A Python-based optimization system for railway rake formation using Streamlit and PuLP, now with a polished UI and smart analytics.

✅ AI-Powered Optimization: Smart rake formation algorithms
✅ Real-time Analytics: Monitor performance metrics
✅ Demand Prediction: ML-based demand categorization
✅ Capacity Management: Track wagon and yard utilization
✅ Export Reports: Download detailed optimization plans
✅ Interactive UI: User-friendly interface with sidebar navigation

• Home
• Data Upload
• View Data
• Optimization
• Analytics

• order.csv: Contains order details (ID, material, weight)
• wagons.csv: Defines wagon capacities
• yard.csv: Contains yard constraints

The repository includes these sample CSV files in the project root for quick testing and demonstration. The app also provides a Data Upload page where users can upload their own CSV files to replace the defaults at runtime.

1. Install dependencies:

pip install streamlit pandas pulp numpy streamlit-option-menu

2. Run the application:

streamlit run app.py

• app.py: Main Streamlit web interface with navigation
• optimizer.py: Core optimization logic using PuLP
• ml_rules.py: ML-based demand prediction system
• requirements.txt: Dependency list
• Procfile: Deployment configuration