Update app.py

app.py

@@ -4,9 +4,7 @@ from processing import process_input
 from transcription_diarization import diarize_audio
 from visualization import create_charts
 import time
-import re
 import cv2
-import os
 from config import openai_api_key
 
 # Load the model
@@ -34,63 +32,33 @@ def analyze_video(video_path, progress=gr.Progress()):
     end_time = time.time()
     execution_time = end_time - start_time
 
-    output_components = []  # transcript
+    output_components = []
 
     output_components.append(f"Completed in {int(execution_time)} seconds.")
     output_components.append(gr.Textbox(value=transcription, label="Transcript", lines=10, visible=True))
 
-    with gr.Tab(label=f'Description', visible=False):
-        gr.Markdown(description_txt)
-        gr.HTML("<div style='height: 20px;'></div>")
-        gr.Image(value="appendix/AI Personality Detection flow - 1.png", label='Flowchart 1', width=1000)
-        gr.Image(value="appendix/AI Personality Detection flow - 2.png", label='Flowchart 2', width=1000)
-
     for i, (speaker_id, speaker_charts) in enumerate(charts.items(), start=1):
-        print(speaker_id)
         speaker_explanations = explanations[speaker_id]
         speaker_general_impression = general_impressions[speaker_id]
         
-        with gr.Tab():
-            with gr.TabItem(label=f'General Impression'):
-                speaker_section1 = [
-                    gr.Markdown(f"### {speaker_id}", visible=True),
-                    gr.Textbox(value=speaker_general_impression, label="General Impression", visible=True, lines=10)
-                ]
-
-            with gr.TabItem(label=f'Attachment Styles'):
-                speaker_section2 = [
-                    gr.Plot(value=speaker_charts.get("attachment", None), visible=True),
-                    gr.Plot(value=speaker_charts.get("dimensions", None), visible=True),
-                    gr.Textbox(value=speaker_explanations.get("attachment", ""), label="Attachment Styles Explanation",
-                               visible=True, lines=2)
-                ]
-
-            with gr.TabItem(label=f'Big Five Traits'):
-                speaker_section3 = [
-                    gr.Plot(value=speaker_charts.get("bigfive", None), visible=True),
-                    gr.Textbox(value=speaker_explanations.get("bigfive", ""), label="Big Five Traits Explanation",
-                               visible=True, lines=2)
-                ]
-
-            with gr.TabItem(label=f'Personalities'):
-                speaker_section4 = [
-                    gr.Plot(value=speaker_charts.get("personality", None), visible=True),
-                    gr.Textbox(value=speaker_explanations.get("personality", ""),
-                               label="Personality Disorders Explanation", visible=True, lines=2)
-                ]
-
-        output_components.extend(speaker_section1)
-        output_components.extend(speaker_section2)
-        output_components.extend(speaker_section3)
-        output_components.extend(speaker_section4)
+        output_components.extend([
+            gr.Markdown(f"### {speaker_id}", visible=True),
+            gr.Textbox(value=speaker_general_impression, label="General Impression", visible=True, lines=10),
+            gr.Plot(value=speaker_charts.get("attachment", None), visible=True),
+            gr.Plot(value=speaker_charts.get("dimensions", None), visible=True),
+            gr.Textbox(value=speaker_explanations.get("attachment", ""), label="Attachment Styles Explanation", visible=True, lines=2),
+            gr.Plot(value=speaker_charts.get("bigfive", None), visible=True),
+            gr.Textbox(value=speaker_explanations.get("bigfive", ""), label="Big Five Traits Explanation", visible=True, lines=2),
+            gr.Plot(value=speaker_charts.get("personality", None), visible=True),
+            gr.Textbox(value=speaker_explanations.get("personality", ""), label="Personality Disorders Explanation", visible=True, lines=2)
+        ])
 
     # Pad with None for any missing speakers
-    while len(output_components) < 28:
-        output_components.extend([gr.update(visible=False)] * 9)
+    while len(output_components) < 29:
+        output_components.append(None)
 
     return output_components
 
-
 def use_example_1():
     return "examples/Scenes.From.A.Marriage.US.mp4"
 def use_example_2():
@@ -111,6 +79,9 @@ def get_middle_frame(video_path):
         return preview_path
     return None
 
+def clear_outputs():
+    return [None] * 29
+
 with gr.Blocks() as iface:
     gr.Markdown("# Multiple-Speakers-Personality-Analyzer")
     gr.Markdown("This project provides an advanced AI system designed for diagnosing and profiling personality attributes from video content based on a single speaker or multiple speakers in a conversation.")
@@ -130,32 +101,17 @@ with gr.Blocks() as iface:
     output_components.append(transcript)
 
     for n in range(3):  # Assuming maximum of 3 speakers
-        with gr.Tab(label=f'Speaker {n + 1}', visible=True):
-            with gr.TabItem(label=f'General Impression'):
-                column_components1 = [
-                    gr.Markdown(visible=False),
-                    gr.Textbox(label="General Impression", visible=False)]
-
-            with gr.TabItem(label=f'Attachment Styles'):
-                column_components2 = [
-                    gr.Plot(visible=False),
-                    gr.Plot(visible=False),
-                    gr.Textbox(label="Attachment Styles Explanation", visible=False)]
-
-            with gr.TabItem(label=f'Big Five Traits'):
-                column_components3 = [
-                    gr.Plot(visible=False),
-                    gr.Textbox(label="Big Five Traits Explanation", visible=False)]
-
-            with gr.TabItem(label=f'Personalities'):
-                column_components4 = [
-                    gr.Plot(visible=False),
-                    gr.Textbox(label="Personality Disorders Explanation", visible=False)]
-
-        output_components.extend(column_components1)
-        output_components.extend(column_components2)
-        output_components.extend(column_components3)
-        output_components.extend(column_components4)
+        with gr.Tab(label=f'Speaker {n + 1}', visible=False) as tab:
+            gr.Markdown(visible=False)
+            gr.Textbox(label="General Impression", visible=False)
+            gr.Plot(visible=False)
+            gr.Plot(visible=False)
+            gr.Textbox(label="Attachment Styles Explanation", visible=False)
+            gr.Plot(visible=False)
+            gr.Textbox(label="Big Five Traits Explanation", visible=False)
+            gr.Plot(visible=False)
+            gr.Textbox(label="Personality Disorders Explanation", visible=False)
+            output_components.extend([tab] + [component for component in tab.children])
 
     with open('description.txt', 'r') as file:
         description_txt = file.read()
@@ -165,7 +121,6 @@ with gr.Blocks() as iface:
         gr.Image(value="appendix/AI Personality Detection flow - 1.png", label='Flowchart 1', width=1000)
         gr.Image(value="appendix/AI Personality Detection flow - 2.png", label='Flowchart 2', width=1000)
 
-    
     gr.Markdown("### Example Videos")
     with gr.Row():
         with gr.Column(scale=1):
@@ -190,6 +145,10 @@ with gr.Blocks() as iface:
             use_example_button_3 = gr.Button("Load Example 3")
             
     analyze_button.click(
+        fn=clear_outputs,
+        inputs=[],
+        outputs=output_components
+    ).then(
         fn=analyze_video,
         inputs=[video_input],
         outputs=output_components,
@@ -200,29 +159,46 @@ with gr.Blocks() as iface:
         fn=use_example_1,
         inputs=[],
         outputs=[video_input],
-    ).then(fn=analyze_video,
-           inputs=[video_input],
-           outputs=output_components,
-           show_progress=True
-           )
+    ).then(
+        fn=clear_outputs,
+        inputs=[],
+        outputs=output_components
+    ).then(
+        fn=analyze_video,
+        inputs=[video_input],
+        outputs=output_components,
+        show_progress=True
+    )
+
     use_example_button_2.click(
         fn=use_example_2,
         inputs=[],
         outputs=[video_input],
-    ).then(fn=analyze_video,
-           inputs=[video_input],
-           outputs=output_components,
-           show_progress=True
-           )
+    ).then(
+        fn=clear_outputs,
+        inputs=[],
+        outputs=output_components
+    ).then(
+        fn=analyze_video,
+        inputs=[video_input],
+        outputs=output_components,
+        show_progress=True
+    )
+
     use_example_button_3.click(
         fn=use_example_3,
         inputs=[],
         outputs=[video_input],
-    ).then(fn=analyze_video,
-           inputs=[video_input],
-           outputs=output_components,
-           show_progress=True
-           )
+    ).then(
+        fn=clear_outputs,
+        inputs=[],
+        outputs=output_components
+    ).then(
+        fn=analyze_video,
+        inputs=[video_input],
+        outputs=output_components,
+        show_progress=True
+    )
 
 if __name__ == "__main__":
     iface.launch()