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chore: update sample predictions to align with new ML pipeline schema
fcec78d
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fix: lazy-initialize DB engine in standalone scripts
Replace direct imports of `engine` and `SessionLocal` from `app.db.session` with local instantiation via `get_database_url()` in create_db, insert_data, and query_db scripts. Prevents DB connection attempts at import time when environment variables are not yet loaded.e1edad9
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feat: log predictions to database after each inference
75c7c56 -
feat: add database query and prediction diagnostic script
Add scripts/query_db.py with utilities to inspect the database: - employee count and churn rate stats - preview of first N employees - on-demand ML prediction for a given employee id - logged prediction stats (count, latest entry)c12fb2b -
feat: add script to seed employee data from CSV into database
c7263fa -
feat: add PostgreSQL database layer with SQLAlchemy ORM
- Add Employee and Prediction SQLAlchemy models (app/db/models.py) - Add database session/engine configuration from env vars (app/db/session.py) - Add script to create database and tables if not exists (scripts/create_db.py) - Add docker-compose with PostgreSQL + pgAdmin services - Update .env.example with DB_* connection variables - Add psycopg2-binary, SQLAlchemy, greenlet to requirements.txt - Document database schema, UML diagram and column constraints in READMEa194bc8
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init: project structure and base configuration
a395150