Edit des class

app.py

@@ -34,44 +34,21 @@ MODEL = None
 # -----------------------------------------------------------------------------
 # FastAPI Setup
 # -----------------------------------------------------------------------------
-app = FastAPI(title="👨‍💼 HR Prediction API")
+app = FastAPI(title="🌬️ Wind Turbine Prediction API")
 
 
 class PredictionFeatures(BaseModel):
-    Age: Union[int, float]
-    BusinessTravel: str
-    DailyRate: Union[int, float]
-    Department: str
-    DistanceFromHome: Union[int, float]
-    Education: Union[int, float]
-    EducationField: str
-    EmployeeCount: Union[int, float]
-    EmployeeNumber: Union[int, float]
-    EnvironmentSatisfaction: Union[int, float]
-    Gender: str
-    HourlyRate: Union[int, float]
-    JobInvolvement: Union[int, float]
-    JobLevel: Union[int, float]
-    JobRole: str
-    JobSatisfaction: Union[int, float]
-    MaritalStatus: str
-    MonthlyIncome: Union[int, float]
-    MonthlyRate: Union[int, float]
-    NumCompaniesWorked: Union[int, float]
-    Over18: str
-    OverTime: str
-    PercentSalaryHike: Union[int, float]
-    PerformanceRating: Union[int, float]
-    RelationshipSatisfaction: Union[int, float]
-    StandardHours: Union[int, float]
-    StockOptionLevel: Union[int, float]
-    TotalWorkingYears: Union[int, float]
-    TrainingTimesLastYear: Union[int, float]
-    WorkLifeBalance: Union[int, float]
-    YearsAtCompany: Union[int, float]
-    YearsInCurrentRole: Union[int, float]
-    YearsSinceLastPromotion: Union[int, float]
-    YearsWithCurrManager: Union[int, float]
+    Hour_Index: Union[int, float]
+    Turbine_ID: int
+    Rotor_Speed_RPM: float
+    Wind_Speed_mps: float
+    Power_Output_kW: float
+    Gearbox_Oil_Temp_C: float
+    Generator_Bearing_Temp_C: float
+    Vibration_Level_mmps: float
+    Ambient_Temp_C: float
+    Humidity_pct: float
+    Maintenance_Label: int
 
 
 # -----------------------------------------------------------------------------
@@ -84,7 +61,7 @@ def load_model_sync():
     try:
         # On attend que le chargement soit fini avant de rendre l'API disponible
         MODEL = mlflow.sklearn.load_model(MODEL_URI)
-        # models:/ibm_attrition_detector@production
+        # models:/wind_turbine_predictor@production
         print("✅ [INFO] Model loaded successfully!")
     except Exception as e:
         print(f"❌ [ERROR] Failed to load model: {e}")
@@ -118,4 +95,4 @@ async def predict(payload: PredictionFeatures):
 
 if __name__ == "__main__":
     # Port 7860 est le standard pour Hugging Face Spaces
-    uvicorn.run(app, host="0.0.0.0", port=int(os.getenv("PORT", 7860)))
+    uvicorn.run(app, host="0.0.0.0", port=int(os.getenv("PORT", 7860)))