Update app.py

app.py

@@ -23,11 +23,8 @@ def get_lat_lon(address,api_key=API_KEY):
 def compute_reliability(std_pred: float):
     scores = pd.read_csv('scores_13072024.csv').iloc[:,1].tolist()
     print(scores)
-    ct = 5
+    ct = 10
     for std_set in scores:
-        print(ct)
-        print(std_set)
-        print(std_pred)
         if std_pred < std_set:
             reliability = ct
         else:
@@ -37,11 +34,9 @@ def compute_reliability(std_pred: float):
 
 def predict_price(date: datetime ,room_count: int, address:str, surface: float , property_type: str ):
     date = int(datetime.timestamp(date))
-    print(date)
     latitude, longitude = get_lat_lon(address)
     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 = 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))
@@ -50,10 +45,8 @@ def predict_price(date: datetime ,room_count: int, address:str, surface: float ,
     q2 = np.exp(np.quantile(predictions_all, 0.5))
     q3 = np.exp(np.quantile(predictions_all, 0.75))
     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} /n Low Price: {q1}, High Price: {q3}")
         return float(q1), float(rf_pred), float(q3), reliability_index