Water Quality Detection System 🌊

A machine learning-powered web application for detecting water quality using multiple ML algorithms. This system provides real-time water quality analysis through an intuitive web interface.

✨ Features

• Multiple ML Models: Random Forest, Neural Network, Decision Tree, KNN, and XGBoost
• Web Interface: User-friendly frontend for easy interaction
• Real-time Prediction: Instant water quality assessment
• Model Comparison: Compare different algorithms performance
• Data Upload: Support for CSV and Excel file uploads
• Dockerized: Easy deployment with Docker containers

🏗️ Architecture

├── backend/
│   ├── app.py                 # Flask API server
│   ├── ml_model/             # Pre-trained ML models
│   └── requirements.txt      # Python dependencies
├── frontend/
│   ├── index.html           # Main interface
│   ├── input.html           # Data input page
│   ├── model.html           # Model comparison
│   ├── result.html          # Results display
│   └── assets/              # Static files
└── Dockerfile               # Multi-stage Docker build

🚀 Quick Start

Option 1: Docker (Recommended)

# Clone the repository
git clone https://github.com/suryangh/water-quality.git
cd water-quality

# Build Docker image
docker build -t water-quality .

# Run the application
docker run -d --name water-quality -p 5000:5000 -p 5500:5500 water-quality

Option 2: Local Development

# Backend setup
cd backend
python -m venv dasildat
source dasildat/bin/activate  # On Windows: dasildat\Scripts\activate
pip install -r requirements.txt
python app.py

# Frontend setup (in another terminal)
cd frontend
python serve_frontend.py

🌐 Access the Application

• Frontend: http://localhost:5500
• Backend API: http://localhost:5000

📊 Water Quality Parameters

The system analyzes the following water quality parameters:

Parameter	Safe Range	Unit
Aluminium	0 - 0.2	mg/L
Ammonia	0 - 0.5	mg/L
Arsenic	0 - 0.01	mg/L
Barium	0 - 2.0	mg/L
Cadmium	0 - 0.005	mg/L
Chloramine	0 - 4.0	mg/L
Chromium	0 - 0.1	mg/L
Copper	0 - 1.3	mg/L
Fluoride	0 - 4.0	mg/L
Bacteria	0	count
Viruses	0	count

🤖 Machine Learning Models

Available Models:

1. Random Forest - General purpose, robust predictions
2. Neural Network - Complex pattern recognition
3. Decision Tree - Simple, interpretable rules
4. K-Nearest Neighbors (KNN) - Pattern-based classification
5. XGBoost - High-performance gradient boosting

Model Performance:

• All models are pre-trained on water quality datasets
• Real-time inference with optimized prediction times
• Model comparison features available in the web interface

🛠️ API Documentation

Predict Water Quality

POST /predict
Content-Type: application/json

{
  "model": "random_forest",
  "features": {
    "Aluminium": 0.1,
    "Ammonia": 0.2,
    "Arsenic": 0.005,
    // ... other parameters
  }
}

Response

{
  "prediction": "Safe",
  "confidence": 0.95,
  "model_used": "random_forest",
  "analysis": {
    "safe_parameters": ["Aluminium", "Ammonia"],
    "unsafe_parameters": ["Arsenic"]
  }
}

📁 File Upload

The system supports uploading water quality data in:

• CSV format
• Excel (.xlsx, .xls) format

Upload files through the web interface for batch predictions.

🐳 Docker Information

Image Size: ~560MB (optimized with multi-stage build)

Container Specifications:

• Base: Python 3.12 Alpine Linux
• Multi-stage build for minimal size
• Non-root user for security
• Exposed ports: 5000 (API), 5500 (Frontend)

🔧 Development

Prerequisites

• Python 3.12+
• Docker (optional)
• Git

Setup Development Environment

# Clone repository
git clone https://github.com/suryangh/water-quality.git
cd water-quality

# Create virtual environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r backend/requirements.txt

# Run in development mode
cd backend
python app.py

📝 Environment Variables

FLASK_ENV=production          # Flask environment
PYTHONUNBUFFERED=1           # Python output buffering
PYTHONDONTWRITEBYTECODE=1    # Disable .pyc files

🤝 Contributing

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

📄 License

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

👥 Team

• Team DASILDAT -

📞 Support

If you have any questions or issues, please:

1. Check the Issues page
2. Create a new issue if your problem isn't already reported
3. Provide detailed information about your setup and the issue

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