https://github.com/aditya-ranjan1234/garment-industry-ml-solutions
Applications on ML in garment industry
https://github.com/aditya-ranjan1234/garment-industry-ml-solutions
applications ml streamlit
Last synced: 4 months ago
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Applications on ML in garment industry
- Host: GitHub
- URL: https://github.com/aditya-ranjan1234/garment-industry-ml-solutions
- Owner: Aditya-Ranjan1234
- Created: 2025-06-11T23:29:19.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-06-12T20:32:42.000Z (4 months ago)
- Last Synced: 2025-06-12T21:43:29.531Z (4 months ago)
- Topics: applications, ml, streamlit
- Language: Python
- Homepage: https://garment-industry-ml-solutions.streamlit.app/
- Size: 50 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Garment ML Dashboard
A comprehensive web-based dashboard for garment manufacturing analytics, quality control, and machine learning predictions.
## Features
- **Production Monitoring**
- Real-time production metrics
- Production trends visualization
- Efficiency analysis
- Line balancing insights
- **Quality Control**
- Quality metrics tracking
- Defect analysis
- Quality trends visualization
- Defect type distribution
- **Machine Learning Predictions**
- Production optimization
- Quality prediction
- Parameter optimization
- Confidence scoring
- **Simulation Tools**
- Production simulation
- Quality impact analysis
- Parameter optimization
- What-if scenarios
## Prerequisites
- Python 3.8 or higher
- pip (Python package installer)
- Virtual environment (recommended)
## Installation
1. Clone the repository:
```bash
git clone
cd garment-ml-dashboard
```
2. Create and activate a virtual environment:
```bash
# Windows
python -m venv venv
.\venv\Scripts\activate
# Linux/Mac
python3 -m venv venv
source venv/bin/activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Set up environment variables:
Create a `.env` file in the root directory with the following content:
```
SECRET_KEY=your-secret-key
DATABASE_URL=sqlite:///garment_ml.db
```
## Running the Application
1. Start the Flask development server:
```bash
flask run
```
2. Access the dashboard at `http://localhost:5000`
## Project Structure
```
garment-ml-dashboard/
├── app.py # Main Flask application
├── requirements.txt # Python dependencies
├── .env # Environment variables
├── static/ # Static files
│ ├── css/ # CSS styles
│ └── js/ # JavaScript files
├── templates/ # HTML templates
├── models/ # ML models
├── data/ # Data files
└── venv/ # Virtual environment
```
## Data Structure
The application uses the following data structure:
- Production Data:
- Date
- Product ID
- Total Units
- Good Units
- Defect Units
- Temperature
- Pressure
- Speed
- Humidity
- Quality Metrics:
- Quality Score
- Defect Rate
- Defect Types
- Parameter Impact
## API Endpoints
- `/api/production-data` - Get production data
- `/api/quality-metrics` - Get quality metrics
- `/api/predictions` - Get ML predictions
- `/api/optimization` - Get optimal parameters
- `/api/quality-trends` - Get quality trends
- `/api/defect-analysis` - Get defect analysis
## Machine Learning Models
The application uses the following ML models:
1. Production Model:
- Predicts production output based on parameters
- Uses historical data for training
- Provides confidence scores
2. Quality Model:
- Predicts quality metrics
- Identifies parameter impacts
- Suggests optimizations
## Contributing
1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request
## License
This project is licensed under the MIT License - see the LICENSE file for details.
## Support
For support, please open an issue in the GitHub repository or contact the development team.
## Acknowledgments
- Flask framework
- Plotly for visualizations
- scikit-learn for ML models
- Bootstrap for UI components