Delete app.py

app.py

@@ -1,141 +0,0 @@
-"""
-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)