| | import gradio as gr |
| | from inference import predict, predict_batch |
| |
|
| | APP_TITLE = "# Face Shape Classification — EfficientNetB4 (300×300)" |
| | APP_DESC = """ |
| | Model EfficientNetB4 (ImageNet) fine-tuned pada 5 kelas: Heart, Oblong, Oval, Round, Square. |
| | |
| | • Input: Foto wajah frontal RGB (1 orang), auto-resize 300×300. |
| | • Output: Prediksi + confidence (Top-5). |
| | • Disclaimer: Untuk penelitian/edukasi. |
| | """ |
| |
|
| | with gr.Blocks(theme=gr.themes.Soft()) as demo: |
| | gr.Markdown(APP_TITLE) |
| | gr.Markdown(APP_DESC) |
| |
|
| | with gr.Row(): |
| | inp = gr.Image(type="pil", label="Upload face (frontal)") |
| | out = gr.Label(num_top_classes=5, label="Predictions") |
| |
|
| | with gr.Row(): |
| | btn = gr.Button("Predict", variant="primary") |
| | gr.ClearButton([inp, out]) |
| |
|
| | |
| | btn.click(predict, inputs=inp, outputs=out, api_name="predict") |
| |
|
| | with gr.Tab("Batch (optional)"): |
| | gal = gr.Gallery(label="Images", columns=4, height="auto") |
| | out_gal = gr.JSON(label="Batch outputs") |
| | runb = gr.Button("Run batch") |
| | runb.click(predict_batch, inputs=gal, outputs=out_gal, api_name="predict_batch") |
| |
|
| | if __name__ == "__main__": |
| | demo.launch() |
| |
|
| |
|
