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| title: BTP Intelligence Engine | |
| colorFrom: purple | |
| colorTo: red | |
| sdk: docker | |
| pinned: false | |
| app_port: 7860 | |
| license: mit | |
| # Bengaluru Traffic Police: Intelligence Engine | |
| **Flipkart Gridlock 2.0 Hackathon Submission** | |
| **Problem Statement 2:** Event-Driven Traffic Congestion Forecasting & Dynamic Routing | |
| The **BTP Intelligence Engine** is a production-grade MLOps prototype designed to simulate a live command center tool. Moving beyond standard static machine learning, it provides a comprehensive end-to-end simulation capable of predicting, visualizing, and coordinating responses to localized traffic breakdown events (both planned and unplanned) across the Bengaluru Metropolitan Area. | |
| ## Key Features | |
| ### 1. Ultra-Premium "Cyber-Neon" Aesthetic | |
| A beautiful, high-contrast dark-mode UI built natively on Streamlit. Features custom CSS for frosted glass metric cards, glowing crimson accents, and centralized state management for a seamless, professional experience. | |
| ### 2. Dual-Mode Input Controller | |
| A robust state controller that perfectly synchronizes input commands without lag: | |
| - **Interactive Map**: Click anywhere on the map to instantly set the event epicenter. | |
| - **Manual GPS**: Precise latitude and longitude inputs with strict geo-fencing (Lat 12.80-13.25, Lon 77.45-77.80) to prevent coordinate drifting to the Arctic. Out-of-bounds clicks trigger non-intrusive toast notifications. | |
| ### 3. Live Spatio-Temporal Forecasting Pipeline | |
| - **Impact Forecaster**: Utilizes advanced ML models (XGBoost) trained on temporal/spatial features to predict congestion radius and clearance times. | |
| - **Operations Optimizer**: Employs NetworkX graph logic to generate dynamic routing diversions, along with deterministic models to calculate the required ground personnel and barricades. | |
| ### 4. Advanced Geospatial Visualizations | |
| - **Live Operational Map**: Interactive `Folium` rendering with an overlay of the congestion zone (Red Circle), diversion points (Green Markers), and calculated polyline escape routes. | |
| - **Resource Allocation Summary Heatmap**: A `folium.plugins.HeatMap` that mathematically distributes density coordinates (epicenter, diversion nodes, and perimeter barricades) to visually communicate the deployment footprint of BTP personnel. | |
| ### 5. Network Cascade Alerts & Plotly Telemetry | |
| - **Dynamic OSM Lookups**: Automatically queries the OpenStreetMap API via `geopy` to reverse-geocode map clicks, generating hyper-local neighborhood-aware warnings. | |
| - **Perimeter Saturation Chart**: A live, horizontal Plotly bar chart visually mapping the predicted stress load onto surrounding arterial roads, shifting to Crimson when capacity hits $\ge 90\%$. | |
| ### 6. Post-Event Retrospective & MLOps Database | |
| - **Model Calibration Form**: Swap the dashboard to retrospective mode. Command center operators can log the actual clearance times and variance factors directly against the AI forecast. | |
| - **SQLite Persistence**: Ground-truth deltas are seamlessly committed to a local `btp_intelligence.db` database using native Python SQLite3. | |
| - **Live MLOps Health Card**: A live diagnostic banner computes Mean Absolute Error (MAE) and Systematic Bias in real-time directly from the database to track model drift. | |
| ## Tech Stack | |
| - **Frontend & App State**: Streamlit, Streamlit-Folium | |
| - **Machine Learning**: Scikit-Learn, XGBoost, Pandas, Numpy, Scipy | |
| - **Geospatial & Routing**: Folium, NetworkX, Geopy | |
| - **Data Visualization**: Plotly | |
| - **Database Persistence**: SQLite3 | |
| ## Installation & Execution | |
| 1. **Navigate to the project directory.** | |
| 2. **Install the dependencies:** | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 3. **Train the local ML Models (Ensure binaries exist):** | |
| ```bash | |
| python src/main.py | |
| ``` | |
| 4. **Launch the Intelligence Engine:** | |
| ```bash | |
| streamlit run src/app.py | |
| ``` | |
| ## Design Philosophy | |
| The system implements a "Demo Mode" initialization, instantly loading a default event profile centered around Cubbon Park (Lat 12.9716, Lon 77.5946) to prevent a cold-start blank screen during hackathon presentations. |