utils/chatbot_interface.py

import os
import json
import logging
from typing import Optional
import asyncio
import gradio as gr
from utils.response_manager import ResponseManager 

class ChatbotInterface:
    def __init__(self, 
                 model: str = "gpt-4.1-nano",
                 temperature: float = 0,
                 max_output_tokens: int = 600,
                 max_num_results: int = 5,
                 vector_store_id: Optional[str] = None, 
                 api_key: Optional[str] = None, 
                 meta_prompt_file: Optional[str] = None,
                 config_path: str = 'config/gradio_config.json'
                ):
        """
        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 .
        """
        # Parameters for UI
        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}")
                
        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 states
            conversation_state = gr.State([])
            response_manager_state = gr.State(None)

            # Row area.
            with gr.Row(elem_id="input-container", equal_height=True):
                reset = gr.ClearButton(
                    value="Clear history 🔄",  
                    variant="secondary",
                    elem_id="reset-button",
                    size="lg"
                )
                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,
                )
                

            # 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, "" # Returns [chatbot_output,conversation_state, response_manager_state, user_input]
    
            # Process input now uses session-specific ResponseManager
            async def process_input(user_message, chat_history, response_manager):
                updated_history = await response_manager.generate_response(user_message, chat_history)
                return updated_history, updated_history, response_manager, "" # Returns [chatbot_output, conversation_state, response_manager_state, user_input]
    
            # Initialize ResponseManager object for a session on load
            demo.load(
                fn=init_response_manager,
                inputs=None,
                outputs=response_manager_state # Each session state gets its own instance of ResponseManager class
            )

            # CLearButton action
            reset.click(
                fn=reset_output,
                inputs=None,
                outputs=[chatbot_output, conversation_state, response_manager_state, user_input]
            )

            # Enter to trigger response generation
            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