README.md

metadata

title: Text-to-SQL Converter
emoji: 🗄️
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.35.0
app_file: app.py
pinned: false
license: mit

Text-to-SQL Converter

A powerful AI model that converts natural language questions into SQL queries.

Features

• Natural language to SQL conversion
• Beautiful web interface
• REST API endpoints
• Batch processing support

Usage

Simply enter your question and table headers to get SQL queries instantly!

🚀 Features

• Natural Language to SQL: Convert plain English questions to SQL queries
• Web Interface: Beautiful ChatGPT-like interface for easy interaction
• Batch Processing: Handle multiple queries at once
• Real-time Generation: Fast and accurate SQL generation
• Health Monitoring: Built-in health checks and monitoring

🎯 Usage

Web Interface

Simply visit the web interface and:

1. Enter your question in natural language
2. Provide the table headers (comma-separated)
3. Click "Generate SQL Query" to get your SQL

API Usage

Single Query

import requests

response = requests.post("https://your-space-url.hf.space/predict", json={
    "question": "How many employees are older than 30?",
    "table_headers": ["id", "name", "age", "department", "salary"]
})

sql_query = response.json()["sql_query"]
print(sql_query)

Batch Queries

response = requests.post("https://your-space-url.hf.space/batch", json={
    "queries": [
        {
            "question": "How many employees are older than 30?",
            "table_headers": ["id", "name", "age", "department", "salary"]
        },
        {
            "question": "Show all employees in IT department",
            "table_headers": ["id", "name", "age", "department", "salary"]
        }
    ]
})

results = response.json()["results"]

📊 Example Queries

Question	Table Headers	Generated SQL
"How many employees are older than 30?"	id, name, age, department, salary	SELECT COUNT(*) FROM table WHERE age > 30
"Show all employees in IT department"	id, name, age, department, salary	SELECT * FROM table WHERE department = 'IT'
"What is the average salary by department?"	id, name, age, department, salary	SELECT department, AVG(salary) FROM table GROUP BY department

🔧 API Endpoints

• GET / - Web interface
• GET /api - API information
• POST /predict - Generate SQL for single question
• POST /batch - Generate SQL for multiple questions
• GET /health - Health check
• GET /docs - Interactive API documentation

🏗️ Model Architecture

This model is based on Salesforce CodeT5 and fine-tuned specifically for text-to-SQL conversion using PEFT (Parameter Efficient Fine-Tuning). The model has been trained on a diverse dataset of natural language questions and their corresponding SQL queries.

Model Details

• Base Model: Salesforce/codet5-base
• Fine-tuning: PEFT (LoRA)
• Input Format: Structured text with table headers and questions
• Output: SQL queries

🚀 Deployment

This application is deployed on Hugging Face Spaces and can be accessed via the provided URL. The deployment includes:

• FastAPI backend
• Modern web interface
• Model serving with automatic scaling
• Health monitoring

📝 License

This project is open source and available under the MIT License.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.