Create app.py

app.py

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+import gradio as gr
+from ultralytics import YOLO
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+MODEL_REPOS = {
+    "Footprint YOLO": "risashinoda/footprint_yolo",     
+    "Feces YOLO": "risashinoda/feces_yolo",     
+    "Egg YOLO": "risashinoda/egg_yolo",       
+    "Bone YOLO": "risashinoda/bone_yolo",      
+    "Feather YOLO": "risashinoda/feather_yolo"
+}
+
+_loaded = {}
+
+def _load(model_key, weights_name="last.pt"):
+    if model_key not in _loaded:
+        repo_id = MODEL_REPOS[model_key]
+        w = hf_hub_download(repo_id=repo_id, filename=weights_name)
+        _loaded[model_key] = YOLO(w)
+    return _loaded[model_key]
+
+def infer(image, model_key, conf_thres=None, iou_nms=None, draw_labels=None):
+    conf_thres = 0.25 if conf_thres is None else float(conf_thres)
+    iou_nms    = 0.70 if iou_nms is None else float(iou_nms)
+    draw_labels = True if draw_labels is None else bool(draw_labels)
+
+    model = _load(model_key)
+    results = model.predict(image, conf=conf_thres, iou=iou_nms)
+    r = results[0]
+
+    plotted = r.plot()
+    img_out = Image.fromarray(plotted[..., ::-1])
+
+    if not draw_labels:
+        import numpy as np, cv2
+        img = results[0].orig_img.copy()
+        if img.ndim == 2:
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+        for box in r.boxes.xyxy.tolist():
+            x1, y1, x2, y2 = map(int, box)
+            cv2.rectangle(img, (x1, y1), (x2, y2), (0, 150, 0), 3)
+        img_out = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+
+    return img_out
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Multi-YOLO Demo (BBox only)")
+    gr.Markdown("## 🔎 **Select a model first**\nChoose one below, then upload an image.")
+
+    with gr.Row():
+        img_in = gr.Image(type="pil", label="Upload an image")
+
+        with gr.Column():
+            # ここは Radio にする
+            model_dd = gr.Radio(
+                choices=list(MODEL_REPOS.keys()),
+                value=list(MODEL_REPOS.keys())[0],
+                label="Select Model",
+                interactive=True
+            )
+
+            conf = gr.Slider(0.05, 1.0, value=0.25, step=0.01, label="Confidence (default 0.25)")
+            iou = gr.Slider(0.1, 0.95, value=0.70, step=0.01, label="NMS IoU (default 0.70)")
+            draw_labels = gr.Checkbox(value=True, label="Draw labels text (off = boxes only)")
+            run_btn = gr.Button("Run")
+
+    img_out = gr.Image(type="pil", label="Detections (boxes only)")
+
+    run_btn.click(fn=infer, inputs=[img_in, model_dd, conf, iou, draw_labels], outputs=[img_out])
+
+if __name__ == "__main__":
+    demo.launch()