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318b10c 8343674 a722bd4 ec4fcee 8343674 ec4fcee 318b10c ec4fcee 318b10c d2f7145 ec4fcee a722bd4 8343674 ec4fcee 8343674 318b10c 8343674 a722bd4 318b10c 8343674 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | 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])
# Expose stable API names for @gradio/client consumers
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()
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