import os
import json
import gradio as gr
import torch
import spaces
import tempfile
from pathlib import Path
import subprocess
import logging
import xml.etree.ElementTree as ET
from xml.dom import minidom
from transformers import AutoProcessor, AutoModelForImageTextToText
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def load_examples(json_path: str) -> dict:
with open(json_path, 'r') as f:
return json.load(f)
def format_duration(seconds: float) -> str:
hours = int(seconds // 3600)
minutes = int((seconds % 3600) // 60)
secs = int(seconds % 60)
return f"{hours:02d}:{minutes:02d}:{secs:02d}"
def get_video_duration_seconds(video_path: str) -> float:
cmd = [
"ffprobe",
"-v", "quiet",
"-print_format", "json",
"-show_format",
video_path
]
result = subprocess.run(cmd, capture_output=True, text=True)
info = json.loads(result.stdout)
return float(info["format"]["duration"])
class VideoHighlightDetector:
def __init__(
self,
model_path: str,
device: str = "cuda",
batch_size: int = 8
):
self.device = device
self.batch_size = batch_size
# Initialize model and processor
self.processor = AutoProcessor.from_pretrained(model_path)
self.model = AutoModelForImageTextToText.from_pretrained(
model_path,
torch_dtype=torch.bfloat16
).to(device)
def analyze_video_content(self, video_path: str) -> str:
system_message = "You are a helpful assistant that can understand videos. Describe what type of video this is and what's happening in it."
messages = [
{
"role": "system",
"content": [{"type": "text", "text": system_message}]
},
{
"role": "user",
"content": [
{"type": "video", "path": video_path},
{"type": "text", "text": "What type of video is this and what's happening in it? Be specific about the content type and general activities you observe."}
]
}
]
inputs = self.processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt"
).to(self.device)
outputs = self.model.generate(**inputs, max_new_tokens=512, do_sample=True, temperature=0.7)
return self.processor.decode(outputs[0], skip_special_tokens=True).lower().split("assistant: ")[1]
def analyze_segment(self, video_path: str) -> str:
"""Analyze a specific video segment and provide a brief description."""
messages = [
{
"role": "system",
"content": [{"type": "text", "text": "Focus only on describing the key dramatic action or notable event occurring in this video segment. Skip general context or scene-setting details unless they are crucial to understanding the main action."}]
},
{
"role": "user",
"content": [
{"type": "video", "path": video_path},
{"type": "text", "text": "WWhat is the main action or notable event happening in this segment? Describe it in one brief sentence."}
]
}
]
inputs = self.processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt"
).to(self.device)
outputs = self.model.generate(**inputs, max_new_tokens=128, do_sample=True, temperature=0.7)
return self.processor.decode(outputs[0], skip_special_tokens=True).split("Assistant: ")[1]
def determine_highlights(self, video_description: str) -> str:
"""Determine what constitutes highlights based on video description."""
messages = [
{
"role": "system",
"content": [{"type": "text", "text": "You are a highlight editor. List archetypal dramatic moments that would make compelling highlights if they appear in the video. Each moment should be specific enough to be recognizable but generic enough to potentially exist in any video of this type."}]
},
{
"role": "user",
"content": [{"type": "text", "text": f"""Here is a description of a video:\n\n{video_description}\n\nList potential highlight moments to look for in this video:"""}]
}
]
inputs = self.processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt"
).to(self.device)
outputs = self.model.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.7)
return self.processor.decode(outputs[0], skip_special_tokens=True).split("Assistant: ")[1]
def process_segment(self, video_path: str, highlight_types: str) -> bool:
messages = [
{
"role": "system",
"content": [{"type": "text", "text": "You are a video highlight analyzer. Your role is to identify moments that have high dramatic value, focusing on displays of skill, emotion, personality, or tension. Compare video segments against provided example highlights to find moments with similar emotional impact and visual interest, even if the specific actions differ."}]
},
{
"role": "user",
"content": [
{"type": "video", "path": video_path},
{"type": "text", "text": f"""Given these highlight examples:\n{highlight_types}\n\nDoes this video contain a moment that matches the core action of one of the highlights? Answer with:\n'yes' or 'no'\nIf yes, justify it"""}]
}
]
inputs = self.processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt"
).to(self.device)
outputs = self.model.generate(**inputs, max_new_tokens=64, do_sample=False)
response = self.processor.decode(outputs[0], skip_special_tokens=True).lower().split("assistant: ")[1]
return "yes" in response
def create_xspf_playlist(video_path: str, segments: list, descriptions: list) -> str:
"""Create XSPF playlist from segments with descriptions."""
# Get video filename with full path
video_filename = os.path.abspath(video_path)
# Create the XML structure as a string
xml_content = [
'',
'',
f' {os.path.basename(video_path)} - Highlights',
' '
]
for idx, ((start_time, end_time), description) in enumerate(zip(segments, descriptions)):
track = [
' '
]
xml_content.extend(track)
xml_content.extend([
' ',
''
])
return '\n'.join(xml_content)
def create_ui(examples_path: str, model_path: str):
examples_data = load_examples(examples_path)
with gr.Blocks() as app:
# gr.Markdown("# VLC Highlight Generator")
# gr.Markdown("Upload a video and get a list of highlights!")
with gr.Row():
gr.HTML("""
VLC Highlight Generator
""")
gr.Markdown("Upload a video and get a list of highlights!")
with gr.Row():
with gr.Column(scale=1):
input_video = gr.Video(
label="Upload your video (max 30 minutes)",
interactive=True
)
process_btn = gr.Button("Process Video", variant="primary")
with gr.Column(scale=1):
output_playlist = gr.File(
label="Highlight Playlist (XSPF)",
visible=False,
interactive=False,
)
status = gr.Markdown()
analysis_accordion = gr.Accordion(
"Analysis Details",
open=True,
visible=False
)
with analysis_accordion:
video_description = gr.Markdown("")
highlight_types = gr.Markdown("")
@spaces.GPU
def on_process(video):
if not video:
return [
None,
"Please upload a video",
"",
"",
gr.update(visible=False)
]
try:
duration = get_video_duration_seconds(video)
if duration > 18000: # 30 minutes
return [
None,
"Video must be shorter than 30 minutes",
"",
"",
gr.update(visible=False)
]
detector = VideoHighlightDetector(model_path=model_path)
# Analyze video content
video_desc = detector.analyze_video_content(video)
formatted_desc = f"### Video Summary:\n{video_desc}"
# Determine highlight types
highlights = detector.determine_highlights(video_desc)
formatted_highlights = f"### Highlight Criteria:\n{highlights}"
# Process video in segments
segment_length = 10.0
kept_segments = []
segment_descriptions = []
for start_time in range(0, int(duration), int(segment_length)):
end_time = min(start_time + segment_length, duration)
# Create temporary segment
with tempfile.NamedTemporaryFile(suffix='.mp4') as temp_segment:
cmd = [
"ffmpeg",
"-y",
"-i", video,
"-ss", str(start_time),
"-t", str(segment_length),
"-c:v", "libx264",
"-preset", "ultrafast",
temp_segment.name
]
subprocess.run(cmd, check=True)
if detector.process_segment(temp_segment.name, highlights):
# Get segment description
description = detector.analyze_segment(temp_segment.name)
kept_segments.append((start_time, end_time))
segment_descriptions.append(description)
if kept_segments:
# Create XSPF playlist
playlist_content = create_xspf_playlist(video, kept_segments, segment_descriptions)
# Save playlist to temporary file
with tempfile.NamedTemporaryFile(mode='w', suffix='.xspf', delete=False) as f:
f.write(playlist_content)
playlist_path = f.name
return [
gr.update(value=playlist_path, visible=True),
"Processing complete! Download the XSPF playlist.",
formatted_desc,
formatted_highlights,
gr.update(visible=True)
]
else:
return [
None,
"No highlights detected in the video.",
formatted_desc,
formatted_highlights,
gr.update(visible=True)
]
except Exception as e:
logger.exception("Error processing video")
return [
None,
f"Error processing video: {str(e)}",
"",
"",
gr.update(visible=False)
]
finally:
torch.cuda.empty_cache()
process_btn.click(
on_process,
inputs=[input_video],
outputs=[
output_playlist,
status,
video_description,
highlight_types,
analysis_accordion
],
queue=True,
)
return app
if __name__ == "__main__":
app = create_ui("video_spec.json", "HuggingFaceTB/SmolVLM2-2.2B-Instruct")
app.launch()