done

predictorK.py

@@ -18,19 +18,21 @@ def load_components_function(fp):
     #To load the machine learning components saved to re-use in the app
     return tf.keras.models.load_model(fp)
 
-def load_components(fp):
+# def load_components(fp):
     #To load the machine learning components saved to re-use in the app
-    with open(fp,"rb") as f:
-        object = pickle.load(f)
-    return object
+
+    # return object
 
 # Loading the machine learning components
 def app():
-    DIRPATH = os.path.dirname(os.path.realpath(__file__))
-    ml_core_fp = os.path.join(DIRPATH,"Assets","telco_model.h5")
+    # DIRPATH = os.path.dirname(os.path.realpath(__file__))
+    ml_core_fp = os.path.join("Assets","telco_model.h5")
     ml_components_dict = load_components_function(fp=ml_core_fp)
-    scaler_fp = os.path.join(DIRPATH,"Assets","scalerKer.pkl")
-    scaler_dict = load_components(fp=scaler_fp)
+    # scaler_fp = os.path.join("Assets","scalerKer.pkl")
+    with open("Assets/scalerKer.pkl","rb") as f:
+        scaler_dict = pickle.load(f)
+    #  = 
+    # load_components(fp=scaler_fp)
 
 
     st.title(":1234: Telecom Churn App")