utils/session_history2.py

import os
import openai
import logging
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 reset_conversation(self):
        """
        Reset the conversation state internally maintained by OpenAI Response API.
        """
        self.previous_response_id = None

    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) -> list:
        """
        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:
            list: A list of dictionaries representing the conversation, including the generated response.
        """
        # 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})
            return history + input_data

        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})
            return history + input_data

        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})
            return history + input_data
            
import os
import json
import logging
from typing import Optional
import gradio as gr
from utils.response_manager import ResponseManager 

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_placeholder = self.config["chatbot_input_placeholder"]
        self.output_label = self.config["chatbot_output_label"]

        # Parameters for ResponseManager class 
        self.model = model
        self.temperature = temperature
        self.max_output_tokens = max_output_tokens
        self.max_num_results = max_num_results
        self.vector_store_id = vector_store_id
        self.api_key = api_key
        self.meta_prompt_file = meta_prompt_file
        
           
    @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_placeholder", 
            "chatbot_output_label"
        ]
        
        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 create_interface(self) -> gr.Blocks:
        """
        Create the Gradio Blocks interface that displays a single container including both
        the text input and a small arrow submit button. The interface will clear the text input
        after each message is submitted.
        """
        logging.info("Creating Gradio interface...")

        with gr.Blocks() as demo: 
            # Title and description area.
            gr.Markdown(f"## {self.title}\n{self.description}")

            # Chatbot output area.
            chatbot_output = gr.Chatbot(label=self.output_label, type="messages")

            # # Session-specific state to store conversation history.
            # conversation_state = gr.State([])

            # Session-specific states
            conversation_state = gr.State([])
            response_manager_state = gr.State(None)

            # Use a gr.Row container as the input box with an integrated submit button.
            with gr.Row(elem_id="input-container", equal_height=True):
                user_input = gr.Textbox(
                    lines=1,
                    show_label=False,              # Hide label for a unified look.
                    elem_id="chat-input",
                    placeholder=self.input_placeholder,
                    scale=500,
                )
                reset = gr.ClearButton(
                    value="Reset 🔄",  
                    variant="secondary",
                    elem_id="reset-button",
                    size="lg"
                )

            # 🟢 Initialization function for session-specific response manager
            def init_response_manager():
                try:
                    rm = ResponseManager(
                            model=self.model,
                            temperature=self.temperature,
                            max_output_tokens=self.max_output_tokens,
                            max_num_results=self.max_num_results,
                            vector_store_id=self.vector_store_id,
                            api_key=self.api_key,
                            meta_prompt_file=self.meta_prompt_file
                        )
                
                    logging.info(
                        "ChatbotInterface initialized with the following parameters:\n"
                        f"  - Model: {self.model}\n"
                        f"  - Temperature: {self.temperature}\n"
                        f"  - Max Output Tokens: {self.max_output_tokens}\n"
                        f"  - Max Number of Results: {self.max_num_results}\n"
                    )
                    
                    rm.reset_conversation()
                    return rm
                except Exception as e:
                    logging.error(f"Failed to initialize ResponseManager: {e}")
                    raise

            # 🟢 Reset function updated to reset ResponseManager
            def reset_output():
                response_manager = init_response_manager()
                return [], response_manager, ""
    
            # 🟢 Process input now uses session-specific ResponseManager
            def process_input(user_message, chat_history, response_manager):
                updated_history = response_manager.generate_response(user_message, chat_history)
                return updated_history, updated_history, response_manager, ""
    
            # Initialize ResponseManager on load
            demo.load(
                fn=init_response_manager,
                inputs=None,
                outputs=response_manager_state
            )

            

            reset.click(
                fn=reset_output,
                inputs=None,
                outputs=[chatbot_output, response_manager_state, user_input]
            )
    
            user_input.submit(
                fn=process_input,
                inputs=[user_input, conversation_state, response_manager_state],
                outputs=[chatbot_output, conversation_state, response_manager_state, user_input]
            )

        logging.info("Gradio interface created successfully.")
        return demo