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import gradio as gr
from llm_loader import load_model
from processing import process_input
from transcription_diarization import process_video
from visualization import create_charts
import os
import time
from config import hf_token, openai_api_key
# Load the model
llm = load_model(openai_api_key)
# Mapping of display names to language codes
LANGUAGE_MAP = {
"English": "en",
"Hebrew": "he",
"Italian": "it",
"French": "fr",
"German": "de",
"Chinese": "zh",
"Arabic": "ar"
}
def analyze_video(video_path, language_display_name, max_speakers, progress=gr.Progress()):
start_time = time.time()
if not video_path:
return "Please upload a video file.", gr.Textbox.update(value="Analysis not started.")
# Convert the display name to the language code
language = LANGUAGE_MAP[language_display_name]
# Start the progress bar
progress(0, desc="Starting analysis...")
# Progress for diarization
progress(0.2, desc="Starting diarization...")
srt_path = process_video(video_path, hf_token, language, max_speakers)
progress(0.4, desc="Diarization complete.")
# Progress for transcription
with open(srt_path, 'r', encoding='utf-8') as file:
transcription = file.read()
progress(0.6, desc="Transcription 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.")
# Clean up the temporary SRT file
os.remove(srt_path)
end_time = time.time()
execution_time = end_time - start_time
# Prepare outputs for each speaker
output_components = []
for speaker_id, speaker_charts in charts.items():
speaker_explanations = explanations[speaker_id]
output_components.extend([
gr.Markdown(f"### Speaker {speaker_id}"),
gr.Plot(speaker_charts["attachment"]),
gr.Textbox(value=speaker_explanations["attachment"],
label=f"Attachment Styles Explanation - Speaker {speaker_id}", lines=2),
gr.Plot(speaker_charts["dimensions"]),
gr.Plot(speaker_charts["bigfive"]),
gr.Textbox(value=speaker_explanations["bigfive"],
label=f"Big Five Traits Explanation - Speaker {speaker_id}", lines=2),
gr.Plot(speaker_charts["personality"]),
gr.Textbox(value=speaker_explanations["personality"],
label=f"Personality Disorders Explanation - Speaker {speaker_id}", lines=2)
])
# Add the transcript and execution info at the end
output_components.extend([
gr.Textbox(value=transcription, label="Transcript", lines=10),
gr.Textbox.update(value=f"Completed in {int(execution_time)} seconds.", label="Execution Information",
visible=True)
])
return output_components, gr.Textbox.update(value=f"Completed in {int(execution_time)} seconds.")
# Define the Gradio interface
with gr.Blocks() as iface:
gr.Markdown("# Video Analysis Tool")
gr.Markdown("Upload a video to analyze speech patterns and personality traits.")
video_input = gr.Video(label="Upload Video")
language_input = gr.Dropdown(choices=list(LANGUAGE_MAP.keys()), value="English", label="Select Language")
max_speakers = gr.Slider(minimum=1, maximum=4, step=1, value=2, label="Maximum Number of Speakers")
analyze_button = gr.Button("Analyze")
# Placeholder for dynamic outputs
output_section = gr.Column()
# Execution time box, initially displaying a waiting message
execution_info_box = gr.Textbox(label="Execution Information", value="Waiting for analysis...", lines=2,
visible=True)
analyze_button.click(
fn=analyze_video,
inputs=[video_input, language_input, max_speakers],
outputs=[output_section, execution_info_box],
show_progress=True # Enables the progress bar in Gradio
)
# Launch the app
if __name__ == "__main__":
iface.launch()
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