Update app.py

app.py

@@ -59,152 +59,152 @@ def main():
 
     st.title('ITACA Insurance Core AI Module')
 
-    col1, col2 = st.columns(2)
+    #col1, col2 = st.columns(2)
 
     if page == "Clustering Analysis":
-        with col1:
-            st.header('Clustering Analysis')
-
-            st.write(
-                """
-                """
-            )
-            # import pycaret unsupervised models
-            from pycaret.clustering import setup, create_model, assign_model, pull, plot_model
-            # import ClusteringExperiment
-            from pycaret.clustering import ClusteringExperiment
-
-            # 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
-            clusteringmodel = ['kmeans', 'ap', 'meanshift', 'sc', 'hclust', 'dbscan', 'optics', 'birch']
-            selected_model = st.selectbox("Choose a clustering model", clusteringmodel)
-
-            # 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 col1:
+        st.header('Clustering Analysis')
+
+        st.write(
+            """
+            """
+        )
+        # import pycaret unsupervised models
+        from pycaret.clustering import setup, create_model, assign_model, pull, plot_model
+        # import ClusteringExperiment
+        from pycaret.clustering import ClusteringExperiment
+
+        # 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
+        clusteringmodel = ['kmeans', 'ap', 'meanshift', 'sc', 'hclust', 'dbscan', 'optics', 'birch']
+        selected_model = st.selectbox("Choose a clustering model", clusteringmodel)
+
+        # 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"):
+            #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"):
-                #insurance_claims_reduced = insurance_claims_reduced[selected_columns].copy()
+            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_clustering = ClusteringExperiment()
+            # init setup on exp
+            exp_clustering.setup(insurance_claims_reduced, session_id = 123)
+
+            with st.spinner("Analyzing..."):
+                #with col2:
+                st.markdown("<br><br><br><br>", unsafe_allow_html=True)
+                # train kmeans model
+                cluster_model = create_model(selected_model, num_clusters = selected_clusters)
+
+                cluster_model_2 = assign_model(cluster_model)
+                # Calculate summary statistics for each cluster
+                cluster_summary = cluster_model_2.groupby('Cluster').agg(['count', 'mean', 'median', 'min', 'max', 
+                                                                            'std', 'var', 'sum', ('quantile_25', lambda x: x.quantile(0.25)), 
+                                                                            ('quantile_75', lambda x: x.quantile(0.75)), 'skew'])
                 
-                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_clustering = ClusteringExperiment()
-                # init setup on exp
-                exp_clustering.setup(insurance_claims_reduced, session_id = 123)
-
-                with st.spinner("Analyzing..."):
-                    with col1:
-                        st.markdown("<br><br><br><br>", unsafe_allow_html=True)
-                        # train kmeans model
-                        cluster_model = create_model(selected_model, num_clusters = selected_clusters)
-
-                        cluster_model_2 = assign_model(cluster_model)
-                        # Calculate summary statistics for each cluster
-                        cluster_summary = cluster_model_2.groupby('Cluster').agg(['count', 'mean', 'median', 'min', 'max', 
-                                                                                    'std', 'var', 'sum', ('quantile_25', lambda x: x.quantile(0.25)), 
-                                                                                    ('quantile_75', lambda x: x.quantile(0.75)), 'skew'])
+                with st.expander("Cluster Summary", expanded=False):
+                    #st.header("Cluster Summary")
+                    cluster_summary
+
+                with st.expander("Model Assign", expanded=False):
+                    #st.header("Assign Model")
+                    cluster_model_2
+
+                # all_metrics = get_metrics()
+                # all_metrics
+
+                with st.expander("Clustering Metrics", expanded=False):
+                    #st.header("Clustering Metrics")
+                    cluster_results = pull()
+                    cluster_results
+
+                with st.expander("Clustering Plots", expanded=False):
+                    if graph_select:
+                        #st.header("Clustering Plots")
+                        # plot pca cluster plot 
+                        plot_model(cluster_model, plot = 'cluster', display_format = 'streamlit')
+                        
+                        if selected_model != 'ap':
+                            plot_model(cluster_model, plot = 'tsne', display_format = 'streamlit')
+                        
+                        if selected_model not in ('ap', 'meanshift', 'dbscan', 'optics'):
+                            plot_model(cluster_model, plot = 'elbow', display_format = 'streamlit')
+                        
+                        if selected_model not in ('ap', 'meanshift', 'sc', 'hclust', 'dbscan', 'optics'):
+                            plot_model(cluster_model, plot = 'silhouette', display_format = 'streamlit')
+                        
+                        if selected_model not in ('ap', 'sc', 'hclust', 'dbscan', 'optics', 'birch'):
+                            plot_model(cluster_model, plot = 'distance', display_format = 'streamlit')
+                        
+                        if selected_model != 'ap':
+                            plot_model(cluster_model, plot = 'distribution', display_format = 'streamlit')  
+
+                with st.expander("Feature Importance", expanded=False):
+                    # Create a Classification Model to extract feature importance
+                    if graph_select and feat_imp_select:
+                        #st.header("Feature Importance")
+                        from pycaret.classification import setup, create_model, get_config
+                        s = setup(cluster_model_2, target = 'Cluster')
+                        lr = create_model('lr')
                         
-                        with st.expander("Cluster Summary", expanded=False):
-                            #st.header("Cluster Summary")
-                            cluster_summary
-
-                        with st.expander("Model Assign", expanded=False):
-                            #st.header("Assign Model")
-                            cluster_model_2
-
-                        # all_metrics = get_metrics()
-                        # all_metrics
-
-                        with st.expander("Clustering Metrics", expanded=False):
-                            #st.header("Clustering Metrics")
-                            cluster_results = pull()
-                            cluster_results
-
-                        with st.expander("Clustering Plots", expanded=False):
-                            if graph_select:
-                                #st.header("Clustering Plots")
-                                # plot pca cluster plot 
-                                plot_model(cluster_model, plot = 'cluster', display_format = 'streamlit')
-                                
-                                if selected_model != 'ap':
-                                    plot_model(cluster_model, plot = 'tsne', display_format = 'streamlit')
-                                
-                                if selected_model not in ('ap', 'meanshift', 'dbscan', 'optics'):
-                                    plot_model(cluster_model, plot = 'elbow', display_format = 'streamlit')
-                                
-                                if selected_model not in ('ap', 'meanshift', 'sc', 'hclust', 'dbscan', 'optics'):
-                                    plot_model(cluster_model, plot = 'silhouette', display_format = 'streamlit')
-                                
-                                if selected_model not in ('ap', 'sc', 'hclust', 'dbscan', 'optics', 'birch'):
-                                    plot_model(cluster_model, plot = 'distance', display_format = 'streamlit')
-                                
-                                if selected_model != 'ap':
-                                    plot_model(cluster_model, plot = 'distribution', display_format = 'streamlit')  
-
-                        with st.expander("Feature Importance", expanded=False):
-                            # Create a Classification Model to extract feature importance
-                            if graph_select and feat_imp_select:
-                                #st.header("Feature Importance")
-                                from pycaret.classification import setup, create_model, get_config
-                                s = setup(cluster_model_2, target = 'Cluster')
-                                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'))
+                        # 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'))
 
     elif page == "Anomaly Detection":
         with col1: