Update app.py

app.py

@@ -1,3 +1,5 @@
+# app.py
+
 import gradio as gr
 from llm_loader import load_model
 from processing import process_input
@@ -67,28 +69,22 @@ def analyze_video(video_path, language_display_name, max_speakers, progress=gr.P
         output_components.append(gr.Markdown(f"### {speaker_id}"))
         
         if "attachment" in speaker_charts:
-            output_components.extend([
-                gr.Plot(speaker_charts["attachment"]),
-                gr.Textbox(value=speaker_explanations.get("attachment", ""),
-                           label=f"Attachment Styles Explanation - {speaker_id}", lines=2),
-            ])
+            output_components.append(gr.Plot(speaker_charts["attachment"]))
+            output_components.append(gr.Textbox(value=speaker_explanations.get("attachment", ""),
+                           label=f"Attachment Styles Explanation - {speaker_id}", lines=2))
         
         if "dimensions" in speaker_charts:
             output_components.append(gr.Plot(speaker_charts["dimensions"]))
         
         if "bigfive" in speaker_charts:
-            output_components.extend([
-                gr.Plot(speaker_charts["bigfive"]),
-                gr.Textbox(value=speaker_explanations.get("bigfive", ""),
-                           label=f"Big Five Traits Explanation - {speaker_id}", lines=2),
-            ])
+            output_components.append(gr.Plot(speaker_charts["bigfive"]))
+            output_components.append(gr.Textbox(value=speaker_explanations.get("bigfive", ""),
+                           label=f"Big Five Traits Explanation - {speaker_id}", lines=2))
         
         if "personality" in speaker_charts:
-            output_components.extend([
-                gr.Plot(speaker_charts["personality"]),
-                gr.Textbox(value=speaker_explanations.get("personality", ""),
-                           label=f"Personality Disorders Explanation - {speaker_id}", lines=2),
-            ])
+            output_components.append(gr.Plot(speaker_charts["personality"]))
+            output_components.append(gr.Textbox(value=speaker_explanations.get("personality", ""),
+                           label=f"Personality Disorders Explanation - {speaker_id}", lines=2))
 
     # Add the transcript at the end
     output_components.append(gr.Textbox(value=transcription, label="Transcript", lines=10))
@@ -106,8 +102,8 @@ with gr.Blocks() as iface:
 
     analyze_button = gr.Button("Analyze")
 
-    # Create a container for dynamic outputs
-    output_container = gr.Column()
+    # Create placeholders for output components
+    output_components = [gr.Markdown(visible=False) for _ in range(50)]  # Adjust the number based on your maximum possible outputs
     
     # Execution time box
     execution_info_box = gr.Textbox(label="Execution Information", value="Waiting for analysis...", lines=2)
@@ -115,7 +111,7 @@ with gr.Blocks() as iface:
     analyze_button.click(
         fn=analyze_video,
         inputs=[video_input, language_input, max_speakers],
-        outputs=[output_container, execution_info_box],
+        outputs=output_components + [execution_info_box],
         show_progress=True
     )