import os import json import gradio as gr import tempfile from PIL import Image, ImageDraw, ImageFont import cv2 from typing import Tuple, Optional import torch from pathlib import Path import time import torch import spaces import os from video_highlight_detector import ( load_model, BatchedVideoHighlightDetector, get_video_duration_seconds ) def load_examples(json_path: str) -> dict: with open(json_path, 'r') as f: return json.load(f) def format_duration(seconds: int) -> str: hours = seconds // 3600 minutes = (seconds % 3600) // 60 secs = seconds % 60 if hours > 0: return f"{hours}:{minutes:02d}:{secs:02d}" return f"{minutes}:{secs:02d}" @spaces.GPU def process_video( video_path: str, progress = gr.Progress() ) -> Tuple[str, str, str, str]: try: # duration = get_video_duration_seconds(video_path) # if duration > 1200: # 20 minutes # return None, None, None, "Video must be shorter than 20 minutes" progress(0.1, desc="Loading model...") model, processor = load_model() detector = BatchedVideoHighlightDetector(model, processor, batch_size=16) progress(0.2, desc="Analyzing video content...") video_description = detector.analyze_video_content(video_path) progress(0.3, desc="Determining highlight types...") highlight_types = detector.determine_highlights(video_description) progress(0.4, desc="Detecting and extracting highlights...") with tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) as tmp_file: output_path = tmp_file.name detector.create_highlight_video(video_path, output_path) # progress(0.9, desc="Adding watermark...") # output_path = temp_output.replace('.mp4', '_watermark.mp4') # add_watermark(temp_output, output_path) os.unlink(output_path) progress(1.0, desc="Complete!") video_description = video_description[:500] + "..." if len(video_description) > 500 else video_description highlight_types = highlight_types[:500] + "..." if len(highlight_types) > 500 else highlight_types return output_path, video_description, highlight_types, None except Exception as e: return None, None, None, f"Error processing video: {str(e)}" def create_ui(examples_path: str): examples_data = load_examples(examples_path) with gr.Blocks() as app: gr.Markdown("# Video Highlight Generator") gr.Markdown("Upload a video (max 20 minutes) and get an automated highlight reel!") with gr.Row(): gr.Markdown("## Example Results") with gr.Row(): for example in examples_data["examples"]: with gr.Column(): gr.Video( value=example["original"]["url"], label=f"Original ({format_duration(example['original']['duration_seconds'])})", interactive=False ) gr.Markdown(f"### {example['title']}") with gr.Column(): gr.Video( value=example["highlights"]["url"], label=f"Highlights ({format_duration(example['highlights']['duration_seconds'])})", interactive=False ) with gr.Accordion("Model chain of thought details", open=False): gr.Markdown(f"#Summary: {example['analysis']['video_description']}") gr.Markdown(f"#Highlights to search for: {example['analysis']['highlight_types']}") # Main interface section gr.Markdown("## Try It Yourself!") with gr.Row(): # Left column: Upload and Process with gr.Column(scale=1): input_video = gr.Video( label="Upload your video (max 20 minutes)", interactive=True, max_length = 1200 ) process_btn = gr.Button("Process Video", variant="primary") status = gr.Markdown(visible=True) # Right column: Progress, Results and Analysis with gr.Column(scale=1): gr.Progress() # Output video (initially hidden) output_video = gr.Video( label="Highlight Video", visible=False, interactive=False, ) # Analysis accordion with gr.Accordion("Model chain of thought details", open=True, visible=False) as analysis_accordion: video_description = gr.Markdown(visible=True) highlight_types = gr.Markdown(visible=True) def on_process(video, progress=gr.Progress()): if not video: return { status: "Please upload a video", output_video: gr.update(visible=False), analysis_accordion: gr.update(visible=False), } status.value = "Processing video..." output_path, desc, highlights, err = process_video(video, progress=progress) if err: return { status: f"Error: {err}", output_video: gr.update(visible=False), analysis_accordion: gr.update(visible=False), } # Format the analysis text desc = f"#Summary: {desc[:500] + '...' if len(desc) > 500 else desc}" highlights = f"#Highlights to search for: {highlights[:500] + '...' if len(highlights) > 500 else highlights}" return { status: "Processing complete!", output_video: gr.update(value=output_path, visible=True), analysis_accordion: gr.update(visible=True), video_description: desc, highlight_types: highlights, } process_btn.click( on_process, inputs=[input_video], outputs=[ status, output_video, analysis_accordion, video_description, highlight_types, ] ) return app # gr.Markdown("## Try It Yourself!") # with gr.Row(): # input_video = gr.Video( # label="Upload your video (max 20 minutes)", # interactive=True # ) # gr.Progress() # process_btn = gr.Button("Process Video", variant="primary") # status = gr.Markdown(visible=True) # with gr.Row() as results_row: # with gr.Column(): # video_description = gr.Markdown(visible=False) # with gr.Column(): # highlight_types = gr.Markdown(visible=False) # with gr.Row() as output_row: # output_video = gr.Video(label="Highlight Video", visible=False) # download_btn = gr.Button("Download Highlights", visible=False) # def on_process(video, progress=gr.Progress()): # if not video: # return { # status: "Please upload a video", # video_description: gr.update(visible=False), # highlight_types: gr.update(visible=False), # output_video: gr.update(visible=False), # download_btn: gr.update(visible=False) # } # status.value = "Processing video..." # output_path, desc, highlights, err = process_video(video, progress=progress) # if err: # return { # status: f"Error: {err}", # video_description: gr.update(visible=False), # highlight_types: gr.update(visible=False), # output_video: gr.update(visible=False), # download_btn: gr.update(visible=False) # } # return { # status: "Processing complete!", # video_description: gr.update(value=desc, visible=True), # highlight_types: gr.update(value=highlights, visible=True), # output_video: gr.update(value=output_path, visible=True), # download_btn: gr.update(visible=True) # } # process_btn.click( # on_process, # inputs=[input_video], # outputs=[status, video_description, highlight_types, output_video, download_btn] # ) # download_btn.click( # lambda x: x, # inputs=[output_video], # outputs=[output_video] # ) # return app if __name__ == "__main__": # Initialize CUDA device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') zero = torch.Tensor([0]).to(device) app = create_ui("video_spec.json") app.launch()