Update app.py

app.py

@@ -1317,164 +1317,51 @@ def bold_high_impact_row(row):
         pass
     return [''] * len(row)
 
-
 def variable_outputs(file_inputs):
-
-    file_inputs_single = file_inputs
+   file_inputs_single = file_inputs
+  
+   # Call batch file processing and get analysis results
+   (
+       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_single)
+
+   # Get the first file results
+   img_trust = img_trust_list[0] if img_trust_list else None
+   img_nps = img_nps_list[0] if img_nps_list else None
+   img_loyalty = img_loyalty_list[0] if img_loyalty_list else None
+   img_consideration = img_consideration_list[0] if img_consideration_list else None
+   img_satisfaction = img_satisfaction_list[0] if img_satisfaction_list else None
+   output_text = output_text_list[0] if output_text_list else ""
+   df_builder_pivot = df_builder_pivot_list[0] if df_builder_pivot_list else None
    
-    # Call batch file processing and get analysis results
-    (
-        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_single)
-
-    # Get number of datasets uploaded
-    k = len(file_inputs_single)
-
-    # Container for visible plots
-    global plots_visible
-    plots_visible = []
-
-    # Use zip_longest to iterate over the lists, padding with None
-    for row, (
-        img_bucketfull,
-        img_trust,
-        img_nps,
-        img_loyalty,
-        img_consideration,
-        img_satisfaction,
-        df_builder_pivot,
-        output_text,
-    ) in enumerate(
-        zip_longest(
-            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,
-        )
-    ):
-        # Get dataset name
-        dataset_name = file_inputs_single[row].split("/")[-1]
-        global plots
-        # Based on the number of files uploaded, determine the content of each textbox
-        plots = [
-            
-            gr.Markdown(
-                "<span style='font-size:20px; font-weight:bold;'>Trust and NPS Drivers</span>",
-                visible=True,
-            ),
-            gr.Markdown(
-                """
-                The analysis identifies the TrustLogic® dimensions that are most effective in driving your audience's likelihood to recommend and trust you
-                """,
-                visible=True,
-            ),
-
-            # ✅ Side-by-side Trust & NPS drivers
-            gr.Image(
-                value=combine_two_images_horizontally(img_trust, "Trust Drivers", img_nps, "NPS Drivers"),
-                type="pil",
-                label="Trust + NPS Drivers",
-                visible=True,
-            ),
-            gr.Image(
-                value=None,
-                type="pil",
-                visible=False,
-            ),
-
-
-            gr.Image(
-                value=None,
-                type="pil",
-                visible=False,
-            ),
-            gr.Image(
-                value=None,
-                type="pil",
-                visible=False,
-            ),
-            gr.Image(
-                value=None,
-                type="pil",
-                visible=False,
-            ),
-            gr.Textbox(
-                value=output_text,
-                visible=False,
-            ),
-        ]
-
-        # add current plots to container
-        plots_visible += plots
-
-        if isinstance(df_builder_pivot, pd.DataFrame):
-            logger.debug(f"df_builder_pivot: {df_builder_pivot}")
-
-            markdown_5 = gr.Markdown(
-                "<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(
-                "<span style='font-size:17px; font-weight:bold;'>The  features and messages that improve your NPS and trust most effectively. They key ones are marked in bold.</span>",
-                #+ "<br>In the table, use the little arrow in each column to toggle the most to least effective TrustBuilders® to fill each Trust Bucket®. Your focus is only on the Trust Bucket® with the highest driver impact. "
-               # + "<br> Note: Even if Trust Buckets® for Customers and Prospects overlap, the most effective statements are very different. This provides clear guidance for acquisition versus loyalty activities.",
-                visible=True,
-            )
-
-            styled_df = df_builder_pivot.style.apply(bold_high_impact_row, axis=1)
-
-
-            table_builder_2 = gr.Dataframe(
-                value=styled_df,
-                headers=list(df_builder_pivot.columns),
-                interactive=False,
-                label=f"{dataset_name}",
-                visible=True,
-                height=800,
-                wrap=True,
-            )
-
-
-
-            plots_visible.append(markdown_5)
-            plots_visible.append(markdown_6)
-            plots_visible.append(table_builder_2)
-        else:
-            plots_visible.append(gr.Markdown("", visible=False))
-            plots_visible.append(gr.Markdown("", visible=False))
-            plots_visible.append(gr.Dataframe(value=None, label="", visible=False))
-
-    plots_invisible = [
-        gr.Markdown("", visible=False),
-        gr.Markdown("", visible=False),
-        gr.Image(label="Trust Buckets", visible=False),
-        gr.Markdown("", visible=False),
-        gr.Markdown("", visible=False),
-        gr.Image(label="Trust Drivers", visible=False),
-        gr.Image(label="NPS Drivers", visible=False),
-        gr.Image(label="Loyalty Drivers", visible=False),
-        gr.Image(label="Consideration Drivers", visible=False),
-        gr.Image(label="Satisfaction Drivers", visible=False),
-        gr.Textbox(label="Analysis Summary", visible=False),
-        gr.Markdown("", visible=False),
-        gr.Markdown("", visible=False),
-        gr.Dataframe(value=None, label=" ", visible=False),
-    ]
-
-    return plots_visible + plots_invisible * (max_outputs - k)
-
+   # Return exactly what your components need - including markdown text
+   return (
+       "<span style='font-size:20px; font-weight:bold;'>Trust and NPS Drivers</span>",  # markdown_3
+       """
+       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.
+       """,  # markdown_4
+       img_trust,           # trust_plot
+       img_nps,            # nps_plot  
+       img_loyalty,        # loyalty_plot
+       img_consideration,  # consideration_plot  
+       img_satisfaction,   # satisfaction_plot
+       output_text,        # summary_text
+       "<span style='font-size:20px; font-weight:bold;'>TrustBuilders®</span>",  # markdown_5
+       """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.""",  # markdown_6
+       df_builder_pivot,   # df_builder_pivot
+   )
 
 def reset_outputs():
     # Reset outputs