Update app.py

app.py

@@ -72,4 +72,36 @@ def prepare_input(data: dict):
     df["age"] = ((dt - dob).dt.days / 365.25).astype("float32")
     
     lat1, lon1 = np.radians(df["lat"]), np.radians(df["long"])
-    lat2, lon2 = np.radians
+    lat2, lon2 = np.radians(df["merch_lat"]), np.radians(df["merch_long"])
+    d = np.sin((lat2-lat1)/2)**2 + np.cos(lat1)*np.cos(lat2)*np.sin((lon2-lon1)/2)**2
+    df["distance"] = (6371 * 2 * np.arcsin(np.sqrt(d))).astype("float32")
+
+    df["avg_amt"] = df["amt"]
+    df["std_amt"] = 0.0
+    df["nb_trans"] = 1.0
+
+    expected_cols = [
+        'amt', 'hour', 'day_of_week', 'day', 'month', 'age', 'lat', 'long', 
+        'city_pop', 'distance', 'avg_amt', 'std_amt', 'nb_trans', 
+        'category', 'gender', 'state', 'merchant', 'job'
+    ]
+    return df[expected_cols]
+
+# --- 5. ENDPOINT NETTOYÉ ---
+@app.post("/predict")
+def predict(data: Transaction):
+    if model is None:
+        return {"error": "Modèle non chargé"}
+    try:
+        X_processed = prepare_input(data.dict())
+        prediction = model.predict(X_processed)
+        
+        # On ne renvoie que la valeur brute
+        return int(prediction[0])
+        
+    except Exception as e:
+        return {"error": str(e)}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)