Update app.py

app.py

@@ -30,6 +30,7 @@ import time
 from sklearn.cluster import KMeans
 import scipy.stats as stats
 import mimetypes
+import shap
 import matplotlib.pyplot as plt
 from sklearn.model_selection import learning_curve
 
@@ -869,6 +870,7 @@ elif app_mode == "Model Training":
                      st.write(f"Accuracy: {accuracy:.4f}")
             except Exception as e: #local error
                  st.error(f"An error occurred during model evaluation: {e}")
+                
 elif app_mode == "Predictions":
     st.title("🔮 Make Predictions")
 
@@ -883,7 +885,7 @@ elif app_mode == "Predictions":
         except AttributeError as e:
             st.error(f"Error accessing model transformers: {e}. Please ensure a valid model is trained and loaded.")
             st.stop()
-        
+
         model_is_classification = hasattr(model, 'predict_proba')  # Check for classification or other problem
         if not set(model_columns).issubset(set(df.columns)): #Fixed comparison
             st.error("The model was trained on a dataframe that contains different columns than the currently uploaded dataframe. Please upload the correct dataframe.")
@@ -897,7 +899,7 @@ elif app_mode == "Predictions":
             else:
                 input_data[col] = st.selectbox(f"Select {col}", df[col].unique())
 
-        # Make Prediction Button
+        # Prediction Button
         if st.button("Make Prediction"):
             try:
                 input_df = pd.DataFrame([input_data])
@@ -924,17 +926,17 @@ elif app_mode == "Predictions":
 
                         shap.initjs()
                         fig_shap, ax_shap = plt.subplots(1, figsize = (10,10))
-                        shap.summary_plot(shap_values, features = X_train, feature_names = feature_columns, plot_type = "bar")#plot for multi class labels
+                        shap.summary_plot(shap_values, features = input_processed, feature_names = model_columns, plot_type = "bar")#plot for multi class labels
                         st.pyplot(fig_shap) #Show the figure
                     except Exception as e:
                         st.write(f"Can show shap values on tree based model: {e}") #Show error
                  # Additional Feedback (Example for Classification)
                 if hasattr(st.session_state.model.steps[-1][1], 'predict_proba'): #If the end variable has predict_proba and is therefore a predictor
-                    probabilities = st.session_state.model.predict_proba(input_df)[0]
+                    probabilities = st.session_state.model.predict_proba(input_processed)[0]
                     st.write("Predicted Probabilities:")
                     st.write(probabilities) #write here
             except Exception as e:
-                st.error(f"An error occurred during prediction: {e}") #Base case error
+                st.error(f"An error occurred during prediction: {e}")
 
         #Add batch prediction section in prediction tab
         st.subheader("Batch Predictions")