How shivaani30/h2s-worker-safety-monitor works
The product receives worker/environment readings, classifies exposure risk, and exposes the latest status for a web dashboard and supervisor alerts. The sampled backend owns prediction, in-memory latest-reading state, and API responses; the README documents the sensor/device and dashboard roles. Model training is represented as a separate supporting stage. The frontend implementation and alert/analytics behavior were not sampled, so their internal wiring is not inferred.
Main entry points: main.py Receive sensor data · index.html Safety dashboard · train_model.py Train classifier · h2s_risk_model.pkl Risk classifier
Components 11
Sensor Ingestion
- Receive sensor data
backend/main.py
Risk Assessment
- Predict risk and confidence
backend/main.py - Risk classifier
ai/h2s_risk_model.pkl
Backend API
- Latest reading state
backend/main.py - Latest data endpoint
backend/main.py
Safety Dashboard
- Safety dashboard
frontend/index.html
Model Preparation
- Training dataset
ai/h2s_training_data.csv - Train classifier
ai/train_model.py
Other components
- Environmental sensors
- IoT / edge device
- Safety supervisor
Connections 12
- Environmental sensors → IoT / edge device: provide readings
- IoT / edge device → Receive sensor data: transmit readings
- Receive sensor data → Predict risk and confidence: passes features
- Predict risk and confidence → Risk classifier: predicts with
- Predict risk and confidence → Receive sensor data: returns risk
- Receive sensor data → Latest reading state: stores result
- Latest data endpoint → Latest reading state: reads
- Safety dashboard → Latest data endpoint: requests latest
- Latest data endpoint → Safety dashboard: returns readings
- Safety dashboard → Safety supervisor: displays status
- Train classifier → Training dataset: loads
- Train classifier → Risk classifier: saves trained model