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# 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_image(image):
results = model(image)
return results.render()[0]
# Create Gradio interfaces for different modes
img_interface = gr.Interface(
fn=detect_image,
inputs=gr.inputs.Image(source="upload"),
outputs="image",
title="Image"
)
vid_interface = gr.Interface(
fn=detect_image,
inputs=gr.inputs.Video(source="upload"),
outputs="video",
title="Videoe"
)
# Create a list of interfaces
interfaces = [img_interface, vid_interface]
# Create the tabbed interface
tabbed_interface = gr.TabbedInterface(interfaces, ["Image", "Video"])
# Launch the tabbed interface
tabbed_interface.launch(debug=True) |