Update app.py

app.py

@@ -121,35 +121,53 @@ def main():
 
     elif app_mode == "Predict Conversion":
         st.subheader("Predict Conversion")
-        # Upload ML config file using Streamlit's file_uploader function
-        mlf_file = st.file_uploader("Upload ML config file", type=["properties"])
+    
+        # Create an instance of MarkovChainClassifier with the ML config file
+        model = MarkovChainClassifier("cc.mod")
+    
+        # Get user input for userID
+        user_id = st.text_input("Enter User ID")
+        
+        # Check if the "Predict" button was clicked
+        if st.button("Predict"):
+            
+        # Call the predict method of the MarkovChainClassifier instance
+            pred = model.predict()
+            if pred == 'T':
+                st.write(f"UserID: {user_id}, Prediction: Visitor is likely to convert into a customer.")
+            else:
+                st.write(f"UserID: {user_id}, Prediction: Visitor is unlikely to convert into a customer.")
+            
+        # st.subheader("Predict Conversion")
+        # # Upload ML config file using Streamlit's file_uploader function
+        # mlf_file = st.file_uploader("Upload ML config file", type=["properties"])
 
-        # Check if ML config file was uploaded
-        if mlf_file is not None:
-            # Save the uploaded file to a local file
-            with open("mcclf_cc.properties", "wb") as f:
-                f.write(mlf_file.read())
+        # # Check if ML config file was uploaded
+        # if mlf_file is not None:
+        #     # Save the uploaded file to a local file
+        #     with open("mcclf_cc.properties", "wb") as f:
+        #         f.write(mlf_file.read())
 
-            # Create an instance of MarkovChainClassifier with the uploaded ML config file
-            model = MarkovChainClassifier("mcclf_cc.properties")
+        #     # Create an instance of MarkovChainClassifier with the uploaded ML config file
+        #     model = MarkovChainClassifier("mcclf_cc.properties")
 
-            # # Load the model from cc.mod
-            # model = MarkovChainClassifier.load_model("cc.mod")
+        #     # # Load the model from cc.mod
+        #     # model = MarkovChainClassifier.load_model("cc.mod")
 
-            # Get user input for userID
-            user_id = st.text_input("Enter User ID")
+        #     # Get user input for userID
+        #     user_id = st.text_input("Enter User ID")
             
-            # Check if the "Predict" button was clicked
-            if st.button("Predict"):
-                # Load the saved model
-                # model.load_model("cc.mod")
+        #     # Check if the "Predict" button was clicked
+        #     if st.button("Predict"):
+        #         # Load the saved model
+        #         # model.load_model("cc.mod")
                 
-                # Call the predict method of the MarkovChainClassifier instance
-                pred = model.predict()
-                if pred == 'T':
-                    st.write(f"UserID: {user_id}, Prediction: Visitor is likely to convert into a customer.")
-                else:
-                    st.write(f"UserID: {user_id}, Prediction: Visitor is unlikely to convert into a customer.")
+        #         # Call the predict method of the MarkovChainClassifier instance
+        #         pred = model.predict()
+        #         if pred == 'T':
+        #             st.write(f"UserID: {user_id}, Prediction: Visitor is likely to convert into a customer.")
+        #         else:
+        #             st.write(f"UserID: {user_id}, Prediction: Visitor is unlikely to convert into a customer.")
 
 if __name__ == "__main__":
     main()