for_status()
-                    result = await response.json()
-                    yield result.get('output', '')

api/providers/blackboxai.py

@@ -1,215 +0,0 @@
-# api/providers/blackboxai.py
-
-from __future__ import annotations
-
-import json
-from datetime import datetime
-import uuid
-from typing import Any, Dict, Optional
-
-import httpx
-from api.config import (
-    MODEL_MAPPING,
-    headers,
-    BASE_URL,
-    MODEL_PREFIXES,
-    MODEL_REFERERS,
-)
-from api.models import ChatRequest
-from api.logger import setup_logger
-from api.image import ImageResponse  # Assuming similar structure to GizAI
-from api.typing import AsyncResult, Messages
-from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
-
-logger = setup_logger(__name__)
-
-class BlackBoxAI(AsyncGeneratorProvider, ProviderModelMixin):
-    url = "https://www.blackbox.ai"
-    api_endpoint = "https://www.blackbox.ai/api/chat"
-    working = True
-
-    supports_system_message = True
-    supports_message_history = True
-
-    # Define BlackBoxAI models
-    default_model = 'blackboxai'
-    chat_models = [
-        'blackboxai',
-        'blackboxai-pro',
-        'flux',
-        'llama-3.1-8b',
-        'llama-3.1-70b',
-        'llama-3.1-405b',
-        'gpt-4o',
-        'gemini-pro',
-        'gemini-1.5-flash',
-        'claude-sonnet-3.5',
-        'PythonAgent',
-        'JavaAgent',
-        'JavaScriptAgent',
-        'HTMLAgent',
-        'GoogleCloudAgent',
-        'AndroidDeveloper',
-        'SwiftDeveloper',
-        'Next.jsAgent',
-        'MongoDBAgent',
-        'PyTorchAgent',
-        'ReactAgent',
-        'XcodeAgent',
-        'AngularJSAgent',
-        'RepoMap',
-        'gemini-1.5-pro-latest',
-        'gemini-1.5-pro',
-        'claude-3-5-sonnet-20240620',
-        'claude-3-5-sonnet',
-        'Niansuh',
-    ]
-
-    image_models = []  # Add image models if applicable
-
-    models = chat_models + image_models
-
-    model_aliases = {
-        # Add aliases if any
-    }
-
-    @classmethod
-    def get_model(cls, model: str) -> str:
-        return MODEL_MAPPING.get(model, cls.default_model)
-
-    @classmethod
-    def is_image_model(cls, model: str) -> bool:
-        return model in cls.image_models
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: Messages,
-        proxy: str = None,
-        **kwargs
-    ) -> AsyncResult:
-        model = cls.get_model(model)
-        model_prefix = MODEL_PREFIXES.get(model, "")
-        referer_path = MODEL_REFERERS.get(model, f"/?model={model}")
-        referer_url = f"{BASE_URL}{referer_path}"
-
-        # Update headers with dynamic Referer
-        dynamic_headers = headers.copy()
-        dynamic_headers['Referer'] = referer_url
-
-        json_data = {
-            "messages": [cls.message_to_dict(msg, model_prefix) for msg in messages],
-            "stream": kwargs.get('stream', False),
-            "temperature": kwargs.get('temperature', 0.7),
-            "top_p": kwargs.get('top_p', 0.9),
-            "max_tokens": kwargs.get('max_tokens', 99999999),
-        }
-
-        async with httpx.AsyncClient() as client:
-            try:
-                if json_data.get("stream"):
-                    async with client.stream(
-                        "POST",
-                        cls.api_endpoint,
-                        headers=dynamic_headers,
-                        json=json_data,
-                        timeout=100,
-                    ) as response:
-                        response.raise_for_status()
-                        async for line in response.aiter_lines():
-                            timestamp = int(datetime.now().timestamp())
-                            if line:
-                                content = line
-                                if content.startswith("$@$v=undefined-rv1$@$"):
-                                    content = content[21:]
-                                # Strip the model prefix from the response content
-                                cleaned_content = cls.strip_model_prefix(content, model_prefix)
-                                yield f"data: {json.dumps(cls.create_chat_completion_data(cleaned_content, model, timestamp))}\n\n"
-
-                        yield f"data: {json.dumps(cls.create_chat_completion_data('', model, timestamp, 'stop'))}\n\n"
-                        yield "data: [DONE]\n\n"
-                else:
-                    response = await client.post(
-                        cls.api_endpoint,
-                        headers=dynamic_headers,
-                        json=json_data,
-                        timeout=100,
-                    )
-                    response.raise_for_status()
-                    full_response = response.text
-                    if full_response.startswith("$@$v=undefined-rv1$@$"):
-                        full_response = full_response[21:]
-                    # Strip the model prefix from the full response
-                    cleaned_full_response = cls.strip_model_prefix(full_response, model_prefix)
-                    return {
-                        "id": f"chatcmpl-{uuid.uuid4()}",
-                        "object": "chat.completion",
-                        "created": int(datetime.now().timestamp()),
-                        "model": model,
-                        "choices": [
-                            {
-                                "index": 0,
-                                "message": {"role": "assistant", "content": cleaned_full_response},
-                                "finish_reason": "stop",
-                            }
-                        ],
-                        "usage": None,
-                    }
-            except httpx.HTTPStatusError as e:
-                logger.error(f"HTTP error occurred: {e}")
-                raise HTTPException(status_code=e.response.status_code, detail=str(e))
-            except httpx.RequestError as e:
-                logger.error(f"Error occurred during request: {e}")
-                raise HTTPException(status_code=500, detail=str(e))
-
-    @staticmethod
-    def message_to_dict(message, model_prefix: Optional[str] = None):
-        if isinstance(message["content"], str):
-            content = message["content"]
-            if model_prefix:
-                content = f"{model_prefix} {content}"
-            return {"role": message["role"], "content": content}
-        elif isinstance(message["content"], list) and len(message["content"]) == 2:
-            content = message["content"][0]["text"]
-            if model_prefix:
-                content = f"{model_prefix} {content}"
-            return {
-                "role": message["role"],
-                "content": content,
-                "data": {
-                    "imageBase64": message["content"][1]["image_url"]["url"],
-                    "fileText": "",
-                    "title": "snapshot",
-                },
-            }
-        else:
-            return {"role": message["role"], "content": message["content"]}
-
-    @staticmethod
-    def strip_model_prefix(content: str, model_prefix: Optional[str] = None) -> str:
-        """Remove the model prefix from the response content if present."""
-        if model_prefix and content.startswith(model_prefix):
-            logger.debug(f"Stripping prefix '{model_prefix}' from content.")
-            return content[len(model_prefix):].strip()
-        logger.debug("No prefix to strip from content.")
-        return content
-
-    @staticmethod
-    def create_chat_completion_data(
-        content: str, model: str, timestamp: int, finish_reason: Optional[str] = None
-    ) -> Dict[str, Any]:
-        return {
-            "id": f"chatcmpl-{uuid.uuid4()}",
-            "object": "chat.completion.chunk",
-            "created": timestamp,
-            "model": model,
-            "choices": [
-                {
-                    "index": 0,
-                    "delta": {"content": content, "role": "assistant"},
-                    "finish_reason": finish_reason,
-                }
-            ],
-            "usage": None,
-        }

api/routes.py

@@ -1,59 +0,0 @@
-import json
-from fastapi import APIRouter, Depends, HTTPException, Request, Response
-from fastapi.responses import StreamingResponse
-from api.auth import verify_app_secret
-from api.config import ALLOWED_MODELS
-from api.models import ChatRequest
-from api.utils import process_non_streaming_response, process_streaming_response
-from api.logger import setup_logger
-
-logger = setup_logger(__name__)
-
-router = APIRouter()
-
-@router.options("/v1/chat/completions")
-@router.options("/api/v1/chat/completions")
-async def chat_completions_options():
-    return Response(
-        status_code=200,
-        headers={
-            "Access-Control-Allow-Origin": "*",
-            "Access-Control-Allow-Methods": "POST, OPTIONS",
-            "Access-Control-Allow-Headers": "Content-Type, Authorization",
-        },
-    )
-
-@router.get("/v1/models")
-@router.get("/api/v1/models")
-async def list_models():
-    return {"object": "list", "data": ALLOWED_MODELS}
-
-@router.post("/v1/chat/completions")
-@router.post("/api/v1/chat/completions")
-async def chat_completions(
-    request: ChatRequest, app_secret: str = Depends(verify_app_secret)
-):
-    logger.info("Entering chat_completions route")
-    logger.info(f"Processing chat completion request for model: {request.model}")
-
-    if request.model not in [model["id"] for model in ALLOWED_MODELS]:
-        raise HTTPException(
-            status_code=400,
-            detail=f"Model {request.model} is not allowed. Allowed models are: {', '.join(model['id'] for model in ALLOWED_MODELS)}",
-        )
-
-    if request.stream:
-        logger.info("Streaming response")
-        return StreamingResponse(process_streaming_response(request), media_type="text/event-stream")
-    else:
-        logger.info("Non-streaming response")
-        return await process_non_streaming_response(request)
-
-@router.route('/')
-@router.route('/healthz')
-@router.route('/ready')
-@router.route('/alive')
-@router.route('/status')
-@router.get("/health")
-def health_check(request: Request):
-    return Response(content=json.dumps({"status": "ok"}), media_type="application/json")

api/utils.py

@@ -1,198 +0,0 @@
-from datetime import datetime
-import json
-from typing import Any, Dict, Optional
-
-import httpx
-from api.config import (
-    MODEL_MAPPING,
-    headers,
-    AGENT_MODE,
-    TRENDING_AGENT_MODE,
-    BASE_URL,
-    MODEL_PREFIXES,
-    MODEL_REFERERS
-)
-from fastapi import HTTPException
-from api.models import ChatRequest
-
-from api.logger import setup_logger
-
-import uuid  # Added import for uuid
-
-logger = setup_logger(__name__)
-
-def create_chat_completion_data(
-    content: str, model: str, timestamp: int, finish_reason: Optional[str] = None
-) -> Dict[str, Any]:
-    return {
-        "id": f"chatcmpl-{uuid.uuid4()}",
-        "object": "chat.completion.chunk",
-        "created": timestamp,
-        "model": model,
-        "choices": [
-            {
-                "index": 0,
-                "delta": {"content": content, "role": "assistant"},
-                "finish_reason": finish_reason,
-            }
-        ],
-        "usage": None,
-    }
-
-def message_to_dict(message, model_prefix: Optional[str] = None):
-    if isinstance(message.content, str):
-        content = message.content
-        if model_prefix:
-            content = f"{model_prefix} {content}"
-        return {"role": message.role, "content": content}
-    elif isinstance(message.content, list) and len(message.content) == 2:
-        content = message.content[0]["text"]
-        if model_prefix:
-            content = f"{model_prefix} {content}"
-        return {
-            "role": message.role,
-            "content": content,
-            "data": {
-                "imageBase64": message.content[1]["image_url"]["url"],
-                "fileText": "",
-                "title": "snapshot",
-            },
-        }
-    else:
-        return {"role": message.role, "content": message.content}
-
-def strip_model_prefix(content: str, model_prefix: str) -> str:
-    """Remove the model prefix from the response content if present."""
-    if content.startswith(model_prefix):
-        return content[len(model_prefix):].strip()
-    return content
-
-async def process_streaming_response(request: ChatRequest):
-    agent_mode = AGENT_MODE.get(request.model, {})
-    trending_agent_mode = TRENDING_AGENT_MODE.get(request.model, {})
-    model_prefix = MODEL_PREFIXES.get(request.model, "")
-    referer_path = MODEL_REFERERS.get(request.model, f"/?model={request.model}")
-    referer_url = f"{BASE_URL}{referer_path}"
-
-    # Update headers with dynamic Referer
-    dynamic_headers = headers.copy()
-    dynamic_headers['Referer'] = referer_url
-
-    json_data = {
-        "messages": [message_to_dict(msg, model_prefix=model_prefix) for msg in request.messages],
-        "previewToken": None,
-        "userId": None,
-        "codeModelMode": True,
-        "agentMode": agent_mode,
-        "trendingAgentMode": trending_agent_mode,
-        "isMicMode": False,
-        "userSystemPrompt": None,
-        "maxTokens": request.max_tokens,
-        "playgroundTopP": request.top_p,
-        "playgroundTemperature": request.temperature,
-        "isChromeExt": False,
-        "githubToken": None,
-        "clickedAnswer2": False,
-        "clickedAnswer3": False,
-        "clickedForceWebSearch": False,
-        "visitFromDelta": False,
-        "mobileClient": False,
-        "userSelectedModel": MODEL_MAPPING.get(request.model, request.model),
-    }
-
-    async with httpx.AsyncClient() as client:
-        try:
-            async with client.stream(
-                "POST",
-                f"{BASE_URL}/api/chat",
-                headers=dynamic_headers,
-                json=json_data,
-                timeout=100,
-            ) as response:
-                response.raise_for_status()
-                async for line in response.aiter_lines():
-                    timestamp = int(datetime.now().timestamp())
-                    if line:
-                        content = line
-                        if content.startswith("$@$v=undefined-rv1$@$"):
-                            content = content[21:]
-                        # Strip the model prefix from the response content
-                        cleaned_content = strip_model_prefix(content, model_prefix)
-                        yield f"data: {json.dumps(create_chat_completion_data(cleaned_content, request.model, timestamp))}\n\n"
-
-                yield f"data: {json.dumps(create_chat_completion_data('', request.model, timestamp, 'stop'))}\n\n"
-                yield "data: [DONE]\n\n"
-        except httpx.HTTPStatusError as e:
-            logger.error(f"HTTP error occurred: {e}")
-            raise HTTPException(status_code=e.response.status_code, detail=str(e))
-        except httpx.RequestError as e:
-            logger.error(f"Error occurred during request: {e}")
-            raise HTTPException(status_code=500, detail=str(e))
-
-async def process_non_streaming_response(request: ChatRequest):
-    agent_mode = AGENT_MODE.get(request.model, {})
-    trending_agent_mode = TRENDING_AGENT_MODE.get(request.model, {})
-    model_prefix = MODEL_PREFIXES.get(request.model, "")
-    referer_path = MODEL_REFERERS.get(request.model, f"/?model={request.model}")
-    referer_url = f"{BASE_URL}{referer_path}"
-
-    # Update headers with dynamic Referer
-    dynamic_headers = headers.copy()
-    dynamic_headers['Referer'] = referer_url
-
-    json_data = {
-        "messages": [message_to_dict(msg, model_prefix=model_prefix) for msg in request.messages],
-        "previewToken": None,
-        "userId": None,
-        "codeModelMode": True,
-        "agentMode": agent_mode,
-        "trendingAgentMode": trending_agent_mode,
-        "isMicMode": False,
-        "userSystemPrompt": None,
-        "maxTokens": request.max_tokens,
-        "playgroundTopP": request.top_p,
-        "playgroundTemperature": request.temperature,
-        "isChromeExt": False,
-        "githubToken": None,
-        "clickedAnswer2": False,
-        "clickedAnswer3": False,
-        "clickedForceWebSearch": False,
-        "visitFromDelta": False,
-        "mobileClient": False,
-        "userSelectedModel": MODEL_MAPPING.get(request.model, request.model),
-    }
-    full_response = ""
-    async with httpx.AsyncClient() as client:
-        try:
-            async with client.stream(
-                method="POST", url=f"{BASE_URL}/api/chat", headers=dynamic_headers, json=json_data
-            ) as response:
-                response.raise_for_status()
-                async for chunk in response.aiter_text():
-                    full_response += chunk
-        except httpx.HTTPStatusError as e:
-            logger.error(f"HTTP error occurred: {e}")
-            raise HTTPException(status_code=e.response.status_code, detail=str(e))
-        except httpx.RequestError as e:
-            logger.error(f"Error occurred during request: {e}")
-            raise HTTPException(status_code=500, detail=str(e))
-    if full_response.startswith("$@$v=undefined-rv1$@$"):
-        full_response = full_response[21:]
-
-    # Strip the model prefix from the full response
-    cleaned_full_response = strip_model_prefix(full_response, model_prefix)
-
-    return {
-        "id": f"chatcmpl-{uuid.uuid4()}",
-        "object": "chat.completion",
-        "created": int(datetime.now().timestamp()),
-        "model": request.model,
-        "choices": [
-            {
-                "index": 0,
-                "message": {"role": "assistant", "content": cleaned_full_response},
-                "finish_reason": "stop",
-            }
-        ],
-        "usage": None,
-    }