How debojyotimondaldev/pulsemonitor works
Pulse Monitor is a local Flask system-monitoring dashboard with a separate public-dataset evaluation workflow. Live metrics are collected in the background, stored locally, then read by web routes for anomaly detection, health scoring, and dashboard/history responses. Dataset analysis evaluates selected public metrics against annotated events and returns scores and chart data to its interface. The graph reflects the inspected runtime excerpts and README; unsampled template wiring and other implementation details are not inferred beyond documented capabilities.
Main entry points: routes.py Flask routes · collector_service.py Background collector · main.py Application launcher · queries.py Metrics query
Components 15
Collection and storage
- Application launcher
main.py - Background collector
collector/collector_service.py - System metrics
collector/system_monitor.py - SQLite metrics store
database/database.py - Metrics query
database/queries.py
Live monitoring
- Live anomaly detection
models/anomaly_detector.py - Health scoring
utils/health_score.py
Dataset evaluation
- Dataset evaluation
models/public_dataset_analysis.py - Public metric data
data/public_dataset
Web interfaces
- Flask routes
app/routes.py - Dashboard interface
app/templates/dashboard.html - Dashboard behavior
app/static/js/dashboard.js - Dataset interface
app/templates/dataset_analysis.html - Dataset interactions
app/static/js/dataset_analysis.js
Other components
- Local user
Connections 14
- Local user → Application launcher: starts locally
README.md - Application launcher → Background collector: starts thread
main.py - Background collector → System metrics: collects metrics
collector/collector_service.py - Background collector → SQLite metrics store: inserts metrics
collector/collector_service.py - Flask routes → Metrics query: loads metrics
app/routes.py - Metrics query → SQLite metrics store: reads metrics
database/queries.py - Flask routes → Live anomaly detection: detects anomalies
app/routes.py - Flask routes → Health scoring: calculates score
app/routes.py - Flask routes → Dashboard interface: renders dashboard
app/routes.py - Flask routes → Dataset interface: renders analysis page
app/routes.py - Dataset interactions → Flask routes: requests results
app/static/js/dataset_analysis.js - Flask routes → Dataset evaluation: analyzes metric
app/routes.py - Dataset evaluation → Public metric data: reads CSV data
models/public_dataset_analysis.py - Local user → Dashboard behavior: views dashboard
README.md