[Super-cloud0]

Summary

This is a major refactoring that transforms EcoWatch AI from a monolithic 873-line Streamlit script into a well-organized Python package with clear separation of concerns. The application logic is now split into focused modules, enabling better testability, reusability, and maintainability.

Key Changes

Package Structure

• Created ecowatch/ package with modular components:
  • config.py — Settings, detection parameters, environment loading
  • detector.py — YOLO model wrapper (unified interface for classify/detect)
  • hazards.py — Hazard catalog with metadata (icons, colors, severity)
  • tracker.py — Temporal smoothing and hysteresis (frames → incidents)
  • sources.py — Frame sources (webcam, RTSP, file, screen capture)
  • storage.py — SQLite-based incident journal (replaces JSON)
  • recorder.py — Video clip recording with pre-roll buffer
  • notifier.py — Async Telegram notifications
  • overlay.py — Frame annotation with Cyrillic support
  • monitor.py — Background monitoring thread orchestrating the pipeline
  • ui.py — Streamlit web interface (extracted from main.py)
  • cli.py — Console mode for headless operation

Entry Points

• main.py — Now a minimal entry point that imports and runs the UI
• ecowatch/__main__.py — Enables python -m ecowatch for CLI mode
• run_app.bat — Updated Windows launcher

Testing & Quality

• Added tests/ directory with unit tests for core modules:
  • test_detector.py — YOLO result parsing
  • test_tracker.py — Decision logic and hysteresis
  • test_storage.py — Incident persistence
  • test_notifier.py — Telegram formatting
  • test_recorder.py — Clip recording with pre-roll
  • test_config_sources.py — Configuration and source parsing
• Added .github/workflows/tests.yml for CI
• Added pyproject.toml with ruff linting config
• Added requirements-dev.txt for development dependencies

Configuration & Deployment

• .env.example — Template for secrets (token, chat ID, coordinates)
• .streamlit/config.toml — Streamlit theme and server settings
• Updated .gitignore to exclude data, cache, and secrets
• Updated requirements.txt with pinned versions

Documentation

• Expanded README.md with feature highlights, installation, and usage examples
• Updated train.py with clearer docstring and refactored dataset validation

Notable Implementation Details

• Background Monitoring: Monitor runs in a daemon thread, keeping the Streamlit UI responsive and allowing monitoring to persist across page reloads
• Incident Tracking: Temporal smoothing via exponential moving average + hysteresis prevents false alarms from single frames
• Async Notifications: Telegram sends happen in a background queue, never blocking video processing
• Pre-roll Recording: ClipRecorder buffers frames before incident detection, capturing context before the alert
• Unified Model Support: Detector wrapper handles both classify and detect YOLO models with a common interface
• Legacy Migration: EventStore can import old JSON detection history into SQLite on first run
• Cyrillic Text: Overlay uses PIL for proper Cyrillic rendering on video frames

Migration Notes

• Old detection_history.json is automatically imported into data/incidents.db on first run
• Telegram credentials now read from .env file or environment variables (never hardcoded)
• Console mode (python -m ecowatch 0) enables 24/7 headless operation on servers

https://claude.ai/code/session_01PKYmgFdxkcjGu11gXfcToj