Upload app.py

app.py

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+"""
+API de prédiction du prix de location - Projet Getaround.
+
+Cette API expose un point de terminaison /predict qui renvoie le prix
+de location journalier suggéré pour un véhicule, à partir de ses
+caractéristiques.
+"""
+
+from pathlib import Path
+from typing import List, Union
+
+import joblib
+import pandas as pd
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel, Field
+
+# ---------------------------------------------------------------------------
+# Configuration de l'application
+# ---------------------------------------------------------------------------
+APP_TITLE = "API Getaround - Prédiction du prix de location"
+APP_DESCRIPTION = (
+    "Cette API met à disposition un modèle de Machine Learning entraîné "
+    "pour suggérer le prix journalier optimal d'une location de véhicule "
+    "sur la plateforme Getaround.\n\n"
+    "### Performances du modèle (XGBoost Regressor) :\n"
+    "* **Erreur absolue moyenne (MAE) :** 9,18 EUR\n"
+    "* **RMSE :** 12,82 EUR\n"
+    "* **R² :** 0,846\n"
+)
+APP_VERSION = "1.0.0"
+MODEL_PATH = Path(__file__).resolve().parent / "best_model.joblib"
+
+FEATURE_COLUMNS = [
+    "model_key", "mileage", "engine_power", "fuel", "paint_color",
+    "car_type", "private_parking_available", "has_gps",
+    "has_air_conditioning", "automatic_car", "has_getaround_connect",
+    "has_speed_regulator", "winter_tires",
+]
+
+BOOL_COLUMNS = [
+    "private_parking_available", "has_gps", "has_air_conditioning",
+    "automatic_car", "has_getaround_connect", "has_speed_regulator",
+    "winter_tires",
+]
+
+
+# ---------------------------------------------------------------------------
+# Initialisation
+# ---------------------------------------------------------------------------
+app = FastAPI(
+    title=APP_TITLE,
+    description=APP_DESCRIPTION,
+    version=APP_VERSION,
+)
+
+# Chargement du modèle
+try:
+    model = joblib.load(MODEL_PATH)
+    MODEL_LOADED = True
+except Exception as exc:
+    print(f"Erreur de chargement du modèle : {exc}")
+    model = None
+    MODEL_LOADED = False
+
+
+# ---------------------------------------------------------------------------
+# Schémas Pydantic
+# ---------------------------------------------------------------------------
+class PredictionInput(BaseModel):
+    """Schéma d'entrée pour l'endpoint /predict."""
+
+    input: List[List[Union[str, int, float, bool]]] = Field(
+        ...,
+        examples=[[[
+            "Citroën", 140411, 100, "diesel", "black", "convertible",
+            True, True, False, False, True, True, True,
+        ]]],
+    )
+
+
+class PredictionOutput(BaseModel):
+    """Schéma de sortie pour l'endpoint /predict."""
+
+    prediction: List[float]
+
+
+# ---------------------------------------------------------------------------
+# Endpoints
+# ---------------------------------------------------------------------------
+@app.get("/", tags=["Accueil"])
+async def root():
+    """Point d'entrée par défaut."""
+    return {
+        "message": "API Getaround opérationnelle",
+        "version": APP_VERSION,
+        "model_loaded": MODEL_LOADED,
+        "documentation": "/docs",
+    }
+
+
+@app.get("/health", tags=["Accueil"])
+async def health():
+    """Indicateur de santé du service."""
+    return {"status": "ok" if MODEL_LOADED else "model_missing"}
+
+
+@app.post(
+    "/predict",
+    tags=["Prédiction"],
+    response_model=PredictionOutput,
+    summary="Prédire le prix de location journalier",
+)
+async def predict(payload: PredictionInput):
+    """Renvoie la prédiction du prix de location journalier (EUR/jour)."""
+    if not MODEL_LOADED:
+        raise HTTPException(
+            status_code=503,
+            detail="Modèle non chargé sur le serveur.",
+        )
+    try:
+        df_input = pd.DataFrame(payload.input, columns=FEATURE_COLUMNS)
+
+        # Conversion des types
+        for col in BOOL_COLUMNS:
+            df_input[col] = df_input[col].astype(int)
+        df_input["mileage"] = df_input["mileage"].astype(int)
+        df_input["engine_power"] = df_input["engine_power"].astype(int)
+
+        preds = model.predict(df_input)
+        return {"prediction": [round(float(p), 2) for p in preds]}
+    except Exception as exc:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Erreur lors de la prédiction : {exc}",
+        )
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=False)