app_gradio.py

"""
Olist Text-to-SQL Gradio Application
Gradio interface for the fine-tuned Mistral-7B model.
"""

import gradio as gr
import pandas as pd
from model_loader import FineTunedModelLoader
from database import DatabaseHandler
import os
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Global variables for lazy loading
db_handler = None
model_loader = None
db_schema = None

def initialize_components():
    """Initialize model and database on first use (lazy loading)."""
    global db_handler, model_loader, db_schema
    
    if model_loader is None:
        print(" Initializing model and database...")
        db_path = os.getenv("DATABASE_PATH", "olist.sqlite")
        adapter_path = os.getenv("ADAPTER_PATH", "mhdakmal80/Olist-SQL-Agent-Final")
        
        db_handler = DatabaseHandler(db_path)
        model_loader = FineTunedModelLoader(adapter_path=adapter_path)
        db_schema = db_handler.get_schema()
        
        print(" Model and database loaded!")
    
    return db_handler, model_loader, db_schema

# Example questions
EXAMPLES = [
    ["How many orders are there?"],
    ["What are the top 5 best-selling products?"],
    ["Show total revenue by customer state"],
    ["Which sellers have the highest ratings?"],
    ["List all orders from São Paulo"],
    ["What is the average delivery time?"],
    ["Count customers by state"],
    ["Show payment types and their usage"],
]

def generate_and_execute(question):
    """
    Generate SQL from question and execute it.
    
    Args:
        question: Natural language question
        
    Returns:
        Tuple of (sql_query, results_df, status_message)
    """
    if not question or not question.strip():
        return "", None, " Please enter a question"
    
    # Initialize components on first use (lazy loading)
    db_handler, model_loader, db_schema = initialize_components()
    
    # Generate SQL
    result = model_loader.generate_sql(question, db_schema)
    
    if not result['success']:
        return "", None, f" SQL Generation Failed: {result['error']}"
    
    sql_query = result['sql']
    
    # Execute query
    exec_result = db_handler.execute_query(sql_query)
    
    if not exec_result['success']:
        return sql_query, None, f" Query Execution Failed: {exec_result['error']}"
    
    # Format results
    df = exec_result['data']
    row_count = exec_result['row_count']
    
    status = f" Success! Retrieved {row_count} rows"
    if exec_result.get('warning'):
        status += f"\n {exec_result['warning']}"
    
    return sql_query, df, status

# Create Gradio interface
with gr.Blocks(title="Olist Text-to-SQL Agent", theme=gr.themes.Soft()) as demo:
    
    gr.Markdown("""
    # 🤖 Olist Text-to-SQL Agent
    
    Convert natural language questions into SQL queries using a **fine-tuned Mistral-7B model**.
    
    **Model**: Mistral-7B-Instruct-v0.2 fine-tuned with QLoRA on Olist e-commerce dataset
    
     **Note**: Running on CPU - queries may take 30-60 seconds. For faster performance, the model supports GPU deployment.
    """)
    
    with gr.Row():
        with gr.Column(scale=2):
            question_input = gr.Textbox(
                label="Ask your question",
                placeholder="e.g., What are the top 10 customers by total spending?",
                lines=3
            )
            
            with gr.Row():
                submit_btn = gr.Button(" Generate SQL & Execute", variant="primary")
                clear_btn = gr.ClearButton([question_input])
        
        with gr.Column(scale=1):
            gr.Markdown("""
            ### 💡 Example Questions
            Click any example to try it!
            """)
    
    with gr.Row():
        sql_output = gr.Code(
            label="Generated SQL Query",
            language="sql",
            lines=5
        )
    
    with gr.Row():
        status_output = gr.Textbox(
            label="Status",
            lines=2
        )
    
    with gr.Row():
        results_output = gr.Dataframe(
            label="Query Results",
            wrap=True
        )
    
    # Examples section
    gr.Examples(
        examples=EXAMPLES,
        inputs=question_input,
        label="Try these examples:"
    )
    
    # Info section
    with gr.Accordion("ℹ About this app", open=False):
        gr.Markdown("""
        ### Model Details
        - **Base Model**: mistralai/Mistral-7B-Instruct-v0.2
        - **Fine-Tuned Model**: [mhdakmal80/Olist-SQL-Agent-Final](https://huggingface.co/mhdakmal80/Olist-SQL-Agent-Final)
        - **Training Method**: QLoRA (4-bit quantization)
        - **Training Data**: 1000+ synthetic question-SQL pairs
        - **Accuracy**: 90% on test set
        
        ### Database
        - **Dataset**: Olist E-commerce (Brazilian marketplace)
        - **Tables**: 9 tables with 100K+ orders
        - **Columns**: Customer info, orders, products, payments, reviews, sellers
        
        ### Tech Stack
        - PyTorch, Transformers, PEFT, BitsAndBytes
        - Gradio for UI
        - SQLite for database
        """)
    
    with gr.Accordion("Database Schema", open=False):
        gr.Markdown("""
        The database schema will be loaded when you submit your first query.
        
        **Tables**: orders, customers, products, sellers, payments, reviews, etc.
        """)
    
    # Event handlers
    submit_btn.click(
        fn=generate_and_execute,
        inputs=question_input,
        outputs=[sql_output, results_output, status_output]
    )
    
    question_input.submit(
        fn=generate_and_execute,
        inputs=question_input,
        outputs=[sql_output, results_output, status_output]
    )

# Launch the app
if __name__ == "__main__":
    demo.launch()