remove redundant code. Update Header. Display msg for classifier.

app.py

@@ -22,7 +22,7 @@ col1, mid, col2 = st.columns([10,1,20])
 with col1:
     st.image('https://images.pexels.com/photos/2599244/pexels-photo-2599244.jpeg?auto=compress&cs=tinysrgb&w=1260&h=750&dpr=1')
 with col2:
-    st.markdown("""This tool prepares a machine learning model, using your tabular data, from scratch. The model is then used to make predictions for various combinations of the provided data to try to obtain a combination that achieves the desired target value (if possible). **Please direct any bug reports or inquiries to the <a href="http://cleanenergy.utoronto.ca/">clean energy lab at UofT</a>**""", unsafe_allow_html=True)
+    st.markdown("""This tool prepares a machine learning model, using your tabular data, from scratch. The tool can be used to quickly determine the performance of various algorithms on your dataset and/or be used to make predictions for various combinations of the provided data to try to obtain a combination that achieves the desired target value (if possible). The tool is currently under active development. **Please direct any bug reports or inquiries to the <a href="http://cleanenergy.utoronto.ca/">clean energy lab at UofT</a>**""", unsafe_allow_html=True)
 st.markdown("""---""")
 
 st.markdown(f"**To use this tool**, fill out all the requested fields from top to bottom.")
@@ -199,12 +199,6 @@ if uploaded_file:
                     ## Display top 10 rows
                     final_proposed_parameters = proposed_values_to_try[0:10]
 
-                    if len(final_proposed_parameters) == 0:
-                        st.write("No parameters could be found for the desired value based on current model. Try collecting additional data or provide a different target value.")
-                    else:
-                        st.write(final_proposed_parameters)
-                        st.download_button(label="Download the Proposed Parameters to Try", data = final_proposed_parameters.to_csv(index=False), file_name='Final_proposed_parameters.csv')
-
                 if opt_type == 'Maximize it':
                     st.subheader("Using the trained model to maximize target value:")
                     generated_preds = generated_predictions.copy()
@@ -216,12 +210,6 @@ if uploaded_file:
                     ## Display top 10 rows
                     final_proposed_parameters = generated_preds[0:10]
 
-                    if len(final_proposed_parameters) == 0:
-                        st.write("No parameters could be found for the desired value based on current model. Try collecting additional data or provide a different target value.")
-                    else:
-                        st.write(final_proposed_parameters)
-                        st.download_button(label="Download the Proposed Parameters to Try", data = final_proposed_parameters.to_csv(index=False), file_name='Final_proposed_parameters.csv')
-
                 if opt_type == 'Minimize it':
                     st.subheader("Using the trained model to minimize target value:")
                     generated_preds = generated_predictions.copy()
@@ -233,28 +221,14 @@ if uploaded_file:
                     ## Display top 10 rows
                     final_proposed_parameters = generated_preds[0:10]
 
-                    if len(final_proposed_parameters) == 0:
-                        st.write("No parameters could be found for the desired value based on current model. Try collecting additional data or provide a different target value.")
-                    else:
-                        st.write(final_proposed_parameters)
-                        st.download_button(label="Download the Proposed Parameters to Try", data = final_proposed_parameters.to_csv(index=False), file_name='Final_proposed_parameters.csv')
+                if len(final_proposed_parameters) == 0:
+                    st.write("No parameters could be found for the desired value based on current model. Try collecting additional data or provide a different target value.")
+                else:
+                    st.write(final_proposed_parameters)
+                    st.download_button(label="Download the Proposed Parameters to Try", data = final_proposed_parameters.to_csv(index=False), file_name='Final_proposed_parameters.csv')
+
 
 # ----------------------------------------------------------------------------------------------------------------------  # 
     # Build Classifier Model
-    # if mod_type == "classifier":
-    #     # Setup Classifier Problem
-    #     s = clf.setup(train_data, target = target_col, log_experiment=True, normalize=True, silent= True, experiment_name = 'QD_ML')
-
-    #     # Compare Model Performance:
-    #     st.subheader("Algorithm Selection")
-    #     with st.spinner(text="Finding the best algorithm for your model..."):
-    #         best_mod = clf.compare_models()
-    #         regression_results = clf.pull()
-
-    #     st.balloons()
-    #     st.success('Model successfully trained! Here are your results:')
-    #     st.write(regression_results)
-
-    #     # Print a SHAP Analysis Summary Plot:
-    #     st.subheader("SHAP Analysis Summary Plot")        
-    #     st.pyplot(clf.interpret_model(best_mod))
+    if mod_type == "classifier":
+        st.write('Classifier is not currently implemented.')