app.py

import sys
import gradio as gr
import os
import tempfile
import cv2
import requests
from ultralytics import YOLO

# Remove extra CLI arguments (like "--import") from Spaces.
sys.argv = [arg for arg in sys.argv if arg != "--import"]

# Load the YOLO11-pose model (will auto-download if needed)
model = YOLO("yolo11n-pose.pt")

def process_input(uploaded_file, youtube_link, image_url):
    """
    Process an uploaded file, a YouTube link, or an image URL for pose detection.
    Returns a tuple: (annotated_file_path, status_message).  
    Priority is given to the YouTube link first, then image URL, then the uploaded file.
    """
    input_path = None

    # Priority 1: YouTube link
    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 None, "No suitable mp4 stream found."
            input_path = stream.download()
        except Exception as e:
            return None, f"Error downloading video: {e}"
    # Priority 2: Image URL
    elif image_url and image_url.strip():
        try:
            response = requests.get(image_url, stream=True)
            if response.status_code != 200:
                return None, f"Error downloading image: HTTP {response.status_code}"
            # Save the downloaded image to a temporary file.
            temp_image_path = os.path.join(tempfile.gettempdir(), "downloaded_image.jpg")
            with open(temp_image_path, "wb") as f:
                f.write(response.content)
            input_path = temp_image_path
        except Exception as e:
            return None, f"Error downloading image: {e}"
    # Priority 3: Uploaded file
    elif uploaded_file is not None:
        input_path = uploaded_file.name
    else:
        return None, "Please provide a YouTube link, image URL, or upload a file."

    # Run pose detection (with save=True so annotated results are written to disk)
    try:
        results = model.predict(source=input_path, save=True)
    except Exception as e:
        return None, f"Error running prediction: {e}"

    output_path = None
    try:
        # If the results object has a save_path attribute, use it.
        if hasattr(results[0], "save_path"):
            output_path = results[0].save_path
        else:
            # Otherwise, generate an annotated image using plot() and save it manually.
            annotated = results[0].plot()  # returns a numpy array
            output_path = os.path.join(tempfile.gettempdir(), "annotated.jpg")
            cv2.imwrite(output_path, annotated)
    except Exception as e:
        return None, f"Error processing the file: {e}"

    # If the input came from YouTube or image URL, remove the temporary file.
    if (youtube_link or (image_url and image_url.strip())) and input_path and os.path.exists(input_path):
        os.remove(input_path)

    return output_path, "Success!"

# Define the Gradio interface.
with gr.Blocks() as demo:
    # Display the default image (crowdresult.jpg) at the top.
    gr.Markdown("# Pose Detection with YOLO11-pose")
    gr.Image(value="crowdresult.jpg", label="Crowd Result", interactive=False)
    gr.Markdown("Upload an image/video, provide an image URL, or supply a YouTube link to detect human poses.")

    with gr.Row():
        file_input = gr.File(label="Upload Image/Video")
    with gr.Row():
        youtube_input = gr.Textbox(label="YouTube Link", placeholder="https://...")
        image_url_input = gr.Textbox(label="Image URL", placeholder="https://...")

    output_file = gr.File(label="Download Annotated Output")
    output_text = gr.Textbox(label="Status", interactive=False)
    run_button = gr.Button("Run Pose Detection")

    run_button.click(process_input, inputs=[file_input, youtube_input, image_url_input],
                     outputs=[output_file, output_text])

# Only launch the interface if executed directly.
if __name__ == "__main__":
    demo.launch()