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import gradio as gr
from video_processor.processor import VideoAnalyzer
import logging
import torch
import spaces
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
@spaces.GPU
def on_process(video):
# Clear all components when starting new processing
yield [
"", # Clear status
"", # Clear description
gr.update(visible=False) # Hide accordion
]
if not video:
yield [
"Please upload a video",
"",
gr.update(visible=False)
]
return
try:
# Initialize analyzer
yield [
"Initializing video analyzer...",
"",
gr.update(visible=False)
]
analyzer = VideoAnalyzer()
# Process video
yield [
"Analyzing video content...",
"",
gr.update(visible=True)
]
logger.info(f"Processing video: {video}")
result = analyzer.process_video(video)
description = result[0]["description"]
# Format output
formatted_desc = f"### Analysis:\n{description}"
yield [
"Processing complete!",
formatted_desc,
gr.update(visible=True)
]
except Exception as e:
logger.exception("Error processing video")
yield [
f"Error processing video: {str(e)}",
"",
gr.update(visible=False)
]
finally:
# Clean up
torch.cuda.empty_cache()
# Create Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# SmolVLM Video Analyzer")
gr.Markdown("Upload a video to get a detailed analysis of its content.")
with gr.Row():
with gr.Column(scale=1):
input_video = gr.Video(
label="Upload your video",
interactive=True
)
process_btn = gr.Button("Process Video", variant="primary")
with gr.Column(scale=1):
status = gr.Markdown()
analysis_accordion = gr.Accordion(
"Analysis Details",
open=True,
visible=False
)
with analysis_accordion:
video_description = gr.Markdown("")
process_btn.click(
on_process,
inputs=[input_video],
outputs=[
status,
video_description,
analysis_accordion
],
queue=True,
)
if __name__ == "__main__":
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=False
) |