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 import re import cv2 import os from config import openai_api_key # Load the model llm = load_model(openai_api_key) def analyze_video(video_path, progress=gr.Progress()): start_time = time.time() if not video_path: return [None] * 29 # Return None for all outputs progress(0, desc="Starting analysis...") progress(0.2, desc="Starting transcription and diarization") transcription = diarize_audio(video_path) 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, general_impressions = create_charts(results) progress(1.0, desc="Charts generation complete.") end_time = time.time() execution_time = end_time - start_time output_components = [] # transcript output_components.append(f"Completed in {int(execution_time)} seconds.") output_components.append(gr.Textbox(value=transcription, label="Transcript", lines=10, visible=True)) 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(visible=True): 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) # Pad with None for any missing speakers while len(output_components) < 28: output_components.extend([gr.update(visible=False)] * 9) return output_components def use_example_1(): return "examples/Scenes.From.A.Marriage.US.mp4" def use_example_2(): return "examples/Billie Eilish.mp4" def use_example_3(): return "examples/Elliot Rodger.mp4" def get_middle_frame(video_path): cap = cv2.VideoCapture(video_path) total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) middle_frame_number = total_frames // 7 cap.set(cv2.CAP_PROP_POS_FRAMES, middle_frame_number) ret, frame = cap.read() cap.release() if ret: preview_path = video_path.rsplit('.', 1)[0] + '_preview.jpg' cv2.imwrite(preview_path, frame) return preview_path return None 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.") gr.Markdown(""" 1. Select input video 2. Click Analyze""") with gr.Row(): video_input = gr.Video(label="Upload Video") analyze_button = gr.Button("Analyze") # Create output components output_components = [] # Add transcript output near the top execution_box = gr.Textbox(label="Execution Info", value="N/A", lines=1) output_components.append(execution_box) transcript = gr.Textbox(label="Transcript", lines=10, visible=False) output_components.append(transcript) with open('description.txt', 'r') as file: description_txt = file.read() 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")] with gr.TabItem(label=f'Attachment Styles'): column_components2 = [ gr.Plot(visible=False), gr.Plot(visible=False), gr.Textbox(label="Attachment Styles Explanation")] with gr.TabItem(label=f'Big Five Traits'): column_components3 = [ gr.Plot(visible=False), gr.Textbox(label="Big Five Traits Explanation")] with gr.TabItem(label=f'Personalities'): column_components4 = [ gr.Plot(visible=False), gr.Textbox(label="Personality Disorders Explanation")] output_components.extend(column_components1) output_components.extend(column_components2) output_components.extend(column_components3) output_components.extend(column_components4) gr.Markdown("### Example Videos") with gr.Row(): with gr.Column(scale=1): example_video_1_path = "examples/Scenes.From.A.Marriage.US.mp4" preview_1 = get_middle_frame(example_video_1_path) gr.Image(preview_1, label="Scenes From A Marriage") example_video_1 = gr.Video(example_video_1_path, label="Example 1", visible=False) use_example_button_1 = gr.Button("Load Example 1") with gr.Column(scale=1): example_video_2_path = "examples/Billie Eilish.mp4" preview_2 = get_middle_frame(example_video_2_path) gr.Image(preview_2, label="Billie Eilish") example_video_2 = gr.Video(example_video_2_path, label="Example 2", visible=False) use_example_button_2 = gr.Button("Load Example 2") with gr.Column(scale=1): example_video_3_path = "examples/Elliot Rodger.mp4" preview_3 = get_middle_frame(example_video_3_path) gr.Image(preview_3, label="Elliot Rodger") example_video_3 = gr.Video(example_video_3_path, label="Example 3", visible=False) use_example_button_3 = gr.Button("Load Example 3") gr.HTML("
") gr.Markdown(description_txt) gr.HTML("
") gr.Image(value="appendix/AI Personality Detection flow - 1.png", label='Flowchart 1', width=900) gr.Image(value="appendix/AI Personality Detection flow - 2.png", label='Flowchart 2', width=900) analyze_button.click( fn=analyze_video, inputs=[video_input], outputs=output_components, show_progress=True ) use_example_button_1.click( fn=use_example_1, inputs=[], outputs=[video_input], ).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 ) 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 ) if __name__ == "__main__": iface.launch()