Update app.py

app.py

@@ -37,19 +37,19 @@ def predict_price(date: datetime ,room_count: int, address:str, surface: float ,
     isHouse = (property_type == 'Maison')
     rf_input = pd.DataFrame([{"date_mutation": date, "nombre_pieces_principales": room_count, "longitude" : longitude,"latitude":latitude, "surface_batie_totale": surface, "type_local_Maison": isHouse}])
     print(rf_input)
-    rf_pred = rf.predict(rf_input)[0]
+    rf_pred = np.exp(rf.predict(rf_input)[0])
     predictions_all = np.array([tree.predict(rf_input) for tree in rf.estimators_])
     std_predict = np.std(predictions_all)
     reliability_index = compute_reliability(std_predict)
     q1 = np.exp(np.quantile(predictions_all, 0.25))
     q2 = np.exp(np.quantile(predictions_all, 0.5))
     q3 = np.exp(np.quantile(predictions_all, 0.75))
-    if (rf_pred_exp <= q1) | (rf_pred_exp >= q3):
+    if (rf_pred <= q1) | (rf_pred >= q3):
         print(f"Estimated Price: {np.exp(q2)} /n Low Price: {q1}, High Price: {q3}")
         return float(q1), float(q2), float(q3), reliability_index
     else:
-        print(f"Estimated Price: {rf_pred_exp} /n Low Price: {q1}, High Price: {q3}")
-        return float(q1), float(rf_pred_exp), float(q3), reliability_index
+        print(f"Estimated Price: {rf_pred} /n Low Price: {q1}, High Price: {q3}")
+        return float(q1), float(rf_pred), float(q3), reliability_index