README1.md

🚀 HDD Solution Predictor - Deployment Package

Ready-to-deploy package for the HDD Solution Predictor application.

📦 Package Contents

Application Files

• app.py - Main Streamlit application (recommended)
• mobile_app.py - Mobile-optimized version
• requirements.txt - Python dependencies

Model Files

• decision_tree_model.pkl - Trained Decision Tree model (100% accuracy)
• dt_soil_encoder.pkl - Soil type label encoder
• dt_water_encoder.pkl - Water table label encoder
• dt_solution_encoder.pkl - Solution label encoder

Assets

• logo2.e8c5ff97.png - MEA (Metropolitan Electricity Authority) logo
• HDD_result.xlsx - Training dataset

Documentation

• README.md - This deployment guide

🔧 Quick Deployment

1. Install Dependencies

pip install -r requirements.txt

2. Run Application

# Main application (recommended)
streamlit run app.py

# Or mobile version
streamlit run mobile_app.py

3. Access Application

• Local: http://localhost:8501
• Network: Will be shown in terminal

🌐 Deployment Options

Option A: Local Development

git clone <repository>
cd deploy
pip install -r requirements.txt
streamlit run app.py

Option B: Streamlit Cloud

1. Upload all files to GitHub repository
2. Connect to Streamlit Cloud
3. Deploy from deploy/app.py

Option C: Docker Deployment

Create Dockerfile:

FROM python:3.11-slim

WORKDIR /app
COPY . .

RUN pip install -r requirements.txt

EXPOSE 8501

CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

Option D: Heroku Deployment

Create Procfile:

web: streamlit run app.py --server.port=$PORT --server.address=0.0.0.0

📊 Application Features

Core Functionality

• Input Parameters: Pipe diameter, soil type, water table
• ML Prediction: Decision Tree with 100% training accuracy
• Solution Types: A, B, C, D, E (different protection levels)

Technical Specifications

• Framework: Streamlit
• ML Model: Scikit-learn Decision Tree
• Design: Canva-style responsive UI
• Branding: MEA institutional logo

Supported Solutions

• Solution A: Enhanced Protection (Sheetpile + Trench + Grouting)
• Solution B: Maximum Protection (+ Casing)
• Solution C: Moderate Protection (Sheetpile + Trench)
• Solution D: Basic Protection (Grouting Only)
• Solution E: Minimal Intervention (No Additional Measures)

🔍 System Requirements

Python Version

• Python 3.8 or higher (recommended: 3.11)

Dependencies

• streamlit
• pandas
• numpy
• scikit-learn
• joblib
• plotly
• openpyxl
• Pillow

Resources

• RAM: Minimum 512MB, Recommended 1GB
• Storage: ~50MB for all files
• Network: Internet connection for initial setup

🔧 Configuration

Environment Variables (Optional)

export STREAMLIT_SERVER_PORT=8501
export STREAMLIT_SERVER_ADDRESS=0.0.0.0

Streamlit Config (Create .streamlit/config.toml)

[server]
port = 8501
address = "0.0.0.0"

[theme]
primaryColor = "#6c5ce7"
backgroundColor = "#ffffff"
secondaryBackgroundColor = "#f0f2f6"

🚨 Troubleshooting

Model Loading Issues

• Ensure all .pkl files are in the same directory
• Check Python/scikit-learn version compatibility

Logo Not Displaying

• Verify logo2.e8c5ff97.png exists in directory
• App will show MEA text fallback if logo missing

Port Already in Use

streamlit run app.py --server.port=8502

Permission Issues

chmod +x app.py
pip install --user -r requirements.txt

📱 Mobile Access

The application is fully responsive and works on:

• ✅ Desktop browsers
• ✅ Mobile phones
• ✅ Tablets
• ✅ Touch devices

🔒 Security Notes

• Application runs locally by default
• No external API calls
• Model predictions processed locally
• Training data included for reference only

📞 Support

Common Issues

1. Dependencies: Run pip install -r requirements.txt
2. Port conflicts: Use different port with --server.port=XXXX
3. File paths: Ensure all files are in same directory

Performance

• Model loading: ~1-2 seconds on first run
• Predictions: Instant (<100ms)
• UI rendering: <1 second

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🎯 Ready to Deploy!

1. Install requirements: pip install -r requirements.txt
2. Run application: streamlit run app.py
3. Open browser: Navigate to displayed URL
4. Start predicting: Enter parameters and get solutions!

🏢 Powered by MEA (Metropolitan Electricity Authority)