app.py

# import gradio as gr
# import torch

# model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt')
# # Define the face detector function
# def detect_faces(image):
#    # Loading in yolov5s - you can switch to larger models such as yolov5m or yolov5l, or smaller such as yolov5n
#     results = model(image)

#     return results.render()[0]

# # Create a Gradio interface
# iface = gr.Interface(fn=detect_faces, inputs="image", outputs="image")

# # Launch the interface
# iface.launch(debug=True)

import gradio as gr

def snap(video):
    results = model(image)
    return [results.render()[0]]
    
# def detect_faces(image):
# #    # Loading in yolov5s - you can switch to larger models such as yolov5m or yolov5l, or smaller such as yolov5n
#      results = model(image)
#     return results.render()[0]

demo = gr.Interface(
    snap,
    gr.Video(source="webcam"),
    "image",
)

if __name__ == "__main__":
    demo.launch()