Update app.py

app.py

@@ -1,80 +1,3 @@
-import gradio as gr
-from llm_loader import load_model
-from processing import process_input
-from transcription_diarization import diarize_audio
-from visualization import create_charts
-import time
-from config import openai_api_key
-
-# 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 [gr.Markdown("Please upload a video file.")] + [gr.update(visible=False)] * 48 + ["Analysis not started."]
-
-    # Start the progress bar
-    progress(0, desc="Starting analysis...")
-
-    # Progress for transcription and diarization
-    progress(0.2, desc="Starting transcription and diarization...")
-    transcription = diarize_audio(video_path, max_speakers)
-    progress(0.6, desc="Transcription and diarization complete.")
-
-    # Progress for processing the transcription
-    progress(0.7, desc="Processing transcription...")
-    results = process_input(transcription, llm)
-    progress(0.8, desc="Transcription processing complete.")
-
-    # Progress for creating charts
-    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
-
-    # Prepare outputs for each speaker
-    output_components = []
-    speaker_count = len(charts)
-
-    for speaker_id, speaker_charts in charts.items():
-        speaker_explanations = explanations[speaker_id]
-
-        speaker_section = [
-                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)
-
-    # Add empty placeholders for remaining outputs if fewer speakers than expected
-    while len(output_components) < 48:
-        output_components.extend([
-            gr.Plot(visible=False),
-            gr.Textbox(visible=False),
-            gr.Plot(visible=False),
-            gr.Plot(visible=False),
-            gr.Textbox(visible=False),
-            gr.Plot(visible=False),
-            gr.Textbox(visible=False),
-        ])
-
-    # Add the transcript at the end
-    output_components.append(gr.Textbox(value=transcription, label="Transcript", lines=10, visible=True))
-
-    # Add the execution time
-    output_components.append(gr.Textbox(value=f"Completed in {int(execution_time)} seconds.", label="Execution Information", visible=True))
-
-    return output_components
-
 # Define the Gradio interface
 with gr.Blocks() as iface:
     gr.Markdown("# AI Personality Detection")