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import re
import gradio as gr
from PIL import Image
def create_detection_tab(predict_fn, example_images):
with gr.TabItem("Breed Detection"):
gr.HTML("""
<div style='
text-align: center;
padding: 20px 0;
margin: 15px 0;
background: linear-gradient(to right, rgba(66, 153, 225, 0.1), rgba(72, 187, 120, 0.1));
border-radius: 10px;
'>
<p style='
font-size: 1.2em;
margin: 0;
padding: 0 20px;
line-height: 1.5;
background: linear-gradient(90deg, #4299e1, #48bb78);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
font-weight: 600;
'>
Upload a picture of a dog, and the model will predict its breed and provide detailed information!
</p>
<p style='
font-size: 0.9em;
color: #666;
margin-top: 8px;
padding: 0 20px;
'>
Note: The model's predictions may not always be 100% accurate, and it is recommended to use the results as a reference.
</p>
</div>
""")
with gr.Row():
input_image = gr.Image(label="Upload a dog image", type="pil")
output_image = gr.Image(label="Annotated Image")
output = gr.HTML(label="Prediction Results")
initial_state = gr.State()
input_image.change(
predict_fn,
inputs=input_image,
outputs=[output, output_image, initial_state]
)
gr.Examples(
examples=example_images,
inputs=input_image
)
return {
'input_image': input_image,
'output_image': output_image,
'output': output,
'initial_state': initial_state
}
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