Update app.py

app.py

@@ -110,115 +110,114 @@ if uploaded_file is not None:
         if st.button("See Your Feature Details"):
             open_dialog()
 
-    if st.button("Generate Plots"):
-        # Generate the Plots
-        with st.spinner("Generating Plots.....", show_time= True):
-            if categorical_ls:
-                st.header("📊 Categorical Plots")
-                # 1. Count Plots
-                count_plots = []
-                for x_col in categorical_ls:
-                    if df[x_col].nunique() <= 20:
-                        count_plots.extend(generate_count_plots(df, x_col))
-                
-                if count_plots:
-                    st.subheader("Count Plots :-")
-                    st.plotly_chart(combine_figures_as_subplots(count_plots), use_container_width=True)
-                
-                # 2. Bar Plots
-                bar_plots = []
-                # (Categorical vs Discrete + Continuous)
-                for x_col in categorical_ls:
-                    if df[x_col].nunique() <= 20:
-                        bar_plots.extend(generate_bar_plots(df, x_col, discrete_ls + continuous_ls))
-    
-                if bar_plots:
-                    st.subheader("Bar Plots :-")
-                    st.plotly_chart(combine_figures_as_subplots(bar_plots), use_container_width=True)
-                
-                # 3. Grouped Bar Plots
-                grp_bar_plots = []
-                # (Categorical vs Discrete + Continuous)
-                grp_bar_plots.extend(generate_grouped_bar_plots(df, categorical_ls, discrete_ls + continuous_ls))
-    
-                if grp_bar_plots:
-                    st.subheader("Grouped Bar Plots :-")
-                    st.plotly_chart(combine_figures_as_subplots(grp_bar_plots), use_container_width=True)
-                
-                # 4. Pie Charts
-                pie_plots = []
-                for x_col in categorical_ls:
-                    if df[x_col].nunique() <= 20:
-                        pie_plots.extend(generate_pie_plots(df, x_col))
-    
-                if pie_plots:
-                    st.subheader("Pie Charts :-")
-                    st.plotly_chart(combine_figures_as_subplots(pie_plots), use_container_width=True)
-    
-            if continuous_ls:
-                st.header("📊 Numerical Plots")
-                # 5. Box Plots
-                box_plots = []
-                for x_col in categorical_ls:
-                    if df[x_col].nunique() <= 10:
-                        box_plots.extend(generate_box_plots(df, x_col, continuous_ls))
-    
-                if box_plots:
-                    st.subheader("Box Plots :-")
-                    st.plotly_chart(combine_figures_as_subplots(box_plots), use_container_width=True)
-                
-                # 6. Heat Maps
-                heat_maps = []
-                if task == 'Regression':
-                    if categorical_ls:
-                        heat_maps.extend(generate_categorical_correlation_heatmap(df, target_col, categorical_ls))
-                heat_maps.extend(generate_numeric_correlation_heatmap(df[continuous_ls]))
-                if heat_maps: 
-                    st.subheader("Heat Maps :-")
-                    st.plotly_chart(combine_figures_as_subplots(heat_maps), use_container_width=True)
-                
-                # 7. Scatter Plots
-                if len(continuous_ls) >= 2:
-                    st.subheader("Scatter Plots")
-                scatter_plots = []
-                # Creation of unique feature pairs (no repetition like (B, A) if (A, B) is already used)
-                feature_pairs = []
-                for i in range(len(continuous_ls)):
-                    for j in range(i + 1, len(continuous_ls)):
-                        feature_pairs.append((continuous_ls[i], continuous_ls[j]))
-                selection = st.pills("Highlight using a categorical feature :- ", categorical_ls)
-                scatter_plots.extend(generate_scatter_plots(df, feature_pairs, selection))
-                if scatter_plots:
-                    st.plotly_chart(combine_figures_as_subplots(scatter_plots), use_container_width=True)
-                
-                # 8. Histograms
-                histograms = []
-                histograms.extend(generate_histograms(df, continuous_ls))
-    
-                if histograms:
-                    st.subheader("Histograms")
-                    st.plotly_chart(combine_figures_as_subplots(histograms), use_container_width=True)
-                
-                # 9. Line Plots
-                line_plots = []
-                if date_time_ls:
-                    # Extract only date-related components
-                    date_related_keywords = ['_year', '_month', '_day', '_weekday']
-                    date_component_cols = [col for col in extracted_datetime if any(key in col for key in date_related_keywords)]
-                    if date_component_cols:
-                        st.subheader("Line Plots :-")
-                        # Mapping of labels to values
-                        time_grouping_options = {
-                            "Daily": "D",
-                            "Weekly": "W",
-                            "Monthly": "ME",
-                            "Yearly": "YE"
-                        }
-                        time_choice  = st.pills("Choose time interval for grouping :- ", list(time_grouping_options.keys()))
-                        # Extract the actual value for resampling
-                        selected_freq = time_grouping_options[time_choice] if time_choice else "ME"  # Use default "ME" if no selection
-                        line_plots.extend(generate_line_plots(df, date_component_cols, continuous_ls, selected_freq))
-                
-                if line_plots:
-                    st.plotly_chart(combine_figures_as_subplots(line_plots), use_container_width= True)
-    
+    # if st.button("Generate Plots"):
+    # Generate the Plots
+    with st.spinner("Generating Plots.....", show_time= True):
+        if categorical_ls:
+            st.header("📊 Categorical Plots")
+            # 1. Count Plots
+            count_plots = []
+            for x_col in categorical_ls:
+                if df[x_col].nunique() <= 20:
+                    count_plots.extend(generate_count_plots(df, x_col))
+            
+            if count_plots:
+                st.subheader("Count Plots :-")
+                st.plotly_chart(combine_figures_as_subplots(count_plots), use_container_width=True)
+            
+            # 2. Bar Plots
+            bar_plots = []
+            # (Categorical vs Discrete + Continuous)
+            for x_col in categorical_ls:
+                if df[x_col].nunique() <= 20:
+                    bar_plots.extend(generate_bar_plots(df, x_col, discrete_ls + continuous_ls))
+
+            if bar_plots:
+                st.subheader("Bar Plots :-")
+                st.plotly_chart(combine_figures_as_subplots(bar_plots), use_container_width=True)
+            
+            # 3. Grouped Bar Plots
+            grp_bar_plots = []
+            # (Categorical vs Discrete + Continuous)
+            grp_bar_plots.extend(generate_grouped_bar_plots(df, categorical_ls, discrete_ls + continuous_ls))
+
+            if grp_bar_plots:
+                st.subheader("Grouped Bar Plots :-")
+                st.plotly_chart(combine_figures_as_subplots(grp_bar_plots), use_container_width=True)
+            
+            # 4. Pie Charts
+            pie_plots = []
+            for x_col in categorical_ls:
+                if df[x_col].nunique() <= 20:
+                    pie_plots.extend(generate_pie_plots(df, x_col))
+
+            if pie_plots:
+                st.subheader("Pie Charts :-")
+                st.plotly_chart(combine_figures_as_subplots(pie_plots), use_container_width=True)
+
+        if continuous_ls:
+            st.header("📊 Numerical Plots")
+            # 5. Box Plots
+            box_plots = []
+            for x_col in categorical_ls:
+                if df[x_col].nunique() <= 10:
+                    box_plots.extend(generate_box_plots(df, x_col, continuous_ls))
+
+            if box_plots:
+                st.subheader("Box Plots :-")
+                st.plotly_chart(combine_figures_as_subplots(box_plots), use_container_width=True)
+            
+            # 6. Heat Maps
+            heat_maps = []
+            if task == 'Regression':
+                if categorical_ls:
+                    heat_maps.extend(generate_categorical_correlation_heatmap(df, target_col, categorical_ls))
+            heat_maps.extend(generate_numeric_correlation_heatmap(df[continuous_ls]))
+            if heat_maps: 
+                st.subheader("Heat Maps :-")
+                st.plotly_chart(combine_figures_as_subplots(heat_maps), use_container_width=True)
+            
+            # 7. Scatter Plots
+            if len(continuous_ls) >= 2:
+                st.subheader("Scatter Plots")
+            scatter_plots = []
+            # Creation of unique feature pairs (no repetition like (B, A) if (A, B) is already used)
+            feature_pairs = []
+            for i in range(len(continuous_ls)):
+                for j in range(i + 1, len(continuous_ls)):
+                    feature_pairs.append((continuous_ls[i], continuous_ls[j]))
+            selection = st.pills("Highlight using a categorical feature :- ", categorical_ls)
+            scatter_plots.extend(generate_scatter_plots(df, feature_pairs, selection))
+            if scatter_plots:
+                st.plotly_chart(combine_figures_as_subplots(scatter_plots), use_container_width=True)
+            
+            # 8. Histograms
+            histograms = []
+            histograms.extend(generate_histograms(df, continuous_ls))
+
+            if histograms:
+                st.subheader("Histograms")
+                st.plotly_chart(combine_figures_as_subplots(histograms), use_container_width=True)
+            
+            # 9. Line Plots
+            line_plots = []
+            if date_time_ls:
+                # Extract only date-related components
+                date_related_keywords = ['_year', '_month', '_day', '_weekday']
+                date_component_cols = [col for col in extracted_datetime if any(key in col for key in date_related_keywords)]
+                if date_component_cols:
+                    st.subheader("Line Plots :-")
+                    # Mapping of labels to values
+                    time_grouping_options = {
+                        "Daily": "D",
+                        "Weekly": "W",
+                        "Monthly": "ME",
+                        "Yearly": "YE"
+                    }
+                    time_choice  = st.pills("Choose time interval for grouping :- ", list(time_grouping_options.keys()))
+                    # Extract the actual value for resampling
+                    selected_freq = time_grouping_options[time_choice] if time_choice else "ME"  # Use default "ME" if no selection
+                    line_plots.extend(generate_line_plots(df, date_component_cols, continuous_ls, selected_freq))
+            
+            if line_plots:
+                st.plotly_chart(combine_figures_as_subplots(line_plots), use_container_width= True)