Update app.py

app.py

@@ -12,10 +12,7 @@ llm = load_model(openai_api_key)
 def analyze_video(video_path, progress=gr.Progress()):
     start_time = time.time()
     if not video_path:
-        return {
-            "transcript": gr.Textbox(value="Please upload a video file.", label="Error"),
-            "execution_info": gr.Textbox(value="Analysis not started.", label="Execution Information")
-        }
+        return [None] * 29  # Return None for all outputs
 
     progress(0, desc="Starting analysis...")
     progress(0.2, desc="Starting transcription and diarization")
@@ -35,53 +32,52 @@ def analyze_video(video_path, progress=gr.Progress()):
     execution_time = end_time - start_time
 
     output_components = {
-        "transcript": gr.Textbox(value=transcription, label="Transcript", lines=10),
+        "transcript": transcription,
+        "execution_info": f"Completed in {int(execution_time)} seconds."
     }
 
     for i, (speaker_id, speaker_charts) in enumerate(charts.items(), start=1):
         speaker_explanations = explanations[speaker_id]
         speaker_general_impression = general_impressions[speaker_id]
         output_components.update({
-            f"speaker_{i}_header": gr.Markdown(f"## {speaker_id}"),
-            f"speaker_{i}_impression": gr.Textbox(value=speaker_general_impression, label="General Impression", lines=3),
-            f"speaker_{i}_attachment": gr.Plot(value=speaker_charts["attachment"]),
-            f"speaker_{i}_attachment_exp": gr.Textbox(value=speaker_explanations["attachment"], label="Attachment Styles Explanation"),
-            f"speaker_{i}_dimensions": gr.Plot(value=speaker_charts["dimensions"]),
-            f"speaker_{i}_bigfive": gr.Plot(value=speaker_charts["bigfive"]),
-            f"speaker_{i}_bigfive_exp": gr.Textbox(value=speaker_explanations["bigfive"], label="Big Five Traits Explanation"),
-            f"speaker_{i}_personality": gr.Plot(value=speaker_charts["personality"]),
-            f"speaker_{i}_personality_exp": gr.Textbox(value=speaker_explanations["personality"], label="Personality Disorders Explanation"),
+            f"speaker_{i}_header": f"## {speaker_id}",
+            f"speaker_{i}_impression": speaker_general_impression,
+            f"speaker_{i}_attachment": speaker_charts["attachment"],
+            f"speaker_{i}_attachment_exp": speaker_explanations["attachment"],
+            f"speaker_{i}_dimensions": speaker_charts["dimensions"],
+            f"speaker_{i}_bigfive": speaker_charts["bigfive"],
+            f"speaker_{i}_bigfive_exp": speaker_explanations["bigfive"],
+            f"speaker_{i}_personality": speaker_charts["personality"],
+            f"speaker_{i}_personality_exp": speaker_explanations["personality"],
         })
 
-    output_components["execution_info"] = gr.Textbox(value=f"Completed in {int(execution_time)} seconds.", label="Execution Information")
-
-    return output_components
-
-def use_example():
-    return "examples/Scenes.From.A.Marriage.US.mp4"
-
-def update_output(components):
+    # Convert the dictionary to a list of outputs
+    return [output_components.get(key, None) for key in [
+        "transcript",
+        "speaker_1_header", "speaker_1_impression", "speaker_1_attachment", "speaker_1_attachment_exp",
+        "speaker_1_dimensions", "speaker_1_bigfive", "speaker_1_bigfive_exp", "speaker_1_personality",
+        "speaker_1_personality_exp",
+        "speaker_2_header", "speaker_2_impression", "speaker_2_attachment", "speaker_2_attachment_exp",
+        "speaker_2_dimensions", "speaker_2_bigfive", "speaker_2_bigfive_exp", "speaker_2_personality",
+        "speaker_2_personality_exp",
+        "speaker_3_header", "speaker_3_impression", "speaker_3_attachment", "speaker_3_attachment_exp",
+        "speaker_3_dimensions", "speaker_3_bigfive", "speaker_3_bigfive_exp", "speaker_3_personality",
+        "speaker_3_personality_exp",
+        "execution_info"
+    ]]
+
+def update_output(*args):
     updates = []
-    updates.append(gr.update(value=components["transcript"].value, visible=True))
-    for i in range(1, 4):
-        if f"speaker_{i}_header" in components:
-            updates.append(gr.update(visible=True))
-            updates.extend([
-                gr.update(value=components[f"speaker_{i}_header"].value),
-                gr.update(value=components[f"speaker_{i}_impression"].value),
-                gr.update(value=components[f"speaker_{i}_attachment"].value),
-                gr.update(value=components[f"speaker_{i}_attachment_exp"].value),
-                gr.update(value=components[f"speaker_{i}_dimensions"].value),
-                gr.update(value=components[f"speaker_{i}_bigfive"].value),
-                gr.update(value=components[f"speaker_{i}_bigfive_exp"].value),
-                gr.update(value=components[f"speaker_{i}_personality"].value),
-                gr.update(value=components[f"speaker_{i}_personality_exp"].value),
-            ])
+    for arg in args:
+        if arg is not None:
+            updates.append(gr.update(value=arg, visible=True))
         else:
             updates.append(gr.update(visible=False))
-    updates.append(gr.update(value=components["execution_info"].value, visible=True))
     return updates
 
+def use_example():
+    return "examples/Scenes.From.A.Marriage.US.mp4"
+
 with gr.Blocks() as iface:
     gr.Markdown("# AI Personality Detection")
     
@@ -101,28 +97,33 @@ with gr.Blocks() as iface:
         speaker_outputs = []
         for i in range(1, 4):  # Assuming a maximum of 3 speakers
             with gr.Column(visible=False) as speaker_column:
-                gr.Markdown(f"## Speaker {i}")
-                gr.Textbox(label="General Impression", lines=3)
-                gr.Plot(label="Attachment Styles")
-                gr.Textbox(label="Attachment Styles Explanation")
-                gr.Plot(label="Attachment Dimensions")
-                gr.Plot(label="Big Five Traits")
-                gr.Textbox(label="Big Five Traits Explanation")
-                gr.Plot(label="Personality Disorders")
-                gr.Textbox(label="Personality Disorders Explanation")
-            speaker_outputs.append(speaker_column)
+                speaker_header = gr.Markdown(f"## Speaker {i}")
+                speaker_impression = gr.Textbox(label="General Impression", lines=3)
+                speaker_attachment = gr.Plot(label="Attachment Styles")
+                speaker_attachment_exp = gr.Textbox(label="Attachment Styles Explanation")
+                speaker_dimensions = gr.Plot(label="Attachment Dimensions")
+                speaker_bigfive = gr.Plot(label="Big Five Traits")
+                speaker_bigfive_exp = gr.Textbox(label="Big Five Traits Explanation")
+                speaker_personality = gr.Plot(label="Personality Disorders")
+                speaker_personality_exp = gr.Textbox(label="Personality Disorders Explanation")
+            speaker_outputs.extend([
+                speaker_header, speaker_impression, speaker_attachment, speaker_attachment_exp,
+                speaker_dimensions, speaker_bigfive, speaker_bigfive_exp, speaker_personality,
+                speaker_personality_exp
+            ])
         execution_info = gr.Textbox(label="Execution Information", visible=False)
 
+    all_outputs = [transcript_output] + speaker_outputs + [execution_info]
 
     analyze_button.click(
         fn=analyze_video,
         inputs=[video_input],
-        outputs=[transcript_output] + speaker_outputs + [execution_info],
+        outputs=all_outputs,
         show_progress=True
     ).then(
         fn=update_output,
-        inputs=[gr.State()],  # We'll pass the output of analyze_video to this function
-        outputs=[transcript_output] + speaker_outputs + [execution_info],
+        inputs=all_outputs,
+        outputs=all_outputs,
     )
 
     use_example_button.click(
@@ -132,12 +133,12 @@ with gr.Blocks() as iface:
     ).then(
         fn=analyze_video,
         inputs=[video_input],
-        outputs=[transcript_output] + speaker_outputs + [execution_info],
+        outputs=all_outputs,
         show_progress=True
     ).then(
         fn=update_output,
-        inputs=[gr.State()],  # We'll pass the output of analyze_video to this function
-        outputs=[transcript_output] + speaker_outputs + [execution_info],
+        inputs=all_outputs,
+        outputs=all_outputs,
     )
 
 if __name__ == "__main__":