Trinetra - AI-Powered Data Preparation Assistant

Trinetra is a powerful AI-driven application that helps users prepare, clean, and enhance various types of data including CSV files, PDFs, and images. The application leverages advanced AI capabilities to automate data quality checks, implement data cleaning operations, and provide intelligent data processing solutions.

Features

CSV Data Processing

• Automated data quality checks
• Intelligent data cleaning and preparation
• Interactive data preview and analysis
• Summary statistics generation
• AI-powered data transformation suggestions

PDF Processing

• Automatic page orientation correction
• OCR (Optical Character Recognition) capabilities
• Noise and artifact removal
• Text formatting and alignment fixes
• Blank page detection and removal

Image Enhancement

• Interactive image enhancement controls
• Brightness, contrast, sharpness, and saturation adjustment
• Quality issue detection
• Format optimization
• Batch processing capabilities

Installation

1. Clone the repository:

git clone https://github.com/yourusername/Trinetra.git
cd Trinetra

2. Install the required dependencies:

pip install -r requirements.txt

3. Set up environment variables:

• Create a .env file in the root directory
• Add your Google API key:

GOOGLE_API_KEY=your_api_key_here

Usage

1. Start the application:

streamlit run streamlit_app.py

2. Access the web interface through your browser at http://localhost:8501

3. Use the sidebar to upload your files:

   • CSV files for data preparation
   • PDF files for document processing
   • Images for enhancement

4. Navigate between tabs:

   • Data Quality Explorer: View and analyze data quality issues
   • AI Data Prep: Select issues to resolve and apply AI-powered solutions

Project Structure

Trinetra/
├── backend/
│   ├── data_preparation_gemini.py
│   ├── data_quality_checks.py
│   ├── image_processing.py
│   └── pdf_processing.py
├── streamlit_app.py
├── requirements.txt
└── README.md

Dependencies

• streamlit
• pandas
• numpy
• scikit-learn
• scipy
• statsmodels
• google-generativeai
• datasketch
• python-dotenv
• PyMuPDF
• pytesseract
• Pillow
• opencv-python
• pywavelets

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Google Generative AI for powering the intelligent data preparation features
• Streamlit for the interactive web interface
• The open-source community for various data processing libraries