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