Update app.py

app.py

@@ -35,8 +35,8 @@ STD_BANKS = 3.0158890435512125
 # with open("xgb_model(1).json", "r") as f:
 #     params = json.load(f)
 
-xgb_model = XGBRegressor()
-xgb_model.load_model("xgb_model_2.json")
+# xgb_model = XGBRegressor()
+# xgb_model.load_model("xgb_model_2.json")
 
 def predict_score(lat, lon, api_key):
     # Convert input to tensor
@@ -54,7 +54,8 @@ def predict_score(lat, lon, api_key):
 
     mu_pred = mu_pred.numpy().flatten()
 
-    mu_pred2 = xgb_model.predict(inputs.unsqueeze(0).numpy())
+    # mu_pred2 = xgb_model.predict(inputs.unsqueeze(0).numpy())
+    mu_pred2 = 1
 
     # r = 1/alpha
     # p = r / (r + mu_pred)
@@ -83,7 +84,7 @@ def predict_score(lat, lon, api_key):
 
     # score = np.sigmoid(mu_pred2 - num_banks + 0.1) * 100
 
-    score =  100 / (1 + np.exp(num_banks - mu_pred2))
+    score =  100 / (1 + np.exp(num_banks - mu_pred))
 
     # You can apply any post-processing here
     return (