Create chatbot_interface3.py

utils/chatbot_interface3.py

@@ -0,0 +1,160 @@
+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
+            ) 
+            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 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")
+
+            # Use a gr.Row container as the input box with an integrated submit button.
+            with gr.Row(elem_id="input-container"):
+            user_input = gr.Textbox(
+                lines=2,
+                label=self.input_label,
+                placeholder=self.input_placeholder,
+                elem_id="chat-input"
+            )
+              
+            reset = gr.ClearButton(
+                value="🔄",  
+                variant="secondary",
+                elem_id="reset-button",
+                size="md"
+            )
+
+            # Define a local function to reset input  
+            def reset_output() -> list:
+                """
+                Reset the chatbot output.
+                :return: An empty list to reset the output.
+                """
+                return [], ""
+                
+            # Define a local function to process input
+            def process_input(user_message, chat_history):
+                """
+                Call generate_response with the user's message and chat history.
+                Return a tuple with the updated chat history and an empty string to clear the input.
+                """
+                updated_history = self.response_manager.generate_response(user_message, chat_history)
+                return updated_history, ""
+
+            
+            # Bind the reset button click to the reset function
+            reset.click(
+                fn=reset_output,
+                inputs=None,
+                outputs=[chatbot_output, user_input]
+            )
+            
+            
+            # Bind the Enter key (textbox submit) to the same processing function
+            user_input.submit(
+                fn=process_input,
+                inputs=[user_input, chatbot_output],
+                outputs=[chatbot_output, user_input]
+            )
+
+        logging.info("Gradio interface created successfully.")
+        return demo