Update app.py

app.py

@@ -20,6 +20,9 @@ def generate_custom_model_points(
     sa_min_val = float(sa_min_val)
     sa_max_val = float(sa_max_val)
 
+    # Policy ID
+    policy_id_col = np.arange(1, mp_count_val + 1)
+
     # Issue Age
     age_at_entry = rng.integers(low=age_min_val, high=age_max_val + 1, size=mp_count_val)
 
@@ -44,7 +47,7 @@ def generate_custom_model_points(
     # Max duration is (term_in_months - 1). Min duration is 1.
     max_duration_val = policy_term_col * 12 - 1
     # Ensure max_duration_val is at least 1 to prevent issues with rng.random if term is very short
-    max_duration_val = np.maximum(1, max_duration_val) 
+    max_duration_val = np.maximum(1, max_duration_val)
     duration_mth_col = (rng.random(size=mp_count_val) * max_duration_val).astype(int) + 1
     # Ensure duration_mth does not exceed max_duration_val (especially if max_duration_val was 1)
     duration_mth_col = np.minimum(duration_mth_col, max_duration_val)
@@ -58,8 +61,9 @@ def generate_custom_model_points(
     else:
         policy_count_col_val = rng.integers(low=1, high=101, size=mp_count_val)  # Variable 1-100
 
-    # Create DataFrame
+    # Create DataFrame with policy_id as the first column
     data_dict = {
+        "policy_id": policy_id_col,
         "age_at_entry": age_at_entry,
         "sex": sex_col,
         "policy_term": policy_term_col,
@@ -67,9 +71,9 @@ def generate_custom_model_points(
         "sum_assured": sum_assured_col,
         "duration_mth": duration_mth_col
     }
-    
-    model_point_df = pd.DataFrame(data_dict, index=pd.RangeIndex(start=1, stop=mp_count_val + 1, name="policy_id"))
-    
+
+    model_point_df = pd.DataFrame(data_dict)
+
     return model_point_df
 
 # 2. Gradio App Definition
@@ -85,7 +89,7 @@ with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column(scale=1):
             gr.Markdown("### Generation Parameters")
-            
+
             mp_count_input = gr.Slider(
                 minimum=100, maximum=50000, value=10000, step=100,
                 label="Number of Model Points"
@@ -93,7 +97,7 @@ with gr.Blocks() as demo:
             seed_input = gr.Number(
                 value=12345, precision=0, label="Random Seed"
             )
-            
+
             gr.Markdown("#### Age Parameters")
             age_min_input = gr.Slider(
                 minimum=18, maximum=40, value=20, step=1, label="Minimum Age at Entry"
@@ -101,7 +105,7 @@ with gr.Blocks() as demo:
             age_max_input = gr.Slider(
                 minimum=41, maximum=80, value=59, step=1, label="Maximum Age at Entry"
             )
-            
+
             gr.Markdown("#### Sum Assured Parameters ($)")
             sum_assured_min_input = gr.Number(
                 value=10000, label="Minimum Sum Assured"
@@ -109,7 +113,7 @@ with gr.Blocks() as demo:
             sum_assured_max_input = gr.Number(
                 value=1000000, label="Maximum Sum Assured"
             )
-            
+
             gr.Markdown("#### Policy Options")
             policy_terms_input = gr.CheckboxGroup(
                 choices=[5, 10, 15, 20, 25, 30], value=[10, 15, 20],
@@ -121,13 +125,13 @@ with gr.Blocks() as demo:
             policy_count_fixed_input = gr.Checkbox(
                 value=True, label="Fixed Policy Count = 1 (Uncheck for variable count 1-100)"
             )
-            
+
             generate_btn = gr.Button("Generate Model Points", variant="primary")
 
         with gr.Column(scale=2):
             model_points_display = gr.Dataframe(label="Generated Model Points")
             download_excel_btn = gr.DownloadButton(
-                label="Download Excel", 
+                label="Download Excel",
                 value="model_points.xlsx", # Default filename
                 variant="secondary"
             )
@@ -160,9 +164,9 @@ with gr.Blocks() as demo:
             pd.DataFrame().to_excel(empty_excel_output, index=False)
             empty_excel_output.seek(0)
             return empty_excel_output
-            
+
         excel_output = io.BytesIO()
-        current_df_to_download.to_excel(excel_output, sheet_name='ModelPoints', engine='xlsxwriter', index=True)
+        current_df_to_download.to_excel(excel_output, sheet_name='ModelPoints', engine='xlsxwriter', index=False)
         excel_output.seek(0)
         return excel_output
 
@@ -172,7 +176,7 @@ with gr.Blocks() as demo:
         sum_assured_min_input, sum_assured_max_input, policy_terms_input,
         include_sex_input, policy_count_fixed_input
     ]
-    
+
     generate_btn.click(
         fn=handle_generate_button_click,
         inputs=inputs_list,
@@ -184,16 +188,6 @@ with gr.Blocks() as demo:
         inputs=[df_state],
         outputs=[download_excel_btn]
     )
-    
-    # Optional: Load with default values on app start
-    # def initial_load():
-    #     # Use default values from the input components
-    #     # This would require getting their default values
-    #     # For simplicity, we can call generate with fixed defaults or trigger a click
-    #     # Or, just start with an empty table.
-    #     gr.Info("App loaded. Configure parameters and click 'Generate'.")
-    #     return None, None # Empty table and state
-    # demo.load(initial_load, outputs=[model_points_display, df_state])
 
 
 if __name__ == "__main__":