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Cover image for FrostGuard AI – Cold Chain
FrostGuard AI – Cold Chain Monitoring & Predictive Rerouting Overview FrostGuard AI is a real-time cold-chain logistics platform built by Team Red Dragon during a hackathon. It monitors a simulated fleet of medical cargo trucks, predicts temperature breaches before they happen using trained ML models, and recommends the nearest cold-storage facility when a shipment is at risk — all rendered on a live geospatial dashboard. Core Capabilities Predictive Breach Detection: An Isolation Forest anomaly detector and a HistGradientBoosting-based forecaster (frost_ml.py (http://ml.py), trained via train_model.py (http://model.py)) flag abnormal temperature patterns and forecast breach risk 30 seconds ahead. The trained model is serialized once (frostguard_ml.joblib) and loaded at boot rather than retrained on every startup — keeping cold starts fast on resource-limited hosts. Nearest-Facility Rerouting: A K-Nearest Neighbors model (scikit-learn) matches an at-risk truck's GPS position to the nearest of 16 cold-storage nodes across India, so operators get an immediate reroute recommendation rather than raw coordinates. Cached Road Routing: Driving paths between cities are fetched from the OSRM routing engine and precomputed once (precompute_routes.py (http://routes.py) → routes_cache.json), removing a live external API dependency from the app's boot path. Interactive Dashboard: main_dashboard.py (http://dashboard.py), built with Streamlit, gives operators a live map, fleet metrics, alerts, and a manual failure-injection tool for demoing breach scenarios. Optional Standalone Services: api.py (http://api.py) (FastAPI) and Bridge.py (http://Bridge.py) (Flask) provide REST endpoints for telemetry ingestion, and Bridge.py (http://Bridge.py) includes a Discord webhook integration that posts an alert when a truck goes CRITICAL — opt-in via a DISCORD_WEBHOOK environment variable. These run independently of the main dashboard and aren't required for it to function. Technologies: Python, Streamlit, scikit-learn (Isolation Forest, HistGradientBoosting, KNN), Docker, REST APIs, OSRM, Discord Webhooks.
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Cover image for BenchMark ◆ Context & Challenge The client
BenchMark ◆ Context & Challenge The client needed a modern and scalable digital platform to showcase services, support marketing campaigns, and improve client engagement. The challenge was to combine high-performance web development with UX-driven design and integrated marketing tools, while enabling businesses to track performance and optimize their digital presence effectively ◆ Approach & Solution We developed a user-centric digital platform that integrates design, content, and marketing capabilities, enabling businesses to manage their online presence and drive data-driven growth. ◆ Key focus areas: ‣ User-centric UI/UX for improved engagement ‣ SEO and performance marketing integration ‣ Content-driven architecture for scalability ‣ Analytics and conversion tracking systems ‣ Secure and high-performance infrastructure ◆ Execution ‣ Built responsive and scalable web platform ‣ Implemented SEO and content management capabilities ‣ Integrated analytics tools for user behavior tracking ‣ Developed UX testing and usability optimization workflows ‣ Designed secure hosting and performance-focused architecture ◆ Outcome & Impact The platform enables businesses to enhance their digital presence and optimize marketing performance. ‣ Improved user engagement through optimized UX design ‣ Increased visibility with integrated SEO strategies ‣ Data-driven decision making with analytics insights ‣ Enhanced conversion rates through performance optimization ‣ Scalable foundation for ongoing digital growth
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