Update app.py

app.py

@@ -85,12 +85,13 @@ def Load_ml_items(relative_path):
     return loaded_object
 
 
-loaded_object = Load_ml_items('ML_items')
+loaded_object = Load_ml_items('ml_items_1')
+
 
 
     #return loaded_object
-Loaded_object = Load_ml_items('ML_items')
-model, encoder, train_data, stores, holidays_event = Loaded_object['model'], Loaded_object['encoder'], Loaded_object['train_data'], Loaded_object['stores'], Loaded_object['holidays_event']
+Loaded_object = Load_ml_items('ml_items_1')
+pipeline, stores, holidays_event = Loaded_object['pipeline'], Loaded_object['stores'], Loaded_object['holidays_event']
 
 # Setting Function for extracting Calendar features
 @st.cache(allow_output_mutation=True)
@@ -208,20 +209,33 @@ if submitted:
     processed_data= getDateFeatures(df, 'date')
     processed_data= processed_data.drop(columns=['date'])
     
-    # Encoding Categorical Variables
-    encoder = preprocessing.LabelEncoder()
-    cols = ['family', 'city', 'state', 'store_type', 'locale', 'locale_name', 'day_type']
-    for col in cols:
-        processed_data[col] = encoder.fit_transform(processed_data[col])
-    
-    # Making Predictions
-    def predict(X, model= Loaded_object['model']):
-        results = model.predict(X)
-        return results
+    # # Encoding Categorical Variables
+    # encoder = preprocessing.LabelEncoder()
+    # cols = ['family', 'city', 'state', 'store_type', 'locale', 'locale_name', 'day_type']
+    # for col in cols:
+    #     processed_data[col] = encoder.fit_transform(processed_data[col])
     
-    prediction = predict(processed_data, model= Loaded_object['model'])
+    # # Making Predictions
+    # def predict(X, model= Loaded_object['model']):
+    #     results = model.predict(X)
+    #     return results
+        
+     #Making predictions
+    prediction = pipeline.predict(processed_data)
     df['Sales']= prediction 
     
+    # # Displaying prediction results
+    # st.markdown('''---''')
+    # st.markdown("<h4 style='text-align: center;'> Prediction Results </h4> ", unsafe_allow_html=True)
+    # st.success(f"Predicted Sales: {prediction[-1]}")
+    # st.markdown('''---''')
+
+
+
+        
+    # prediction = predict(processed_data, model= Loaded_object['model'])
+    # df['Sales']= prediction 
+    
     
     # Displaying prediction results
     st.markdown('''---''')