app.py

import os
import sys
import gradio as gr
import json
from typing import List, Dict, Any, Optional, Tuple
import logging

try:
    # Intentar importar dependencias opcionales
    from langchain_community.agent_toolkits import create_sql_agent
    from langchain_community.utilities import SQLDatabase
    from langchain_google_genai import ChatGoogleGenerativeAI
    from langchain.agents.agent_types import AgentType
    import pymysql
    from dotenv import load_dotenv
    
    DEPENDENCIES_AVAILABLE = True
except ImportError:
    # Si faltan dependencias, la aplicación funcionará en modo demo
    DEPENDENCIES_AVAILABLE = False

# Configuración de logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def check_environment():
    """Verifica si el entorno está configurado correctamente."""
    if not DEPENDENCIES_AVAILABLE:
        return False, "Missing required Python packages. Please install them with: pip install -r requirements.txt"
    
    # Verificar si estamos en un entorno con variables de entorno
    required_vars = ["DB_USER", "DB_PASSWORD", "DB_HOST", "DB_NAME", "GOOGLE_API_KEY"]
    missing_vars = [var for var in required_vars if not os.getenv(var)]
    
    if missing_vars:
        return False, f"Missing required environment variables: {', '.join(missing_vars)}"
    
    return True, "Environment is properly configured"

def setup_database_connection():
    """Intenta establecer una conexión a la base de datos."""
    if not DEPENDENCIES_AVAILABLE:
        return None, "Dependencies not available"
        
    try:
        load_dotenv(override=True)
        
        # Debug: Log all environment variables (without sensitive values)
        logger.info("Environment variables:")
        for key, value in os.environ.items():
            if any(s in key.lower() for s in ['pass', 'key', 'secret']):
                logger.info(f"  {key}: {'*' * 8} (hidden for security)")
            else:
                logger.info(f"  {key}: {value}")
        
        db_user = os.getenv("DB_USER")
        db_password = os.getenv("DB_PASSWORD")
        db_host = os.getenv("DB_HOST")
        db_name = os.getenv("DB_NAME")
        
        # Debug: Log database connection info (without password)
        logger.info(f"Database connection attempt - Host: {db_host}, User: {db_user}, DB: {db_name}")
        if not all([db_user, db_password, db_host, db_name]):
            missing = [var for var, val in [
                ("DB_USER", db_user),
                ("DB_PASSWORD", "*" if db_password else ""),
                ("DB_HOST", db_host),
                ("DB_NAME", db_name)
            ] if not val]
            logger.error(f"Missing required database configuration: {', '.join(missing)}")
            return None, f"Missing database configuration: {', '.join(missing)}"
        
        if not all([db_user, db_password, db_host, db_name]):
            return None, "Missing database configuration"
            
        logger.info(f"Connecting to database: {db_user}@{db_host}/{db_name}")
        
        # Probar conexión
        connection = pymysql.connect(
            host=db_host,
            user=db_user,
            password=db_password,
            database=db_name,
            connect_timeout=5,
            cursorclass=pymysql.cursors.DictCursor
        )
        connection.close()
        
        # Si la conexión es exitosa, crear motor SQLAlchemy
        db_uri = f"mysql+pymysql://{db_user}:{db_password}@{db_host}/{db_name}"
        logger.info("Database connection successful")
        return SQLDatabase.from_uri(db_uri), ""
        
    except Exception as e:
        error_msg = f"Error connecting to database: {str(e)}"
        logger.error(error_msg)
        return None, error_msg

def initialize_llm():
    """Inicializa el modelo de lenguaje."""
    if not DEPENDENCIES_AVAILABLE:
        error_msg = "Dependencies not available. Make sure all required packages are installed."
        logger.error(error_msg)
        return None, error_msg
        
    google_api_key = os.getenv("GOOGLE_API_KEY")
    logger.info(f"GOOGLE_API_KEY found: {'Yes' if google_api_key else 'No'}")
    
    if not google_api_key:
        error_msg = "GOOGLE_API_KEY not found in environment variables. Please check your Hugging Face Space secrets."
        logger.error(error_msg)
        return None, error_msg
        
    try:
        logger.info("Initializing Google Generative AI...")
        llm = ChatGoogleGenerativeAI(
            model="gemini-2.0-flash",
            temperature=0,
            google_api_key=google_api_key
        )
        
        # Test the model with a simple prompt
        test_prompt = "Hello, this is a test."
        logger.info(f"Testing model with prompt: {test_prompt}")
        test_response = llm.invoke(test_prompt)
        logger.info(f"Model test response: {str(test_response)[:100]}...")  # Log first 100 chars
        
        logger.info("Google Generative AI initialized successfully")
        return llm, ""
        
    except Exception as e:
        error_msg = f"Error initializing Google Generative AI: {str(e)}"
        logger.error(error_msg, exc_info=True)  # Include full stack trace
        return None, error_msg

def create_agent():
    """Crea el agente SQL si es posible."""
    if not DEPENDENCIES_AVAILABLE:
        error_msg = "Dependencies not available. Please check if all required packages are installed."
        logger.error(error_msg)
        return None, error_msg
    
    logger.info("Starting agent creation process...")
    
    # Step 1: Set up database connection
    logger.info("Setting up database connection...")
    db, db_error = setup_database_connection()
    if not db:
        error_msg = f"Failed to connect to database: {db_error}"
        logger.error(error_msg)
    else:
        logger.info("Database connection successful")
    
    # Step 2: Initialize LLM
    logger.info("Initializing language model...")
    llm, llm_error = initialize_llm()
    if not llm:
        error_msg = f"Failed to initialize language model: {llm_error}"
        logger.error(error_msg)
    else:
        logger.info("Language model initialized successfully")
    
    # Check if both components are available
    if not db or not llm:
        error_msg = f"Cannot create agent. {db_error if not db else ''} {llm_error if not llm else ''}"
        logger.error(error_msg)
        return None, error_msg
    
    # Step 3: Create SQL agent
    try:
        logger.info("Creating SQL agent...")
        agent = create_sql_agent(
            llm=llm,
            db=db,
            agent_type=AgentType.OPENAI_FUNCTIONS,
            verbose=True
        )
        
        # Test the agent with a simple query
        try:
            logger.info("Testing agent with a simple query...")
            test_result = agent.invoke({"input": "What tables are available?"})
            logger.info(f"Agent test response: {str(test_result)[:200]}...")  # Log first 200 chars
        except Exception as test_error:
            logger.warning(f"Agent test query failed (this might be expected): {str(test_error)}")
        
        logger.info("SQL agent created and tested successfully")
        return agent, ""
        
    except Exception as e:
        error_msg = f"Error creating SQL agent: {str(e)}"
        logger.error(error_msg, exc_info=True)  # Include full stack trace
        return None, error_msg

# Inicializar el agente
logger.info("="*50)
logger.info("Starting application initialization...")
logger.info(f"Python version: {sys.version}")
logger.info(f"Current working directory: {os.getcwd()}")
logger.info(f"Files in working directory: {os.listdir('.')}")

# Check environment variables
logger.info("Checking environment variables...")
required_vars = ["DB_USER", "DB_HOST", "DB_NAME", "GOOGLE_API_KEY"]
for var in required_vars:
    logger.info(f"{var}: {'*' * 8 if os.getenv(var) else 'NOT SET'}")

# Initialize agent
logger.info("Initializing agent...")
agent, agent_error = create_agent()
db_connected = agent is not None

if agent:
    logger.info("Agent initialized successfully")
else:
    logger.error(f"Failed to initialize agent: {agent_error}")

logger.info("="*50)

def extract_sql_query(text):
    """Extrae consultas SQL del texto usando expresiones regulares."""
    if not text:
        return None
    
    # Buscar código SQL entre backticks
    sql_match = re.search(r'```(?:sql)?\s*(.*?)```', text, re.DOTALL)
    if sql_match:
        return sql_match.group(1).strip()
    
    # Si no hay backticks, buscar una consulta SQL simple
    sql_match = re.search(r'(SELECT|INSERT|UPDATE|DELETE|CREATE|ALTER|DROP|TRUNCATE).*?;', text, re.IGNORECASE | re.DOTALL)
    if sql_match:
        return sql_match.group(0).strip()
    
    return None

def execute_sql_query(query, db_connection):
    """Ejecuta una consulta SQL y devuelve los resultados como una cadena."""
    if not db_connection:
        return "Error: No hay conexión a la base de datos"
        
    try:
        with db_connection._engine.connect() as connection:
            result = connection.execute(query)
            rows = result.fetchall()
            
            # Convertir los resultados a un formato legible
            if not rows:
                return "La consulta no devolvió resultados"
                
            # Si es un solo resultado, devolverlo directamente
            if len(rows) == 1 and len(rows[0]) == 1:
                return str(rows[0][0])
                
            # Si hay múltiples filas, formatear como tabla
            try:
                import pandas as pd
                df = pd.DataFrame(rows)
                return df.to_markdown(index=False)
            except ImportError:
                # Si pandas no está disponible, usar formato simple
                return "\n".join([str(row) for row in rows])
                
    except Exception as e:
        return f"Error ejecutando la consulta: {str(e)}"

def generate_plot(data, x_col, y_col, title, x_label, y_label):
    """Generate a plot from data and return the file path."""
    plt.figure(figsize=(10, 6))
    plt.bar(data[x_col], data[y_col])
    plt.title(title)
    plt.xlabel(x_label)
    plt.ylabel(y_label)
    plt.xticks(rotation=45)
    plt.tight_layout()
    
    # Save to a temporary file
    temp_dir = tempfile.mkdtemp()
    plot_path = os.path.join(temp_dir, "plot.png")
    plt.savefig(plot_path)
    plt.close()
    
    return plot_path

def convert_to_messages_format(chat_history):
    """Convert chat history to the format expected by Gradio 5.x"""
    if not chat_history:
        return []
        
    messages = []
    
    # If the first element is a list, assume it's in the old format
    if isinstance(chat_history[0], list):
        for msg in chat_history:
            if isinstance(msg, list) and len(msg) == 2:
                # Format: [user_msg, bot_msg]
                user_msg, bot_msg = msg
                if user_msg:
                    messages.append({"role": "user", "content": user_msg})
                if bot_msg:
                    messages.append({"role": "assistant", "content": bot_msg})
    else:
        # Assume it's already in the correct format or can be used as is
        for msg in chat_history:
            if isinstance(msg, dict) and "role" in msg and "content" in msg:
                messages.append(msg)
            elif isinstance(msg, str):
                # If it's a string, assume it's a user message
                messages.append({"role": "user", "content": msg})
    
    return messages

async def stream_agent_response(question: str, chat_history: List) -> tuple:
    """Procesa la pregunta del usuario y devuelve la respuesta del agente."""
    # Initialize response
    response_text = ""
    messages = []
    
    # Add previous chat history
    if chat_history:
        messages.extend(chat_history)
    
    # Add user's question
    messages.append({"role": "user", "content": question})
    
    if not agent:
        error_msg = (
            "## ⚠️ Error: Agente no inicializado\n\n"
            "No se pudo inicializar el agente de base de datos. Por favor, verifica que:\n"
            "1. Todas las variables de entorno estén configuradas correctamente\n"
            "2. La base de datos esté accesible\n"
            f"3. El modelo de lenguaje esté disponible\n\n"
            f"Error: {agent_error}"
        )
        messages.append({"role": "assistant", "content": error_msg})
        yield messages
        return
    
    try:
        # Execute the agent
        response = await agent.ainvoke({"input": question, "chat_history": chat_history})
        
        # Process the response
        if hasattr(response, 'output'):
            response_text = response.output
            
            # Check if the response contains an SQL query
            sql_query = extract_sql_query(response_text)
            if sql_query:
                # Execute the query and update the response
                db_connection, _ = setup_database_connection()
                if db_connection:
                    query_result = execute_sql_query(sql_query, db_connection)
                    response_text += f"\n\n### 🔍 Resultado de la consulta:\n```sql\n{sql_query}\n```\n\n{query_result}"
                else:
                    response_text += "\n\n⚠️ No se pudo conectar a la base de datos para ejecutar la consulta."
        else:
            response_text = "Error: No se recibió respuesta del agente."
        
        # Add assistant's response to the chat history
        messages.append({"role": "assistant", "content": response_text})
        
        # Yield the updated messages
        yield messages
        
    except Exception as e:
        error_msg = f"## ❌ Error\n\nOcurrió un error al procesar tu solicitud:\n\n```\n{str(e)}\n```"
        messages.append({"role": "assistant", "content": error_msg})
        yield messages

# Custom CSS for the app
custom_css = """
.gradio-container {
    max-width: 1200px !important;
    margin: 0 auto !important;
    font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif;
}

#chatbot {
    min-height: 500px;
    border: 1px solid #e0e0e0;
    border-radius: 8px;
    margin-bottom: 20px;
    padding: 20px;
    background-color: #f9f9f9;
}

.user-message, .bot-message {
    padding: 12px 16px;
    border-radius: 18px;
    margin: 8px 0;
    max-width: 80%;
    line-height: 1.5;
}

.user-message {
    background-color: #007bff;
    color: white;
    margin-left: auto;
    border-bottom-right-radius: 4px;
}

.bot-message {
    background-color: #f1f1f1;
    color: #333;
    margin-right: auto;
    border-bottom-left-radius: 4px;
}

#question-input textarea {
    min-height: 50px !important;
    border-radius: 8px !important;
    padding: 12px !important;
    font-size: 16px !important;
}

#send-button {
    height: 100%;
    background-color: #007bff !important;
    color: white !important;
    border: none !important;
    border-radius: 8px !important;
    font-weight: 500 !important;
    transition: background-color 0.2s !important;
}

#send-button:hover {
    background-color: #0056b3 !important;
}

.status-message {
    text-align: center;
    color: #666;
    font-style: italic;
    margin: 10px 0;
}
"""

def create_ui():
    """Crea y devuelve los componentes de la interfaz de usuario de Gradio."""
    # Verificar el estado del entorno
    env_ok, env_message = check_environment()
    
    # Crear el tema personalizado
    theme = gr.themes.Soft(
        primary_hue="blue",
        secondary_hue="indigo",
        neutral_hue="slate"
    )
    
    with gr.Blocks(
        css=custom_css,
        title="Asistente de Base de Datos SQL",
        theme=theme
    ) as demo:
        # Encabezado
        gr.Markdown("""
        # 🤖 Asistente de Base de Datos SQL
        
        Haz preguntas en lenguaje natural sobre tu base de datos y obtén resultados de consultas SQL.
        """)
        
        # Mensaje de estado
        if not env_ok:
            gr.Warning("⚠️ " + env_message)
        
        # Chatbot component
        chatbot = gr.Chatbot(
            label="Chat",
            height=500,
            show_label=True,
            container=True,
            type="messages",
            elem_id="chatbot"
        )
        
        # Input area
        with gr.Row():
            question_input = gr.Textbox(
                label="",
                placeholder="Escribe tu pregunta aquí...",
                container=False,
                scale=5,
                min_width=300,
                max_lines=3,
                autofocus=True,
                elem_id="question-input"
            )
            submit_button = gr.Button(
                "Enviar",
                variant="primary",
                min_width=100,
                scale=1,
                elem_id="send-button"
            )
        
        # System status
        with gr.Accordion("ℹ️ Estado del sistema", open=not env_ok):
            if not DEPENDENCIES_AVAILABLE:
                gr.Markdown("""
                ## ❌ Dependencias faltantes
                
                Para ejecutar esta aplicación localmente, necesitas instalar las dependencias:
                
                ```bash
                pip install -r requirements.txt
                ```
                """)
            else:
                if not agent:
                    gr.Markdown(f"""
                    ## ⚠️ Configuración incompleta
                    
                    No se pudo inicializar el agente de base de datos. Por favor, verifica que:
                    
                    1. Todas las variables de entorno estén configuradas correctamente
                    2. La base de datos esté accesible
                    3. La API de Google Gemini esté configurada
                    
                    **Error:** {agent_error if agent_error else 'No se pudo determinar el error'}
                    
                    ### Configuración local
                    
                    Crea un archivo `.env` en la raíz del proyecto con las siguientes variables:
                    
                    ```
                    DB_USER=tu_usuario
                    DB_PASSWORD=tu_contraseña
                    DB_HOST=tu_servidor
                    DB_NAME=tu_base_de_datos
                    GOOGLE_API_KEY=tu_api_key_de_google
                    ```
                    """)
                else:
                    if os.getenv('SPACE_ID'):
                        # Modo demo en Hugging Face Spaces
                        gr.Markdown("""
                        ## 🚀 Modo Demo
                        
                        Esta es una demostración del asistente de base de datos SQL. Para usar la versión completa con conexión a base de datos:
                        
                        1. Clona este espacio en tu cuenta de Hugging Face
                        2. Configura las variables de entorno en la configuración del espacio:
                           - `DB_USER`: Tu usuario de base de datos
                           - `DB_PASSWORD`: Tu contraseña de base de datos
                           - `DB_HOST`: La dirección del servidor de base de datos
                           - `DB_NAME`: El nombre de la base de datos
                           - `GOOGLE_API_KEY`: Tu clave de API de Google Gemini
                        
                        **Nota:** Actualmente estás en modo de solo demostración.
                        """)
                    else:
                        gr.Markdown("""
                        ## ✅ Sistema listo
                        
                        El asistente está listo para responder tus preguntas sobre la base de datos.
                        """)
        
        # Hidden component for streaming output
        streaming_output_display = gr.Textbox(visible=False)
        
        return demo, chatbot, question_input, submit_button, streaming_output_display

def create_application():
    """Create and configure the Gradio application."""
    # Create the UI components
    demo, chatbot, question_input, submit_button, streaming_output_display = create_ui()
    
    def user_message(user_input: str, chat_history: List[Dict]) -> Tuple[str, List[Dict]]:
        """Add user message to chat history and clear input."""
        if not user_input.strip():
            return "", chat_history
            
        logger.info(f"User message: {user_input}")
        
        # Initialize chat history if needed
        if chat_history is None:
            chat_history = []
        
        # Add user message
        chat_history.append({"role": "user", "content": user_input})
        
        # Add empty assistant response
        chat_history.append({"role": "assistant", "content": ""})
        
        # Clear the input
        return "", chat_history
    
    async def bot_response(chat_history: List[Dict]) -> List[Dict]:
        """Get bot response and update chat history."""
        if not chat_history or chat_history[-1]["role"] != "assistant":
            return chat_history
        
        try:
            # Get the user's question (second to last message)
            if len(chat_history) < 2:
                return chat_history
                
            question = chat_history[-2]["content"]
            logger.info(f"Processing question: {question}")
            
            # Call the agent and stream the response
            async for response in stream_agent_response(question, chat_history[:-2]):
                if isinstance(response, tuple) and len(response) > 0 and isinstance(response[0], list):
                    messages = response[0]
                    if messages and messages[-1]["role"] == "assistant":
                        # Update the assistant's response
                        chat_history[-1] = messages[-1]
                        yield chat_history
            
            logger.info("Response generation complete")
            
        except Exception as e:
            error_msg = f"Error al procesar la solicitud: {str(e)}"
            logger.error(error_msg, exc_info=True)
            chat_history[-1]["content"] = error_msg
            yield chat_history
    
    # 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],
            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]
        )
    
    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
    )