breed_detection.py

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
#     }


def create_detection_tab(predict_fn, example_images):
    # 首先定義CSS樣式
    custom_css = """
        /* 標籤樣式 */
        .tab-nav {
            padding: 0 !important;
            margin-bottom: 20px !important;
            border-bottom: 1px solid #e2e8f0 !important;
        }
        
        /* 所有標籤的基本樣式 */
        .tab-nav button {
            padding: 12px 16px !important;
            margin: 0 8px !important;
            font-size: 1.1em !important;
            font-weight: 500 !important;
            transition: all 0.3s ease !important;
            border-bottom: 2px solid transparent !important;
            background: none !important;
            position: relative !important;
        }
        
        /* 被選中的標籤樣式 */
        .tab-nav button.selected {
            color: #4299e1 !important;
            border-bottom: 2px solid #4299e1 !important;
            background: linear-gradient(to bottom, rgba(66, 153, 225, 0.1), transparent) !important;
        }
        
        /* hover 效果 */
        .tab-nav button:hover {
            color: #4299e1 !important;
            background: rgba(66, 153, 225, 0.05) !important;
        }
    """

    with gr.Blocks(css=custom_css) as detection_tab:
        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
    }