Delete utils/session_history.py

utils/session_history.py

@@ -1,249 +0,0 @@
-import os
-import openai
-import logging
-import json
-import gradio as gr
-from typing import Optional
-
-class ResponseManager:
-    """
-    This class initializes the OpenAI client and provides methods to create responses,
-    maintain conversation history, and handle user queries.
-    """
-    def __init__(self, 
-                 vector_store_id: Optional[str] = None, 
-                 api_key: Optional[str] = None, 
-                 meta_prompt_file: Optional[str] = None,
-                 model: str = "gpt-4o-mini",
-                 temperature: float = 0,
-                 max_output_tokens: int = 800,
-                 max_num_results: int = 15):
-        """
-        Initialize the ResponseManager with optional parameters for configuration.
-        :param vector_store_id: The ID of the vector store to use for file search.
-        :param api_key: The OpenAI API key for authentication.
-        :param meta_prompt_file: Path to the meta prompt file (default: 'config/meta_prompt.txt').
-        :param model: The OpenAI model to use (default: 'gpt-4o-mini').
-        :param temperature: The temperature for response generation (default: 0).
-        :param max_output_tokens: The maximum number of output tokens (default: 800).
-        :param max_num_results: The maximum number of search results to return (default: 15).
-        """
-        # Load vector_store_id and api_key from environment variables if not provided
-        self.vector_store_id = vector_store_id or os.getenv('VECTOR_STORE_ID')
-        if not self.vector_store_id:
-            logging.error("VECTOR_STORE_ID is not provided or set in the environment.")
-            raise ValueError("VECTOR_STORE_ID is required.")
-    
-        self.api_key = api_key or os.getenv('OPENAI_API_KEY')
-        if not self.api_key:
-            logging.error("OPENAI_API_KEY is not provided or set in the environment.")
-            raise ValueError("OPENAI_API_KEY is required.")
-    
-        # Initialize other attributes
-        self.meta_prompt_file = meta_prompt_file or 'config/meta_prompt.txt'
-        self.previous_response_id = None
-    
-        # Initialize the OpenAI client
-        self.client = openai.OpenAI(api_key=self.api_key)
-    
-        # Load the meta prompt from the specified file
-        self.meta_prompt = self._load_meta_prompt(self.meta_prompt_file)
-    
-        # Set default parameters for response generation
-        self.model = model
-        self.temperature = temperature
-        self.max_output_tokens = max_output_tokens
-        self.max_num_results = max_num_results
-    
-    def _load_meta_prompt(self, meta_prompt_file: str) -> str:
-        """
-        Load the meta prompt from the specified file.
-        :param meta_prompt_file: Path to the meta prompt file.
-        :return: The meta prompt as a string.
-        """
-        if not os.path.exists(meta_prompt_file):
-            logging.error(f"Meta prompt file '{meta_prompt_file}' not found.")
-            raise FileNotFoundError(f"Meta prompt file '{meta_prompt_file}' not found.")
-        with open(meta_prompt_file, 'r', encoding='utf-8') as file:
-            meta_prompt = file.read().strip()
-        logging.info(f"Meta prompt loaded successfully from '{meta_prompt_file}'.")
-        return meta_prompt
-    
-    def generate_response(self, query: str, history: list) -> tuple:
-        """
-        Generate a response to a user query using the OpenAI API.
-        This method interacts with the OpenAI API to create a response based on the user's query.
-        It supports optional parameters for model configuration and handles errors gracefully.
-        Args:
-            query (str): The user query to respond to.
-            history (list): The conversation history from the chatbot.
-        Returns:
-            tuple: (updated conversation list for display, updated conversation list for state)
-        """
-        # Prepare the input for the API call
-        input_data = [{"role": "developer", "content": self.meta_prompt}] if self.previous_response_id is None else []
-        input_data.append({"role": "user", "content": query})
-        
-        # Validate the query
-        if not query.strip():
-            logging.warning("Empty or invalid query received.")
-            warning_message = "Please enter a valid query."
-            input_data.append({"role": "assistant", "content": warning_message})
-            new_history = history + input_data
-            return new_history, new_history
-    
-        try:
-            logging.info("Sending request to OpenAI API...")
-            response = self.client.responses.create(
-                model=self.model,
-                previous_response_id=self.previous_response_id,
-                input=input_data,
-                tools=[{
-                    "type": "file_search",
-                    "vector_store_ids": [self.vector_store_id],
-                    "max_num_results": self.max_num_results
-                }],
-                truncation="auto",
-                temperature=self.temperature,
-                max_output_tokens=self.max_output_tokens
-            )
-            self.previous_response_id = response.id
-            logging.info("Response received successfully.")
-            input_data.append({"role": "assistant", "content": response.output_text})
-            new_history = history + input_data
-            return new_history, new_history
-        except Exception as e:
-            logging.error(f"An error occurred while generating a response: {e}")
-            error_message = "Sorry, I couldn't generate a response at this time. Please try again later."
-            input_data.append({"role": "assistant", "content": error_message})
-            new_history = history + input_data
-            return new_history, new_history
- 
-class ChatbotInterface:
-    def init(self,
-        config_path: str = 'config/gradio_config.json',
-        model: str = "gpt-4o-mini",
-        temperature: float = 0,
-        max_output_tokens: int = 800,
-        max_num_results: int = 15,
-        vector_store_id: Optional[str] = None,
-        api_key: Optional[str] = None,
-        meta_prompt_file: Optional[str] = None):
-        """
-        Initialize the ChatbotInterface with configuration and custom parameters for ResponseManager.
-        :param config_path: Path to the configuration JSON file.
-        :param model: The OpenAI model to use (default: 'gpt-4o-mini').
-        :param temperature: The temperature for response generation (default: 0).
-        :param max_output_tokens: The maximum number of output tokens (default: 800).
-        :param max_num_results: The maximum number of search results to return (default: 15).
-        :param vector_store_id: The ID of the vector store to use for file search.
-        :param api_key: The OpenAI API key for authentication.
-        :param meta_prompt_file: Path to the meta prompt file.
-        """
-        self.config = self.load_config(config_path)
-        self.title = self.config["chatbot_title"]
-        self.description = self.config["chatbot_description"]
-        self.input_label = self.config["chatbot_input_label"]
-        self.input_placeholder = self.config["chatbot_input_placeholder"]
-        self.output_label = self.config["chatbot_output_label"]
-        self.reset_button = self.config["chatbot_reset_button"]
-        self.submit_button = self.config["chatbot_submit_button"]
-        
-        # Initialize ResponseManager with custom parameters
-        try:
-            self.response_manager = ResponseManager(
-                model=model,
-                temperature=temperature,
-                max_output_tokens=max_output_tokens,
-                max_num_results=max_num_results,
-                vector_store_id=vector_store_id,
-                api_key=api_key,
-                meta_prompt_file=meta_prompt_file
-            )
-            self.generate_response = self.response_manager.generate_response  
-            logging.info(
-                "ChatbotInterface initialized with the following parameters:\n"
-                f"  - Model: {model}\n"
-                f"  - Temperature: {temperature}\n"
-                f"  - Max Output Tokens: {max_output_tokens}\n"
-                f"  - Max Number of Results: {max_num_results}\n"
-                f"  - Vector Store ID: {vector_store_id}\n"
-                f"  - API Key: {'Provided' if api_key else 'Not Provided'}\n"
-                f"  - Meta Prompt File: {meta_prompt_file or 'Default'}"
-            )
-        except Exception as e:
-            logging.error(f"Failed to initialize ResponseManager: {e}")
-            raise
-        
-           
-    @staticmethod
-    def load_config(config_path: str) -> dict:
-        """
-        Load the configuration for Gradio GUI interface from the JSON file.
-        :param config_path: Path to the configuration JSON file.
-        :return: Configuration dictionary.
-        """
-        logging.info(f"Loading configuration from {config_path}...")
-        if not os.path.exists(config_path):
-            logging.error(f"Configuration file not found: {config_path}")
-            raise FileNotFoundError(f"Configuration file not found: {config_path}")
-    
-        with open(config_path, 'r') as config_file:
-            config = json.load(config_file)
-    
-        required_keys = [
-            "chatbot_title", "chatbot_description", "chatbot_input_label",
-            "chatbot_input_placeholder", "chatbot_output_label",
-            "chatbot_reset_button", "chatbot_submit_button"
-        ]
-        for key in required_keys:
-            if key not in config:
-                logging.error(f"Missing required configuration key: {key}")
-                raise ValueError(f"Missing required configuration key: {key}")
-    
-        logging.info("Configuration loaded successfully.")
-        return config
-    
-    def reset_output(self) -> list:
-        """
-        Reset the chatbot output.
-        :return: An empty list to reset the output.
-        """
-        return []
-    
-    def create_interface(self) -> gr.Blocks:
-        """
-        Create the Gradio Blocks interface.
-        :return: A Gradio Blocks interface object.
-        """
-        logging.info("Creating Gradio interface...")
-    
-        # Define the Gradio Blocks interface
-        with gr.Blocks() as demo:
-            gr.Markdown(f"## {self.title}\n{self.description}")
-    
-            # Chatbot history component    
-            chatbot_output = gr.Chatbot(label=self.output_label, type="messages")
-            
-            # Adding a session-specific state to store conversation history.
-            conversation_state = gr.State([])
-    
-            # User input
-            user_input = gr.Textbox(
-                lines=2,
-                label=self.input_label,
-                placeholder=self.input_placeholder
-            )
-    
-            # Buttons
-            with gr.Row():
-                reset = gr.Button(self.reset_button, variant="secondary")
-                submit = gr.Button(self.submit_button, variant="primary")
-    
-            submit.click(fn=self.generate_response, inputs=[user_input, conversation_state], outputs=[chatbot_output, conversation_state])
-            user_input.submit(fn=self.generate_response, inputs=[user_input, conversation_state], outputs=[chatbot_output, conversation_state])
-            reset.click(fn=self.reset_output, inputs=None, outputs=chatbot_output)
-    
-    
-        logging.info("Gradio interface created successfully.")
-        return demo