Text to SQL NLP

This project converts natural language queries into SQL statements using machine learning and natural language processing techniques.

Features

• Preprocessing of natural language queries
• Model training for text-to-SQL translation
• Inference pipeline for converting text to SQL
• Dataset download and inspection utilities

Project Structure

main.py
requirements.txt
src/
  config/
    model.conf.yaml
  model/
    inference.py
    preprocess.py
    train.py
  output/
  pipeline/
    dep_install.py
    download_dataset.py
    inspect.py
    main_pipeline.py
  processed/
    dataset/
      train.csv

Getting Started

1. Clone the repository

   https://github.com/DeveloperAromal/Text_to_SQL.git
   cd Text_to_SQL

2. Create a virtual environment (recommended)

   python -m venv venv
   
   # On Windows
   venv\Scripts\activate
   
   # On macOS/Linux
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Run the code

   python main.py

Prerequisites

• Python 3.11 or higher
• All dependencies listed in requirements.txt

Configuration

• Model and pipeline configurations can be found in src/config/model.conf.yaml.

Dataset

• The processed dataset is located at src/processed/dataset/train.csv.
• Use src/pipeline/download_dataset.py to download and prepare datasets.

Note: Ensure the dataset is preprocessed before training the model. You can customize dataset handling in the pipeline scripts as needed.