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| 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)] * 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}", 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") | |
| gr.Markdown("Upload a video") | |
| video_input = gr.Video(label="Upload Video") | |
| max_speakers = gr.Slider(minimum=1, maximum=3, step=1, value=2, label="Maximum Number of Speakers") | |
| analyze_button = gr.Button("Analyze") | |
| # 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=analyze_video, | |
| inputs=[video_input, max_speakers], | |
| outputs=output_components, | |
| show_progress=True | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() |