Update app.py

app.py

@@ -142,7 +142,7 @@ def main():
 
             with st.spinner("Analyzing..."):
                 #with col2:
-                st.markdown("<br><br><br><br>", unsafe_allow_html=True)
+                #st.markdown("<br><br><br><br>", unsafe_allow_html=True)
                 # train kmeans model
                 cluster_model = create_model(selected_model, num_clusters = selected_clusters)
 
@@ -207,123 +207,123 @@ def main():
                         st.bar_chart(feat_imp.set_index('Feature'))
 
     elif page == "Anomaly Detection":
-        with col1:
-            st.header('Anomaly Detection')
+        #with col1:
+        st.header('Anomaly Detection')
 
-            st.write(
-                """
-                """
-            )
-
-            # import pycaret anomaly
-            from pycaret.anomaly import setup, create_model, assign_model, pull, plot_model
-            # import AnomalyExperiment
-            from pycaret.anomaly import AnomalyExperiment
-
-            # Display the list of CSV files
-            directory = "./"
-            all_files = os.listdir(directory)
-            # Filter files to only include CSV files
-            csv_files = [file for file in all_files if file.endswith(".csv")]
-            # Select a CSV file from the list
-            selected_csv = st.selectbox("Select a CSV file from the list", ["None"] + csv_files)
+        st.write(
+            """
+            """
+        )
+
+        # import pycaret anomaly
+        from pycaret.anomaly import setup, create_model, assign_model, pull, plot_model
+        # import AnomalyExperiment
+        from pycaret.anomaly import AnomalyExperiment
+
+        # Display the list of CSV files
+        directory = "./"
+        all_files = os.listdir(directory)
+        # Filter files to only include CSV files
+        csv_files = [file for file in all_files if file.endswith(".csv")]
+        # Select a CSV file from the list
+        selected_csv = st.selectbox("Select a CSV file from the list", ["None"] + csv_files)
+        
+        # Upload the CSV file
+        uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+
+        # Define the unsupervised model
+        anomalymodel = ['abod', 'cluster', 'cof', 'iforest', 'histogram', 'knn', 'lof', 'svm', 'pca', 'mcd', 'sod', 'sos']
+        selected_model = st.selectbox("Choose an anomaly model", anomalymodel)
+
+        # Read and display the CSV file
+        if selected_csv != "None" or uploaded_file is not None:
+            if uploaded_file:
+                try:
+                    delimiter = ','
+                    insurance_claims = pd.read_csv (uploaded_file, sep=delimiter)
+                except ValueError:
+                    delimiter = '|'
+                    insurance_claims = pd.read_csv (uploaded_file, sep=delimiter, encoding='latin-1')
+            else:
+                insurance_claims = pd.read_csv(selected_csv)
+
+            num_rows = int(insurance_claims.shape[0]*int(num_lines)/100)
+            insurance_claims_reduced = insurance_claims.head(num_rows)
+            st.write("Rows to be processed: " + str(num_rows))
+
+            all_columns = insurance_claims_reduced.columns.tolist()
+            selected_columns = st.multiselect("Choose columns", all_columns, default=all_columns)
+            insurance_claims_reduced = insurance_claims_reduced[selected_columns].copy()
+
+            with st.expander("Inference Description", expanded=True):
+                insurance_claims_reduced.describe().T
+
+            with st.expander("Head Map", expanded=True):
+                cat_col = insurance_claims_reduced.select_dtypes(include=['object']).columns
+                num_col = insurance_claims_reduced.select_dtypes(exclude=['object']).columns
+
+                # insurance_claims[num_col].hist(bins=15, figsize=(20, 15), layout=(5, 4))
+                # Calculate the correlation matrix
+                corr_matrix = insurance_claims_reduced[num_col].corr()
+                # Create a Matplotlib figure
+                fig, ax = plt.subplots(figsize=(12, 8))
+                # Create a heatmap using seaborn
+                #st.header("Heat Map")
+                sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f', ax=ax)
+                # Set the title for the heatmap
+                ax.set_title('Correlation Heatmap')
+                # Display the heatmap in Streamlit
+                st.pyplot(fig)
             
-            # Upload the CSV file
-            uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
-
-            # Define the unsupervised model
-            anomalymodel = ['abod', 'cluster', 'cof', 'iforest', 'histogram', 'knn', 'lof', 'svm', 'pca', 'mcd', 'sod', 'sos']
-            selected_model = st.selectbox("Choose an anomaly model", anomalymodel)
-
-            # Read and display the CSV file
-            if selected_csv != "None" or uploaded_file is not None:
-                if uploaded_file:
-                    try:
-                        delimiter = ','
-                        insurance_claims = pd.read_csv (uploaded_file, sep=delimiter)
-                    except ValueError:
-                        delimiter = '|'
-                        insurance_claims = pd.read_csv (uploaded_file, sep=delimiter, encoding='latin-1')
-                else:
-                    insurance_claims = pd.read_csv(selected_csv)
-
-                num_rows = int(insurance_claims.shape[0]*int(num_lines)/100)
-                insurance_claims_reduced = insurance_claims.head(num_rows)
-                st.write("Rows to be processed: " + str(num_rows))
-
-                all_columns = insurance_claims_reduced.columns.tolist()
-                selected_columns = st.multiselect("Choose columns", all_columns, default=all_columns)
-                insurance_claims_reduced = insurance_claims_reduced[selected_columns].copy()
-
-                with st.expander("Inference Description", expanded=True):
-                    insurance_claims_reduced.describe().T
-
-                with st.expander("Head Map", expanded=True):
-                    cat_col = insurance_claims_reduced.select_dtypes(include=['object']).columns
-                    num_col = insurance_claims_reduced.select_dtypes(exclude=['object']).columns
-
-                    # insurance_claims[num_col].hist(bins=15, figsize=(20, 15), layout=(5, 4))
-                    # Calculate the correlation matrix
-                    corr_matrix = insurance_claims_reduced[num_col].corr()
-                    # Create a Matplotlib figure
-                    fig, ax = plt.subplots(figsize=(12, 8))
-                    # Create a heatmap using seaborn
-                    #st.header("Heat Map")
-                    sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f', ax=ax)
-                    # Set the title for the heatmap
-                    ax.set_title('Correlation Heatmap')
-                    # Display the heatmap in Streamlit
-                    st.pyplot(fig)
-                
-                if st.button("Prediction"):
-                
-                    s = setup(insurance_claims_reduced, session_id = 123, remove_multicollinearity=p_remove_multicollinearity, multicollinearity_threshold=p_multicollinearity_threshold,
-                                # remove_outliers=p_remove_outliers, outliers_method=p_outliers_method, 
-                                transformation=p_transformation, 
-                                normalize=p_normalize, pca=p_pca, pca_method=p_pca_method)
-
-                    exp_anomaly = AnomalyExperiment()
-                    # init setup on exp
-                    exp_anomaly.setup(insurance_claims_reduced, session_id = 123)
-                
-                    with st.spinner("Analyzing..."):
-                        with col2:
-                            st.markdown("<br><br><br><br>", unsafe_allow_html=True)
-                            # train model
-                            anomaly_model = create_model(selected_model)
-
-                            with st.expander("Assign Model", expanded=False):
-                                #st.header("Assign Model")
-                                anomaly_model_2 = assign_model(anomaly_model)
-                                anomaly_model_2
-
-                            with st.expander("Anomaly Metrics", expanded=False):
-                                #st.header("Anomaly Metrics")
-                                anomaly_results = pull()
-                                anomaly_results
-
-                            with st.expander("Anomaly Plots", expanded=False):
-                                if graph_select:
-                                    # plot
-                                    #st.header("Anomaly Plots")
-                                    plot_model(anomaly_model, plot = 'tsne', display_format = 'streamlit')
-                                    plot_model(anomaly_model, plot = 'umap', display_format = 'streamlit')
-
-                            with st.expander("Feature Importance", expanded=False):
-                                if graph_select and feat_imp_select:
-                                    # Create a Classification Model to extract feature importance
-                                    #st.header("Feature Importance")
-                                    from pycaret.classification import setup, create_model, get_config
-                                    s = setup(anomaly_model_2, target = 'Anomaly')
-                                    lr = create_model('lr')
-                                    # this is how you can recreate the table
-                                    feat_imp = pd.DataFrame({'Feature': get_config('X_train').columns, 'Value' : abs(lr.coef_[0])}).sort_values(by='Value', ascending=False)
-                                    # sort by feature importance value and filter top 10
-                                    feat_imp = feat_imp.sort_values(by='Value', ascending=False).head(10)
-                                    # Display the filtered table in Streamlit
-                                    # st.dataframe(feat_imp)
-                                    # Display the filtered table as a bar chart in Streamlit
-                                    st.bar_chart(feat_imp.set_index('Feature'))
+            if st.button("Prediction"):
+            
+                s = setup(insurance_claims_reduced, session_id = 123, remove_multicollinearity=p_remove_multicollinearity, multicollinearity_threshold=p_multicollinearity_threshold,
+                            # remove_outliers=p_remove_outliers, outliers_method=p_outliers_method, 
+                            transformation=p_transformation, 
+                            normalize=p_normalize, pca=p_pca, pca_method=p_pca_method)
+
+                exp_anomaly = AnomalyExperiment()
+                # init setup on exp
+                exp_anomaly.setup(insurance_claims_reduced, session_id = 123)
+            
+                with st.spinner("Analyzing..."):
+                    #with col2:
+                    #st.markdown("<br><br><br><br>", unsafe_allow_html=True)
+                    # train model
+                    anomaly_model = create_model(selected_model)
+
+                    with st.expander("Assign Model", expanded=False):
+                        #st.header("Assign Model")
+                        anomaly_model_2 = assign_model(anomaly_model)
+                        anomaly_model_2
+
+                    with st.expander("Anomaly Metrics", expanded=False):
+                        #st.header("Anomaly Metrics")
+                        anomaly_results = pull()
+                        anomaly_results
+
+                    with st.expander("Anomaly Plots", expanded=False):
+                        if graph_select:
+                            # plot
+                            #st.header("Anomaly Plots")
+                            plot_model(anomaly_model, plot = 'tsne', display_format = 'streamlit')
+                            plot_model(anomaly_model, plot = 'umap', display_format = 'streamlit')
+
+                    with st.expander("Feature Importance", expanded=False):
+                        if graph_select and feat_imp_select:
+                            # Create a Classification Model to extract feature importance
+                            #st.header("Feature Importance")
+                            from pycaret.classification import setup, create_model, get_config
+                            s = setup(anomaly_model_2, target = 'Anomaly')
+                            lr = create_model('lr')
+                            # this is how you can recreate the table
+                            feat_imp = pd.DataFrame({'Feature': get_config('X_train').columns, 'Value' : abs(lr.coef_[0])}).sort_values(by='Value', ascending=False)
+                            # sort by feature importance value and filter top 10
+                            feat_imp = feat_imp.sort_values(by='Value', ascending=False).head(10)
+                            # Display the filtered table in Streamlit
+                            # st.dataframe(feat_imp)
+                            # Display the filtered table as a bar chart in Streamlit
+                            st.bar_chart(feat_imp.set_index('Feature'))
 try:
     main()
 except Exception as e: