Update app.py

app.py

@@ -2551,6 +2551,7 @@ def load_nps_and_r2(file_path):
 
     return nps_img, r2_img
 
+
 with gr.Blocks(css=css, js=js_func) as demo:
     gr.HTML("""
     <style>
@@ -2609,11 +2610,8 @@ with gr.Blocks(css=css, js=js_func) as demo:
                 gr.Markdown("""<div style='text-align: center;'><h3>How much of your NPS is determined by TrustLogic®</h3></div>""")
                 trust_r2_img = gr.HTML()
 
-        # ADD THE MISSING OUTPUTS SECTION HERE
+        # CREATE THE OUTPUT COMPONENTS THAT MATCH variable_outputs() STRUCTURE
         with gr.Column():
-            # Create all the output components that variable_outputs() expects
-            outputs = []
-            
             # Trust and NPS Drivers section
             markdown_3 = gr.Markdown(
                 "<span style='font-size:20px; font-weight:bold;'>Trust and NPS Drivers</span>",
@@ -2621,13 +2619,19 @@ with gr.Blocks(css=css, js=js_func) as demo:
             )
             markdown_4 = gr.Markdown(
                 """
-                This analysis shows which Trust Buckets® are most effective in building trust and improving your key performance indicators (KPIs).
-                <br><br>
-                The middle line is the average importance. The bars extending to the right show which Trust Buckets® are most important. The higher the percentage, the more important the Trust Bucket® is to your audience.
+                The analysis identifies the TrustLogic® dimensions that are most effective in driving your audience's likelihood to recommend and trust you
                 """,
                 visible=True,
             )
-            trust_plot = gr.Image(value=None, label="Trust Drivers", visible=False)
+            
+            # Combined Trust + NPS Drivers Image
+            combined_drivers_img = gr.Image(
+                value=None,
+                label="Trust + NPS Drivers",
+                visible=True,
+            )
+            
+            # Individual driver images (mostly hidden)
             nps_plot = gr.Image(value=None, label="NPS Drivers", visible=False)
             loyalty_plot = gr.Image(value=None, label="Loyalty Drivers", visible=False)
             consideration_plot = gr.Image(value=None, label="Consideration Drivers", visible=False)
@@ -2636,32 +2640,27 @@ with gr.Blocks(css=css, js=js_func) as demo:
 
             # TrustBuilders section
             markdown_5 = gr.Markdown(
-                "<span style='font-size:20px; font-weight:bold;'>TrustBuilders®",
+                "<span style='font-size:20px; font-weight:bold;'>What to say and do to build your trust and Net Promoter Score</span>",
                 visible=True,
             )
             markdown_6 = gr.Markdown(
-                "These are the specific reasons to trust and recommend. They can be your brand values, features, attributes, programs, and messages. "
-                + "<br>For practical purposes, they tell you exactly what to do and say to build more trust and improve your KPIs. "
-                + "<br>In the table, use the little arrows to toggle by Trust Bucket® or Trust Builder® importance. "
-                + "<br>Tip: Compare Owners and Prospects. Even though some of the Trust Buckets® are the same, the Trust Builders® are very different.",
+                "<span style='font-size:17px; font-weight:bold;'>The features and messages that improve your NPS and trust most effectively. The key ones are marked in bold.</span>",
                 visible=True,
             )
-            df_builder_pivot = gr.Dataframe(value=None, label="", visible=True)
+            df_builder_pivot = gr.Dataframe(value=None, label="", visible=True, height=800, wrap=True)
 
-            # Collect outputs in the correct order
+            # Collect outputs in the correct order to match variable_outputs()
             outputs = [
-                markdown_3, markdown_4, trust_plot, nps_plot, loyalty_plot, 
+                markdown_3, markdown_4, combined_drivers_img, nps_plot, loyalty_plot, 
                 consideration_plot, satisfaction_plot, summary_text,
                 markdown_5, markdown_6, df_builder_pivot
             ]
 
-    # 🔁 States - FIXED FILE PATHS
+    # 🔁 States 
     vw_customers_state11 = gr.State(value=["example_files/VW Owners.xlsx"])
     vw_prospects_state12 = gr.State(value=["example_files/Volkswagen Non Customers.xlsx"])
-    # ✅ FIXED: Added proper file paths
     vw_customers_state = gr.State(value=["example_files/VW Owners.xlsx"])
     vw_prospects_state = gr.State(value=["example_files/Volkswagen Non Customers.xlsx"])
-    # ✅ FIXED: Added proper file path for AI dataset selection
     selected_dataset_ai = gr.State(value="example_files/VW Owners.xlsx")
     chat_history = gr.State(value=[])
 
@@ -2674,24 +2673,64 @@ with gr.Blocks(css=css, js=js_func) as demo:
         return gr.update(visible=new_visible), button_text, new_visible
     
     def set_vw_owners():
-        return "example_files/VW Owners.xlsx"  # ✅ FIXED: Added proper path
+        return "example_files/VW Owners.xlsx"
     
     def set_vw_prospects():
-        return "example_files/Volkswagen Non Customers.xlsx"  # ✅ FIXED: Added proper path
+        return "example_files/Volkswagen Non Customers.xlsx"
 
-    # MODIFIED process_examples function to work with your outputs
+    # MODIFIED wrapper function to extract VALUES from variable_outputs()
     def process_examples_wrapper(file_inputs):
         try:
-            # Call your existing variable_outputs function
-            result = variable_outputs(file_inputs)
-            # Return only the first 11 items to match your outputs list
-            return result[:11]
+            # Call batch file processing directly to get the data
+            (
+                img_bucketfull_list,
+                img_trust_list,
+                img_nps_list,
+                img_loyalty_list,
+                img_consideration_list,
+                img_satisfaction_list,
+                df_builder_pivot_list,
+                output_text_list,
+            ) = batch_file_processing(file_inputs)
+            
+            # Process the first dataset (since we're only showing one at a time)
+            if len(file_inputs) > 0:
+                img_trust = img_trust_list[0] if img_trust_list else None
+                img_nps = img_nps_list[0] if img_nps_list else None
+                df_builder_pivot = df_builder_pivot_list[0] if df_builder_pivot_list else None
+                output_text = output_text_list[0] if output_text_list else ""
+                
+                # Create combined image
+                combined_img = None
+                if img_trust is not None and img_nps is not None:
+                    combined_img = combine_two_images_horizontally(img_trust, "Trust Drivers", img_nps, "NPS Drivers")
+                
+                # Prepare dataframe for display
+                styled_df = None
+                if isinstance(df_builder_pivot, pd.DataFrame):
+                    styled_df = df_builder_pivot.style.apply(bold_high_impact_row, axis=1)
+                
+                # Return updates for all components
+                return [
+                    gr.update(value="<span style='font-size:20px; font-weight:bold;'>Trust and NPS Drivers</span>", visible=True),
+                    gr.update(value="The analysis identifies the TrustLogic® dimensions that are most effective in driving your audience's likelihood to recommend and trust you", visible=True),
+                    gr.update(value=combined_img, visible=True),
+                    gr.update(visible=False),  # nps_plot
+                    gr.update(visible=False),  # loyalty_plot
+                    gr.update(visible=False),  # consideration_plot
+                    gr.update(visible=False),  # satisfaction_plot
+                    gr.update(value=output_text, visible=False),  # summary_text
+                    gr.update(value="<span style='font-size:20px; font-weight:bold;'>What to say and do to build your trust and Net Promoter Score</span>", visible=True),
+                    gr.update(value="<span style='font-size:17px; font-weight:bold;'>The features and messages that improve your NPS and trust most effectively. The key ones are marked in bold.</span>", visible=True),
+                    gr.update(value=styled_df, visible=True)
+                ]
+            else:
+                # Return empty updates if no files
+                return [gr.update() for _ in range(11)]
+                
         except Exception as e:
             logger.error(f"Error in process_examples_wrapper: {e}")
-            # Return empty/default values for all outputs
-            return [
-                gr.update(visible=False) for _ in range(11)
-            ]
+            return [gr.update() for _ in range(11)]
 
     # Event handlers
     show_data_btn.click(
@@ -2781,6 +2820,3 @@ except Exception as e:
 
 
 
-
-
-