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_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")

            # 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")

            # Button actions
            submit.click(fn=self.generate_response, inputs=[user_input, chatbot_output], outputs=chatbot_output)
            user_input.submit(fn=self.generate_response, inputs=[user_input, chatbot_output], outputs=chatbot_output)
            reset.click(fn=self.reset_output, inputs=None, outputs=chatbot_output)

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