Update app.py

app.py

@@ -93,8 +93,8 @@ with gr.Blocks() as demo:
             generate_btn = gr.Button("Generate Model Points", variant="primary")
 
         with gr.Column(scale=2):
-            model_points_display = gr.Dataframe(label="Generated Model Points", wrap=True, height=400)
-            download_excel_btn = gr.DownloadButton(label="Download Excel", variant="secondary") # Removed value, will be set by click
+            model_points_display = gr.Dataframe(label="Generated Model Points", wrap=True) # CORRECTED: Removed height=400
+            download_excel_btn = gr.DownloadButton(label="Download Excel", variant="secondary")
             gr.Markdown("---")
             gr.Markdown("## 📊 Data Summary & Analysis")
             with gr.Tabs():
@@ -104,8 +104,6 @@ with gr.Blocks() as demo:
                     gr.Markdown("Distribution of **Sum Assured**. Data is generated uniformly; a normal curve is fitted for illustration.")
                     distribution_plot_display = gr.Plot()
                 with gr.TabItem("Categorical Summary"):
-                    # For simplicity, we'll display sex distribution here.
-                    # You might want to add another Dataframe for policy_term or combine them.
                     sex_summary_display = gr.Dataframe(label="Sex Distribution", wrap=True)
                     policy_term_summary_display = gr.Dataframe(label="Policy Term Distribution", wrap=True)
 
@@ -119,7 +117,6 @@ with gr.Blocks() as demo:
         no_data_df_num = pd.DataFrame({'Message': ["No data generated or summary failed."]})
         no_data_df_cat = pd.DataFrame({'Message': ["No data generated or summary failed."]})
 
-
         current_df = df_state.value if df_state.value is not None else pd.DataFrame()
 
         if int(age_m) >= int(age_mx):
@@ -130,28 +127,27 @@ with gr.Blocks() as demo:
             return current_df, df_state.value, pd.DataFrame({'Error': ["Minimum Sum Assured must be less than Maximum Sum Assured."]}), empty_plot, pd.DataFrame({'Error': ["Minimum Sum Assured must be less than Maximum Sum Assured."]}), pd.DataFrame({'Error': ["Minimum Sum Assured must be less than Maximum Sum Assured."]})
         if not p_terms:
             gr.Warning("At least one Policy Term must be selected. Using defaults.")
-            p_terms = [10, 15, 20] # Apply default if none selected
+            p_terms = [10, 15, 20]
 
         gr.Info("Generating model points... Please wait.")
         try:
             df = generate_custom_model_points(mp_c, s, age_m, age_mx, sa_m, sa_mx, p_terms, incl_sex, pc_fixed)
             gr.Info(f"{len(df)} model points generated successfully!")
 
-            # Numerical Summary
-            numerical_cols = ['age_at_entry', 'sum_assured', 'duration_mth', 'policy_count']
-            existing_numerical_cols = [col for col in numerical_cols if col in df.columns]
             desc_stats_df = no_data_df_num
-            if existing_numerical_cols and not df.empty:
-                desc_stats = df[existing_numerical_cols].describe().transpose()
-                if 'count' in desc_stats.columns:
-                    desc_stats['count'] = desc_stats['count'].astype(int)
-                # Gradio Dataframe handles formatting, so raw numbers are fine
-                desc_stats_df = desc_stats.reset_index().rename(columns={'index': 'Statistic'})
+            if not df.empty:
+                numerical_cols = ['age_at_entry', 'sum_assured', 'duration_mth', 'policy_count']
+                existing_numerical_cols = [col for col in numerical_cols if col in df.columns]
+                if existing_numerical_cols:
+                    desc_stats = df[existing_numerical_cols].describe().transpose()
+                    if 'count' in desc_stats.columns:
+                        desc_stats['count'] = desc_stats['count'].astype(int)
+                    desc_stats_df = desc_stats.reset_index().rename(columns={'index': 'Statistic'})
+                else:
+                    desc_stats_df = pd.DataFrame({'Message': ["No numerical columns found for summary."]})
             elif df.empty:
                  desc_stats_df = pd.DataFrame({'Message': ["DataFrame is empty, no numerical data to describe."]})
 
-
-            # Distribution Plot
             fig = empty_plot
             if 'sum_assured' in df.columns and not df['sum_assured'].empty:
                 fig, ax = plt.subplots(figsize=(8, 5))
@@ -167,9 +163,7 @@ with gr.Blocks() as demo:
                 ax.legend()
                 ax.grid(axis='y', linestyle='--', alpha=0.7)
                 plt.tight_layout()
-            # Removed plot_object = fig, fig is directly returned
 
-            # Categorical Summary
             sex_counts_df = no_data_df_cat
             term_counts_df = no_data_df_cat
 
@@ -177,7 +171,7 @@ with gr.Blocks() as demo:
                 if 'sex' in df.columns:
                     sex_counts = df['sex'].value_counts().reset_index()
                     sex_counts.columns = ['Sex', 'Count']
-                    sex_counts['Percentage'] = (sex_counts['Count'] / sex_counts['Count'].sum() * 100).round(2) # Keep as number for potential sorting in Dataframe
+                    sex_counts['Percentage'] = (sex_counts['Count'] / sex_counts['Count'].sum() * 100).round(2)
                     sex_counts_df = sex_counts
                 else:
                     sex_counts_df = pd.DataFrame({'Message': ["'sex' column not found or data is empty."]})
@@ -185,7 +179,7 @@ with gr.Blocks() as demo:
                 if 'policy_term' in df.columns:
                     term_counts = df['policy_term'].value_counts().sort_index().reset_index()
                     term_counts.columns = ['Policy Term (Years)', 'Count']
-                    term_counts['Percentage'] = (term_counts['Count'] / term_counts['Count'].sum() * 100).round(2) # Keep as number
+                    term_counts['Percentage'] = (term_counts['Count'] / term_counts['Count'].sum() * 100).round(2)
                     term_counts_df = term_counts
                 else:
                     term_counts_df = pd.DataFrame({'Message': ["'policy_term' column not found or data is empty."]})
@@ -193,7 +187,6 @@ with gr.Blocks() as demo:
                  sex_counts_df = pd.DataFrame({'Message': ["DataFrame is empty, no categorical data for 'sex'."]})
                  term_counts_df = pd.DataFrame({'Message': ["DataFrame is empty, no categorical data for 'policy_term'."]})
 
-
             return df, df, desc_stats_df, fig, sex_counts_df, term_counts_df
 
         except Exception as e:
@@ -205,18 +198,13 @@ with gr.Blocks() as demo:
     def handle_download_button_click(current_df_to_download):
         if current_df_to_download is None or not isinstance(current_df_to_download, pd.DataFrame) or current_df_to_download.empty:
             gr.Warning("No data available to download. Generate model points first.")
-            # Return a dummy file path or None if Gradio handles it gracefully for gr.DownloadButton
-            # For safety, returning a named file path that won't be created avoids errors.
-            return gr.DownloadButton.update(interactive=False) # Or simply return None if that works for your Gradio version.
+            return gr.DownloadButton.update(interactive=False)
 
         excel_output = io.BytesIO()
-        # Ensure 'Statistic' column from describe().transpose().reset_index() is handled if it exists
-        # The main df (current_df_to_download) should be fine as is.
         current_df_to_download.to_excel(excel_output, sheet_name='ModelPoints', engine='xlsxwriter', index=False)
         excel_output.seek(0)
-        # When returning a file for gr.DownloadButton, Gradio expects a file path or a BytesIO object
-        # The 'value' of the DownloadButton component itself is not what's downloaded.
-        # The function should return the file object.
+        # Returning a BytesIO object directly for file download
+        # Provide a filename for the browser
         return gr.File(file_path=excel_output, file_name="model_points.xlsx", visible=True)
 
 
@@ -235,13 +223,10 @@ with gr.Blocks() as demo:
         ]
     )
 
-    # The DownloadButton's click function should return the file object to the button itself.
-    # The `value` parameter in gr.DownloadButton is for the initial filename,
-    # but the actual file content comes from the function's return.
     download_excel_btn.click(
         fn=handle_download_button_click,
         inputs=[df_state],
-        outputs=[download_excel_btn] # The output should be the button itself to receive the file
+        outputs=[download_excel_btn]
     )
 
 if __name__ == "__main__":