fix: replace empty api.py with working FastAPI endpoint

Author: Dreipfelt

api.py

@@ -0,0 +1,188 @@
+"""
+Oasis Security – Crime Predictor API
+FastAPI + MLflow production-ready endpoint
+"""
+
+from contextlib import asynccontextmanager
+from typing import Dict, Optional
+
+import mlflow
+import mlflow.lightgbm
+import numpy as np
+import os
+import pandas as pd
+import uvicorn
+
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+
+# ---------------------------------------------------------------------------
+# Config MLflow
+# ---------------------------------------------------------------------------
+MLFLOW_URI = os.getenv("MLFLOW_TRACKING_URI", "http://localhost:5000")
+mlflow.set_tracking_uri(MLFLOW_URI)
+mlflow.set_experiment("crime_predictor_prod")
+
+# ---------------------------------------------------------------------------
+# Lifespan : chargement modèle au démarrage
+# ---------------------------------------------------------------------------
+predictor = None
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    global predictor
+    print("🚀 Chargement modèle...")
+    try:
+        model_uri = "models:/crime_predictor_prod/Production"
+        predictor = mlflow.lightgbm.load_model(model_uri)
+        print("✅ Modèle chargé depuis MLflow Registry")
+    except Exception:
+        # Fallback : modèle local sérialisé
+        from models.crime_predictor.src.model import CrimeRatePredictor
+        predictor = CrimeRatePredictor()
+        predictor.load("models/crime_predictor/artifacts/crime_predictor.pkl")
+        print("✅ Modèle chargé depuis fichier local")
+    yield
+    print("🛑 API shutdown")
+
+
+# ---------------------------------------------------------------------------
+# App
+# ---------------------------------------------------------------------------
+app = FastAPI(
+    title="Oasis Security – Crime Predictor API",
+    version="2.0.0",
+    description="Prédiction du taux de délinquance par région (pour 100 000 habitants)",
+    lifespan=lifespan,
+)
+
+
+# ---------------------------------------------------------------------------
+# Schémas
+# ---------------------------------------------------------------------------
+class PredictionRequest(BaseModel):
+    year: int = 2030
+    indicateur: str
+    region: str
+    lag1: Optional[float] = 250.0
+    lag2: Optional[float] = 245.0
+
+    model_config = {"json_schema_extra": {
+        "example": {
+            "year": 2030,
+            "indicateur": "Coups et blessures volontaires",
+            "region": "R11",
+            "lag1": 280.5,
+            "lag2": 275.0,
+        }
+    }}
+
+
+# ---------------------------------------------------------------------------
+# Endpoints
+# ---------------------------------------------------------------------------
+@app.get("/health", tags=["Monitoring"])
+async def health():
+    """Vérifie que l'API et le modèle sont opérationnels."""
+    return {
+        "status": "healthy",
+        "model_loaded": predictor is not None,
+        "model_version": "v2.0",
+        "mlflow_uri": MLFLOW_URI,
+    }
+
+
+@app.post("/predict", response_model=Dict, tags=["Prédiction"])
+async def predict(request: PredictionRequest):
+    """
+    Prédit le taux de délinquance pour un indicateur et une région donnés.
+
+    - **year** : année cible (ex. 2030)
+    - **indicateur** : catégorie de crime (ex. "Coups et blessures volontaires")
+    - **region** : code région INSEE (ex. "R11" pour Île-de-France)
+    - **lag1 / lag2** : taux des 2 années précédentes (optionnel, valeurs par défaut utilisées si absent)
+    """
+    if predictor is None:
+        raise HTTPException(status_code=503, detail="Modèle non chargé")
+
+    with mlflow.start_run(nested=True) as run:
+        try:
+            lag1 = request.lag1 or 250.0
+            lag2 = request.lag2 or 245.0
+
+            features = pd.DataFrame([{
+                "year_sin":    np.sin(2 * np.pi * request.year / 10),
+                "year_cos":    np.cos(2 * np.pi * request.year / 10),
+                "year_trend":  (request.year - 2016) / 9,
+                "lag1":        lag1,
+                "lag2":        lag2,
+                "roll_mean_3": (lag1 + lag2 + 240.0) / 3,
+                "region_mean": 250.0,
+                "ind_code":    hash(request.indicateur) % 100,
+                "reg_code":    int(request.region.replace("R", "")),
+            }])
+
+            pred = float(predictor.predict(features)[0])
+
+            # Observabilité MLflow
+            mlflow.log_params({
+                "indicateur": request.indicateur,
+                "region":     request.region,
+                "year":       request.year,
+            })
+            mlflow.log_metric("prediction", pred)
+
+            niveau = (
+                "🚨 Risque élevé"   if pred > 400 else
+                "⚠️  Risque modéré" if pred > 300 else
+                "✅ Risque faible"
+            )
+
+            return {
+                "prediction":    round(pred, 2),
+                "unit":          "taux / 100 000 habitants",
+                "year":          request.year,
+                "indicateur":    request.indicateur,
+                "region":        request.region,
+                "interpretation": niveau,
+                "mlflow_run_id": run.info.run_id,
+            }
+
+        except Exception as e:
+            mlflow.log_metric("error", 1)
+            raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.get("/leaderboard", tags=["Analyse"])
+async def leaderboard():
+    """
+    Retourne le top 5 des combinaisons région/indicateur
+    avec les prédictions 2030 les plus élevées (risques prioritaires).
+    """
+    try:
+        client = mlflow.MlflowClient()
+        runs = client.search_runs(
+            experiment_ids=["0"],
+            order_by=["metrics.prediction DESC"],
+            max_results=50,
+        )
+        return {
+            "top_risks": [
+                {
+                    "indicateur": r.data.params.get("indicateur", "N/A"),
+                    "region":     r.data.params.get("region", "N/A"),
+                    "pred_2030":  r.data.metrics.get("prediction", 0),
+                }
+                for r in runs
+            ][:5]
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+# ---------------------------------------------------------------------------
+# Lancement direct
+# ---------------------------------------------------------------------------
+if __name__ == "__main__":
+    uvicorn.run("api:app", host="0.0.0.0", port=8000, reload=False)