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import gradio as gr
import os
from ultralytics import YOLO
# Load the YOLO11-pose model (auto-downloads if not present)
model = YOLO("yolo11n-pose.pt")
def process_input(uploaded_file, youtube_link):
"""
Process an uploaded file or a YouTube link to perform pose detection.
Returns the path to the annotated output.
"""
if youtube_link and youtube_link.strip():
try:
from pytube import YouTube
yt = YouTube(youtube_link)
stream = yt.streams.filter(file_extension='mp4', progressive=True)\
.order_by("resolution").desc().first()
if stream is None:
return "No suitable mp4 stream found."
input_path = stream.download()
except Exception as e:
return f"Error downloading video: {e}"
elif uploaded_file is not None:
input_path = uploaded_file.name
else:
return "Please provide an uploaded file or a YouTube link."
# Run pose detection and save the annotated output.
results = model.predict(source=input_path, save=True)
try:
output_path = results[0].save_path
except Exception as e:
return f"Error processing the file: {e}"
# Optionally remove the downloaded video if applicable.
if youtube_link and os.path.exists(input_path):
os.remove(input_path)
return output_path
# Define the Gradio Blocks interface as a global variable.
demo = gr.Blocks()
with demo:
gr.Markdown("# Pose Detection with YOLO11-pose")
gr.Markdown("Upload an image/video or provide a YouTube link to detect human poses.")
with gr.Row():
file_input = gr.File(label="Upload Image/Video")
youtube_input = gr.Textbox(label="Or enter a YouTube link", placeholder="https://...")
output_file = gr.File(label="Download Annotated Output")
run_button = gr.Button("Run Pose Detection")
run_button.click(process_input, inputs=[file_input, youtube_input], outputs=output_file)
# Only launch the interface if this file is executed directly.
if __name__ == "__main__":
demo.launch()
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