Update app.py

app.py

@@ -41,13 +41,10 @@ def generate_custom_model_points(
     sum_assured_col = np.round((sa_max_val - sa_min_val) * rng.random(size=mp_count_val) + sa_min_val, -3)
     
     max_duration_val = policy_term_col * 12 - 1
-    max_duration_val = np.maximum(1, max_duration_val) # Ensure at least 1
-    # Adjust random generation to ensure it's within [0, max_duration_val-1] then add 1
-    # If max_duration_val is 1, rng.random * 1 is [0,1), int is 0. So duration is 1.
-    # If max_duration_val is > 1, rng.random * (max_duration_val) is [0, max_duration_val), int is [0, max_duration_val-1]. Add 1 for [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
-    duration_mth_col = np.minimum(duration_mth_col, max_duration_val) # Should not be necessary if max_duration_val is used correctly
-    duration_mth_col = np.maximum(1, duration_mth_col) # Ensure duration is at least 1
+    duration_mth_col = np.minimum(duration_mth_col, max_duration_val)
+    duration_mth_col = np.maximum(1, duration_mth_col)
 
 
     if policy_count_fixed_val:
@@ -64,7 +61,7 @@ def generate_custom_model_points(
     return model_point_df
 
 # 2. Gradio App Definition
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
+with gr.Blocks() as demo: # Removed theme=gr.themes.Soft() to use Gradio default
     gr.Markdown("# Actuarial Model Points Generator")
     gr.Markdown(
         "Configure the parameters below to generate a custom set of seriatim model points. "
@@ -77,7 +74,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     with gr.Row():
         with gr.Column(scale=1):
             gr.Markdown("### Generation Parameters")
-            mp_count_input = gr.Slider(minimum=100, maximum=50000, value=1000, step=100, label="Number of Model Points") # Reduced default for faster testing
+            mp_count_input = gr.Slider(minimum=100, maximum=50000, value=1000, step=100, label="Number of Model Points")
             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 +98,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
                     summary_stats_display = gr.Markdown(value="*No data generated yet or summary failed.*")
                 with gr.TabItem("Distribution Plot"):
                     gr.Markdown("Distribution of **Sum Assured**. Data is generated uniformly; a normal curve is fitted for illustration.")
-                    distribution_plot_display = gr.Plot() # No label needed here, will be blank initially
+                    distribution_plot_display = gr.Plot()
                 with gr.TabItem("Categorical Summary"):
                     categorical_summary_display = gr.Markdown(value="*No data generated yet or summary failed.*")
 
@@ -109,18 +106,16 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     def handle_generate_button_click(
         mp_c, s, age_m, age_mx, sa_m, sa_mx, p_terms, incl_sex, pc_fixed
     ):
-        # Initialize outputs for potential error returns
         error_desc_md = "Error during generation. Check parameters."
         error_cat_md = "Error during generation. Check parameters."
         current_df = df_state.value if df_state.value is not None else pd.DataFrame()
 
-
         if int(age_m) >= int(age_mx):
             gr.Warning("Minimum Age must be less than Maximum Age.")
-            return current_df, df_state.value, "Error: Minimum Age < Maximum Age.", None, "Error: Minimum Age < Maximum Age."
+            return current_df, df_state.value, "Error: Minimum Age must be less than Maximum Age.", None, "Error: Minimum Age must be less than Maximum Age."
         if float(sa_m) >= float(sa_mx):
             gr.Warning("Minimum Sum Assured must be less than Maximum Sum Assured.")
-            return current_df, df_state.value, "Error: Min Sum Assured < Max Sum Assured.", None, "Error: Min Sum Assured < Max Sum Assured."
+            return current_df, df_state.value, "Error: Minimum Sum Assured must be less than Maximum Sum Assured.", None, "Error: Minimum Sum Assured must be less than Maximum Sum Assured."
         
         gr.Info("Generating model points... Please wait.")
         try:
@@ -141,9 +136,9 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             else:
                 desc_stats_md = "No numerical data to describe or DataFrame is empty."
 
-            fig = None # Initialize fig to None
+            fig = None
             if 'sum_assured' in df.columns and not df['sum_assured'].empty:
-                fig, ax = plt.subplots(figsize=(8, 5)) # Create fig here
+                fig, ax = plt.subplots(figsize=(8, 5))
                 ax.hist(df['sum_assured'], bins=50, density=True, alpha=0.7, color='skyblue', edgecolor='black', label='Actual Distribution')
                 mu, std = stats.norm.fit(df['sum_assured'])
                 xmin_hist, xmax_hist = ax.get_xlim()
@@ -156,8 +151,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
                 ax.legend()
                 ax.grid(axis='y', linestyle='--', alpha=0.7)
                 plt.tight_layout()
-            # Do NOT call plt.close(fig) here. Gradio handles the figure object.
-            plot_object = fig # This will be None if sum_assured data was missing
+            plot_object = fig
 
             cat_summary_md = "### Categorical Data Frequencies\n\n"
             if not df.empty:
@@ -171,7 +165,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
                     term_counts.columns = ['Policy Term (Years)', 'Count']
                     term_counts['Percentage'] = (term_counts['Count'] / term_counts['Count'].sum() * 100).round(2).astype(str) + '%'
                     cat_summary_md += "#### Policy Term Distribution\n" + term_counts.to_markdown(index=False)
-                if cat_summary_md == "### Categorical Data Frequencies\n\n": # Only header added
+                if cat_summary_md == "### Categorical Data Frequencies\n\n":
                     cat_summary_md += "*No categorical columns ('sex', 'policy_term') found or data is empty.*"
             else:
                 cat_summary_md = "*DataFrame is empty, no categorical data to summarize.*"
@@ -180,7 +174,6 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
 
         except Exception as e:
             gr.Error(f"An error occurred during generation: {str(e)}")
-            # Return current state or empty placeholders for all outputs
             return current_df, df_state.value, f"Error: {str(e)}", None, f"Error: {str(e)}"