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=8) # 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 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']}") gr.Markdown("## Try It Yourself!") with gr.Row(): with gr.Column(scale=1): input_video = gr.Video( label="Upload your video (max 20 minutes)", interactive=True ) process_btn = gr.Button("Process Video", variant="primary") with gr.Column(scale=1): output_video = gr.Video( label="Highlight Video", visible=False, interactive=False, ) status = gr.Markdown() analysis_accordion = gr.Accordion( "Model chain of thought details", open=True, visible=False ) with analysis_accordion: video_description = gr.Markdown("", elem_id="video_desc") highlight_types = gr.Markdown("", elem_id="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 # ) # process_btn = gr.Button("Process Video", variant="primary") # # Right column: Progress and Analysis # with gr.Column(scale=1): # # Output video (initially hidden) # output_video = gr.Video( # label="Highlight Video", # visible=False, # interactive=False, # ) # status = gr.Markdown() # with gr.Accordion("Model chain of thought details", open=True, visible=True) as analysis_accordion: # video_description = gr.Markdown("", elem_id="video_desc") # highlight_types = gr.Markdown("", elem_id="highlight_types") @spaces.GPU def on_process(video): if not video: return { status: "Please upload a video", video_description: "", highlight_types: "", output_video: gr.update(visible=False), analysis_accordion: gr.update(visible=False) } try: duration = get_video_duration_seconds(video) if duration > 1200: # 20 minutes return { status: "Video must be shorter than 20 minutes", video_description: "", highlight_types: "", output_video: gr.update(visible=False), analysis_accordion: gr.update(visible=False) } # Make accordion visible as soon as processing starts yield { status: "Loading model...", video_description: "", highlight_types: "", output_video: gr.update(visible=False), analysis_accordion: gr.update(visible=True) } model, processor = load_model() detector = BatchedVideoHighlightDetector(model, processor, batch_size=8) yield { status: "Analyzing video content...", video_description: "", highlight_types: "", output_video: gr.update(visible=False), analysis_accordion: gr.update(visible=True) } video_desc = detector.analyze_video_content(video) formatted_desc = f"#Summary: {video_desc[:500] + '...' if len(video_desc) > 500 else video_desc}" # Update description as soon as it's available yield { status: "Determining highlight types...", video_description: formatted_desc, highlight_types: "", output_video: gr.update(visible=False), analysis_accordion: gr.update(visible=True) } highlights = detector.determine_highlights(video_desc) formatted_highlights = f"#Highlights to search for: {highlights[:500] + '...' if len(highlights) > 500 else highlights}" # Update highlights as soon as they're available yield { status: "Detecting and extracting highlights...", video_description: formatted_desc, highlight_types: formatted_highlights, output_video: gr.update(visible=False), analysis_accordion: gr.update(visible=True) } with tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) as tmp_file: temp_output = tmp_file.name detector.create_highlight_video(video, temp_output) return { status: "Processing complete!", video_description: formatted_desc, highlight_types: formatted_highlights, output_video: gr.update(value=temp_output, visible=True), analysis_accordion: gr.update(visible=True) } except Exception as e: return { status: f"Error processing video: {str(e)}", video_description: "", highlight_types: "", output_video: gr.update(visible=False), analysis_accordion: gr.update(visible=False) } process_btn.click( on_process, inputs=[input_video], outputs=[status, video_description, highlight_types, output_video, analysis_accordion] ) return app # @spaces.GPU # def on_process(video, progress=gr.Progress()): # if not video: # return { # status: "Please upload a video", # video_description: "", # highlight_types: "", # output_video: gr.update(visible=False) # } # try: # duration = get_video_duration_seconds(video) # if duration > 1200: # 20 minutes # return { # status: "Video must be shorter than 20 minutes", # video_description: "", # highlight_types: "", # output_video: gr.update(visible=False) # } # progress(0.1, desc="Loading model...") # status.value = "Loading model..." # model, processor = load_model() # detector = BatchedVideoHighlightDetector(model, processor, batch_size=8) # progress(0.2, desc="Analyzing video content...") # status.value = "Analyzing video content..." # video_desc = detector.analyze_video_content(video) # # Update description in real-time # video_description.value = f"#Summary: {video_desc[:500] + '...' if len(video_desc) > 500 else video_desc}" # progress(0.3, desc="Determining highlight types...") # status.value = "Determining highlight types..." # highlights = detector.determine_highlights(video_desc) # # Update highlights in real-time # highlight_types.value = f"#Highlights to search for: {highlights[:500] + '...' if len(highlights) > 500 else highlights}" # progress(0.4, desc="Detecting and extracting highlights...") # status.value = "Detecting and extracting highlights..." # with tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) as tmp_file: # temp_output = tmp_file.name # detector.create_highlight_video(video, temp_output) # # progress(0.9, desc="Adding watermark...") # # status.value = "Adding watermark..." # # output_path = temp_output.replace('.mp4', '_watermark.mp4') # # add_watermark(temp_output, output_path) # # os.unlink(temp_output) # progress(1.0, desc="Complete!") # return { # status: "Processing complete!", # video_description: video_description.value, # highlight_types: highlight_types.value, # output_video: gr.update(value=temp_output, visible=True) # } # except Exception as e: # return { # status: f"Error processing video: {str(e)}", # video_description: "", # highlight_types: "", # output_video: gr.update(visible=False) # } # process_btn.click( # on_process, # inputs=[input_video], # outputs=[status, video_description, highlight_types, 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()