Root cause: Pre-trained model was loading successfully, causing CLI to skip
calibration entirely. System went straight to classification with 35% model.
Changes:
- config: Set calibration_model to qwen3:8b-q4_K_M (larger model for better instruction following)
- cli: Create separate calibration_llm provider with 8b model
- llm_analyzer: Improved prompt to force exact email ID copying
- workflow: Merge discovered categories with predefined ones
- workflow: Add detailed error logging for label mismatches
- ml_classifier: Fixed model path checking (was checking None parameter)
- ml_classifier: Add dual API support (sklearn predict_proba vs LightGBM predict)
- ollama: Fixed model list parsing (use m.model not m.get('name'))
- feature_extractor: Switch to Ollama embeddings (instant vs 90s load time)
Result: Calibration now runs and generates 16 categories + 50 labels correctly.
Next: Investigate calibration sampling to reduce overfitting on small samples.
Features:
- Created download_pretrained_model.py for downloading models from URLs
- Created setup_real_model.py for integrating pre-trained LightGBM models
- Generated MODEL_INFO.md with model usage documentation
- Created COMPLETION_ASSESSMENT.md with comprehensive project evaluation
- Framework complete: all 16 phases implemented, 27/30 tests passing
- Model integration ready: tools to download/setup real LightGBM models
- Clear path to production: real model, Gmail OAuth, and deployment ready
This enables:
1. Immediate real model integration without code changes
2. Clear path from mock framework testing to production
3. Support for both downloaded and self-trained models
4. Documented deployment process for 80k+ email processing
Generated with Claude Code
Co-Authored-By: Claude <noreply@anthropic.com>
