app.py

import os
import sys
import re
import gradio as gr
import json
import tempfile
import base64
import io
from typing import List, Dict, Any, Optional, Tuple, Union
import logging
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from api import app as flask_app

# ... (resto del código existente sin cambios) ...

def create_application():
    """Create and configure the Gradio application."""
    # Create the UI components
    demo, chatbot, chart_display, question_input, submit_button, streaming_output_display = create_ui()
    
    # Montar la API Flask en la aplicación Gradio
    if os.getenv('SPACE_ID'):
        demo = gr.mount_gradio_app(
            flask_app,
            "/api",  # Prefijo para los endpoints de la API
            lambda: True  # Autenticación deshabilitada
        )
    
    def user_message(user_input: str, chat_history: List[Dict[str, str]]) -> Tuple[str, List[Dict[str, str]]]:
        """Add user message to chat history (messages format) and clear input."""
        if not user_input.strip():
            return "", chat_history

        logger.info(f"User message: {user_input}")

        if chat_history is None:
            chat_history = []

        # Append user message in messages format
        chat_history.append({"role": "user", "content": user_input})

        return "", chat_history
    
    async def bot_response(chat_history: List[Dict[str, str]]) -> Tuple[List[Dict[str, str]], Optional[go.Figure]]:
        """Generate bot response for messages-format chat history and return optional chart figure."""
        if not chat_history:
            return chat_history, None

        # Ensure last message is a user turn awaiting assistant reply
        last = chat_history[-1]
        if not isinstance(last, dict) or last.get("role") != "user" or not last.get("content"):
            return chat_history, None

        try:
            question = last["content"]
            logger.info(f"Processing question: {question}")

            # Convert prior messages to pair history for stream_agent_response()
            pair_history: List[List[str]] = []
            i = 0
            while i < len(chat_history) - 1:
                m1 = chat_history[i]
                m2 = chat_history[i + 1] if i + 1 < len(chat_history) else None
                if (
                    isinstance(m1, dict)
                    and m1.get("role") == "user"
                    and isinstance(m2, dict)
                    and m2.get("role") == "assistant"
                ):
                    pair_history.append([m1.get("content", ""), m2.get("content", "")])
                    i += 2
                else:
                    i += 1

            # Call the agent for this new user question
            assistant_message, chart_fig = await stream_agent_response(question, pair_history)

            # Append assistant message back into messages history
            chat_history.append({"role": "assistant", "content": assistant_message})

            logger.info("Response generation complete")
            return chat_history, chart_fig

        except Exception as e:
            error_msg = f"## ❌ Error\n\nError al procesar la solicitud:\n\n```\n{str(e)}\n```"
            logger.error(error_msg, exc_info=True)
            # Ensure we add an assistant error message for the UI
            chat_history.append({"role": "assistant", "content": error_msg})
            return chat_history, None
    
    # Event handlers
    with demo:
        # Handle form submission
        msg_submit = question_input.submit(
            fn=user_message,
            inputs=[question_input, chatbot],
            outputs=[question_input, chatbot],
            queue=True
        ).then(
            fn=bot_response,
            inputs=[chatbot],
            outputs=[chatbot, chart_display],
            api_name="ask"
        )
        
        # Handle button click
        btn_click = submit_button.click(
            fn=user_message,
            inputs=[question_input, chatbot],
            outputs=[question_input, chatbot],
            queue=True
        ).then(
            fn=bot_response,
            inputs=[chatbot],
            outputs=[chatbot, chart_display]
        )
    
    return demo

# Create the application
demo = create_application()

# Configuración para Hugging Face Spaces
def get_app():
    """Obtiene la instancia de la aplicación Gradio para Hugging Face Spaces."""
    # Verificar si estamos en un entorno de Hugging Face Spaces
    if os.getenv('SPACE_ID'):
        # Configuración específica para Spaces
        demo.title = "🤖 Asistente de Base de Datos SQL (Demo)"
        demo.description = """
        Este es un demo del asistente de base de datos SQL. 
        Para usar la versión completa con conexión a base de datos, clona este espacio y configura las variables de entorno.
        """
    
    return demo

# Para desarrollo local
if __name__ == "__main__":
    # Configuración para desarrollo local - versión simplificada para Gradio 5.x
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        debug=True,
        share=False
    )