merge: Merge feat/database into dev

DB_SCHEMA.md

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+# Database Schema Documentation
+
+The application uses a single table `prediction_logs` to store all prediction requests and results.
+
+## Table: `prediction_logs`
+
+| Column Name | Type | Description |
+| :--- | :--- | :--- |
+| `id` | Integer | Primary Key, Auto-increment |
+| `timestamp` | DateTime | Timestamp of the prediction (UTC) |
+| `prediction` | Integer | Predicted class (0 or 1) |
+| `probability` | Float | Probability of the positive class (optional) |
+| `age` | Integer | Age of the employee |
+| `genre` | String | Gender (M/F) |
+| `revenu_mensuel` | Integer | Monthly income |
+| `statut_marital` | String | Marital status |
+| `departement` | String | Department |
+| `poste` | String | Job title |
+| `nombre_experiences_precedentes` | Integer | Number of previous companies |
+| `nombre_heures_travailless` | Integer | Hours worked |
+| `annee_experience_totale` | Integer | Total working years |
+| `annees_dans_l_entreprise` | Integer | Years at current company |
+| `annees_dans_le_poste_actuel` | Integer | Years in current role |
+| `satisfaction_employee_environnement` | Integer | Environment satisfaction (1-4) |
+| `note_evaluation_precedente` | Integer | Previous performance rating |
+| `niveau_hierarchique_poste` | Integer | Job level |
+| `satisfaction_employee_nature_travail` | Integer | Job involvement (1-4) |
+| `satisfaction_employee_equipe` | Integer | Relationship satisfaction (1-4) |
+| `satisfaction_employee_equilibre_pro_perso` | Integer | Work-life balance (1-4) |
+| `note_evaluation_actuelle` | Integer | Current performance rating |
+| `heure_supplementaires` | String | Overtime (Yes/No) |
+| `augementation_salaire_precedente` | String | Percent salary hike |
+| `nombre_participation_pee` | Integer | Stock option level |
+| `nb_formations_suivies` | Integer | Training times last year |
+| `nombre_employee_sous_responsabilite` | Integer | Number of subordinates |
+| `distance_domicile_travail` | Integer | Distance from home |
+| `niveau_education` | Integer | Education level (1-5) |
+| `domaine_etude` | String | Education field |
+| `ayant_enfants` | String | Has children (Yes/No) |
+| `frequence_deplacement` | String | Business travel frequency |
+| `annees_depuis_la_derniere_promotion` | Integer | Years since last promotion |
+| `annes_sous_responsable_actuel` | Integer | Years with current manager |
+
+## Relationships
+No relationships as this is a single-table logging schema for this POC.

app/core/config.py

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+import os
+
+class Settings:
+    PROJECT_NAME: str = "ML Prediction API"
+    PROJECT_VERSION: str = "1.0.0"
+    
+    # Database
+    DATABASE_URL: str = os.getenv("DATABASE_URL", "postgresql://postgres:pwd@localhost:5432/prediction_db")
+    
+    # Security
+    API_KEY: str = os.getenv("API_KEY", "default_insecure_key")
+    
+    # Model
+    MODEL_PATH: str = os.getenv("MODEL_PATH", "02_xgb_model_tuned.pkl")
+
+settings = Settings()

app/core/database.py

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+from sqlalchemy import create_engine
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import sessionmaker
+from app.core.config import settings
+
+# Handle cases where DATABASE_URL might start with postgres:// (Heroku style) instead of postgresql://
+SQLALCHEMY_DATABASE_URL = settings.DATABASE_URL
+if SQLALCHEMY_DATABASE_URL and SQLALCHEMY_DATABASE_URL.startswith("postgres://"):
+    SQLALCHEMY_DATABASE_URL = SQLALCHEMY_DATABASE_URL.replace("postgres://", "postgresql://", 1)
+
+engine = create_engine(SQLALCHEMY_DATABASE_URL)
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+
+Base = declarative_base()
+
+def get_db():
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()

app/models/models.py

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+from sqlalchemy import Column, Integer, String, Float, DateTime
+from sqlalchemy.sql import func
+from app.core.database import Base
+
+class PredictionLog(Base):
+    __tablename__ = "prediction_logs"
+
+    id = Column(Integer, primary_key=True, index=True)
+    timestamp = Column(DateTime(timezone=True), server_default=func.now())
+    
+    # Input features
+    age = Column(Integer)
+    genre = Column(String)
+    revenu_mensuel = Column(Integer)
+    statut_marital = Column(String)
+    departement = Column(String)
+    poste = Column(String)
+    nombre_experiences_precedentes = Column(Integer)
+    nombre_heures_travailless = Column(Integer)
+    annee_experience_totale = Column(Integer)
+    annees_dans_l_entreprise = Column(Integer)
+    annees_dans_le_poste_actuel = Column(Integer)
+    satisfaction_employee_environnement = Column(Integer)
+    note_evaluation_precedente = Column(Integer)
+    niveau_hierarchique_poste = Column(Integer)
+    satisfaction_employee_nature_travail = Column(Integer)
+    satisfaction_employee_equipe = Column(Integer)
+    satisfaction_employee_equilibre_pro_perso = Column(Integer)
+    note_evaluation_actuelle = Column(Integer)
+    heure_supplementaires = Column(String)
+    augementation_salaire_precedente = Column(String)
+    nombre_participation_pee = Column(Integer)
+    nb_formations_suivies = Column(Integer)
+    nombre_employee_sous_responsabilite = Column(Integer)
+    distance_domicile_travail = Column(Integer)
+    niveau_education = Column(Integer)
+    domaine_etude = Column(String)
+    ayant_enfants = Column(String)
+    frequence_deplacement = Column(String)
+    annees_depuis_la_derniere_promotion = Column(Integer)
+    annes_sous_responsable_actuel = Column(Integer)
+
+    # Output
+    prediction = Column(Integer)
+    probability = Column(Float, nullable=True)

app/models/schemas.py

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+from pydantic import BaseModel
+from typing import Optional
+
+class InputSchema(BaseModel):
+    age: int
+    genre: str
+    revenu_mensuel: int
+    statut_marital: str
+    departement: str
+    poste: str
+    nombre_experiences_precedentes: int
+    nombre_heures_travailless: int
+    annee_experience_totale: int
+    annees_dans_l_entreprise: int
+    annees_dans_le_poste_actuel: int
+    satisfaction_employee_environnement: int
+    note_evaluation_precedente: int
+    niveau_hierarchique_poste: int
+    satisfaction_employee_nature_travail: int
+    satisfaction_employee_equipe: int
+    satisfaction_employee_equilibre_pro_perso: int
+    note_evaluation_actuelle: int
+    heure_supplementaires: str
+    augementation_salaire_precedente: str
+    nombre_participation_pee: int
+    nb_formations_suivies: int
+    nombre_employee_sous_responsabilite: int
+    distance_domicile_travail: int
+    niveau_education: int
+    domaine_etude: str
+    ayant_enfants: str
+    frequence_deplacement: str
+    annees_depuis_la_derniere_promotion: int
+    annes_sous_responsable_actuel: int
+
+class PredictionOutput(BaseModel):
+    prediction: int
+    probability: Optional[float] = None

scripts/create_db.py

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+from app.core.database import Base, engine
+from app.models.models import PredictionLog
+
+def create_tables():
+    print("Creating database")
+    Base.metadata.create_all(bind=engine)
+    print("Tables created")
+
+if __name__ == "__main__":
+    create_tables()