Update app.py

app.py

@@ -5,104 +5,65 @@ from transcription_diarization import diarize_audio
 from visualization import create_charts
 import time
 from config import openai_api_key
-import plotly.graph_objs as go
-import json
 
 # Load the model
 llm = load_model(openai_api_key)
 
+
 def analyze_video(video_path, max_speakers, progress=gr.Progress()):
     start_time = time.time()
     if not video_path:
-        return {"error": "Please upload a video file."}
-
-    try:
-        progress(0, desc="Starting analysis...")
-        progress(0.2, desc="Starting transcription and diarization")
-        transcription = diarize_audio(video_path, max_speakers)
-        print("Transcription:", transcription)  # Debug print
-        progress(0.5, desc="Transcription and diarization complete.")
-
-        progress(0.6, desc="Processing transcription")
-        raw_llm_output = llm(transcription)  # This should return the JSON string
-        print("Raw LLM Output:", raw_llm_output)  # Debug print
-        results = process_input(raw_llm_output, llm)
-        print("Processed results:", results)  # Debug print
-        progress(0.7, desc="Transcription processing complete.")
-
-        progress(0.9, desc="Generating charts")
-        charts, explanations = create_charts(results)
-        print("Charts generated:", charts.keys())  # Debug print
-        print("Explanations generated:", explanations.keys())  # Debug print
-        progress(1.0, desc="Charts generation complete.")
-
-        end_time = time.time()
-        execution_time = end_time - start_time
-
-        return {
-            "transcript": transcription,
-            "charts": charts,
-            "explanations": explanations,
-            "execution_time": int(execution_time)
-        }
-    except Exception as e:
-        print(f"Error in analyze_video: {e}")
-        return {"error": f"An error occurred during analysis: {str(e)}"}
-
-def create_output_components():
-    with gr.Row() as row:
-        with gr.Column():
-            transcript = gr.Textbox(label="Transcript", lines=10, visible=True)
-            
-            tabs = gr.Tabs()
-            tab_components = []
-            for i in range(3):  # Pre-create 3 tabs (max number of speakers)
-                with gr.Tab(f"Speaker {i+1}", visible=True) as tab:
-                    speaker_components = [
-                        gr.Markdown(f"## Speaker {i+1}", visible=True),
-                        gr.Plot(label="Attachment", visible=True),
-                        gr.Textbox(label="Attachment Styles Explanation", visible=True),
-                        gr.Plot(label="Dimensions", visible=True),
-                        gr.Plot(label="Big Five", visible=True),
-                        gr.Textbox(label="Big Five Traits Explanation", visible=True),
-                        gr.Plot(label="Personality", visible=True),
-                        gr.Textbox(label="Personality Disorders Explanation", visible=True)
-                    ]
-                    tab_components.extend(speaker_components)
-            
-            execution_info = gr.Textbox(label="Execution Information", visible=True)
-    return row, transcript, tab_components, execution_info
-
-def run_analysis(video_path, max_speakers):
-    results = analyze_video(video_path, max_speakers)
-    
-    if "error" in results:
-        return [gr.update(value=results["error"], visible=True)] + [gr.update(visible=False)] * 24 + [gr.update(value="Analysis failed", visible=True)]
-
-    transcript = results["transcript"]
-    execution_info = f"Completed in {results['execution_time']} seconds."
-
-    tab_updates = []
-    for i in range(3):  # For each potential speaker
-        if i < len(results["charts"]):
-            speaker_id = list(results["charts"].keys())[i]
-            speaker_charts = results["charts"][speaker_id]
-            speaker_explanations = results["explanations"][speaker_id]
-            
-            tab_updates.extend([
-                gr.update(value=f"## {speaker_id}", visible=True),  # Markdown
-                gr.update(value=speaker_charts.get("attachment", go.Figure()), visible=True),  # Attachment plot
-                gr.update(value=speaker_explanations.get("attachment", ""), visible=True),  # Attachment explanation
-                gr.update(value=speaker_charts.get("dimensions", go.Figure()), visible=True),  # Dimensions plot
-                gr.update(value=speaker_charts.get("bigfive", go.Figure()), visible=True),  # Big Five plot
-                gr.update(value=speaker_explanations.get("bigfive", ""), visible=True),  # Big Five explanation
-                gr.update(value=speaker_charts.get("personality", go.Figure()), visible=True),  # Personality plot
-                gr.update(value=speaker_explanations.get("personality", ""), visible=True),  # Personality explanation
-            ])
-        else:
-            tab_updates.extend([gr.update(visible=False)] * 8)  # Hide unused tab components
-
-    return [gr.update(value=transcript, visible=True)] + tab_updates + [gr.update(value=execution_info, visible=True)]
+        return [gr.Markdown("Please upload a video file.")] + [gr.update(visible=False)] * 49 + [
+            "Analysis not started."]
+
+    progress(0, desc="Starting analysis...")
+    progress(0.2, desc="Starting transcription and diarization")
+    transcription = diarize_audio(video_path, max_speakers)
+    print("Transcription:", transcription)  # Debug print
+    progress(0.5, desc="Transcription and diarization complete.")
+
+    progress(0.6, desc="Processing transcription")
+    results = process_input(transcription, llm)
+    progress(0.7, desc="Transcription processing complete.")
+
+    progress(0.9, desc="Generating charts")
+    charts, explanations = create_charts(results)
+    progress(1.0, desc="Charts generation complete.")
+
+    end_time = time.time()
+    execution_time = end_time - start_time
+
+    output_components = []
+
+    # Add transcript near the beginning
+    output_components.append(gr.Textbox(value=transcription, label="Transcript", lines=10, visible=True))
+
+    for speaker_id, speaker_charts in charts.items():
+        speaker_explanations = explanations[speaker_id]
+        speaker_section = [
+            gr.Markdown(f"## {speaker_id}<hr>", visible=True),
+            gr.Plot(value=speaker_charts.get("attachment", None), visible=True),
+            gr.Textbox(value=speaker_explanations.get("attachment", ""), label="Attachment Styles Explanation",
+                       visible=True),
+            gr.Plot(value=speaker_charts.get("dimensions", None), visible=True),
+            gr.Plot(value=speaker_charts.get("bigfive", None), visible=True),
+            gr.Textbox(value=speaker_explanations.get("bigfive", ""), label="Big Five Traits Explanation",
+                       visible=True),
+            gr.Plot(value=speaker_charts.get("personality", None), visible=True),
+            gr.Textbox(value=speaker_explanations.get("personality", ""), label="Personality Disorders Explanation",
+                       visible=True),
+        ]
+        output_components.extend(speaker_section)
+
+    while len(output_components) < 49:
+        output_components.extend([gr.update(visible=False)] * 8)
+
+    # Add execution info
+    output_components.append(
+        gr.Textbox(value=f"Completed in {int(execution_time)} seconds.", label="Execution Information", visible=True))
+
+    return output_components
+
 
 with gr.Blocks() as iface:
     gr.Markdown("# AI Personality Detection")
@@ -112,12 +73,33 @@ with gr.Blocks() as iface:
     max_speakers = gr.Slider(minimum=1, maximum=3, step=1, value=2, label="Maximum Number of Speakers")
     analyze_button = gr.Button("Analyze")
 
-    output_row, transcript_output, tab_components, execution_info_output = create_output_components()
+    # Create output components
+    output_components = []
+
+    # Add transcript output near the top
+    execution_info_box = gr.Textbox(label="Execution Information", value="N/A", lines=1)
+    output_components.append(execution_info_box)
+
+    for _ in range(3):  # Assuming maximum of 3 speakers
+        output_components.extend([
+            gr.Markdown(visible=False),
+            gr.Plot(visible=False),
+            gr.Textbox(label="Attachment Styles Explanation", visible=False),
+            gr.Plot(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),
+        ])
+
+    # Add execution info component
+    transcript_output = gr.Textbox(label="Transcript", lines=10, visible=False)
+    output_components.append(transcript_output)
 
     analyze_button.click(
-        fn=run_analysis,
+        fn=analyze_video,
         inputs=[video_input, max_speakers],
-        outputs=[transcript_output] + tab_components + [execution_info_output],
+        outputs=output_components,
         show_progress=True
     )