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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 | |
| } | |