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=gr.Image(source="webcam", tool =None), outputs="image")

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

# demo = gr.TabbedInterface([img_demo, vid_demo], ["Image", "Video"])

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

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):
    results = model(image)
    return results.render()[0]

# Create Gradio interfaces for different modes
webcam_interface = gr.Interface(
    fn=detect_faces,
    inputs=gr.inputs.Image(source="webcam", tool=None),
    outputs="image",
    title="Webcam Mode"
)

file_interface = gr.Interface(
    fn=detect_faces,
    inputs=gr.inputs.Image(source="file"),
    outputs="image",
    title="File Mode"
)

url_interface = gr.Interface(
    fn=detect_faces,
    inputs=gr.inputs.Image(source="url"),
    outputs="image",
    title="URL Mode"
)

# Create a list of interfaces
interfaces = [webcam_interface, file_interface, url_interface]

# Create the tabbed interface
tabbed_interface = gr.Interface(interfaces, layout="horizontal")

# Launch the tabbed interface
tabbed_interface.launch(debug=True)