Create app.py

app.py

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+import gradio as gr
+from ultralytics import YOLO
+from PIL import Image
+import numpy as np
+import os
+
+# --- Load YOLOv12 model ---
+model_path = "best.pt"
+if os.path.exists(model_path):
+    model = YOLO(model_path)
+else:
+    model = None
+
+# --- Inference function ---
+def detect_ear(image: Image.Image, conf: float = 0.25):
+    if model is not None:
+        try:
+            # Run YOLOv12 inference
+            results = model(np.array(image), conf=conf, verbose=False)
+            annotated = results[0].plot()
+            return Image.fromarray(annotated)
+        except Exception as e:
+            print(f"Model inference failed: {e}")
+    
+    # --- Dummy fallback ---
+    import PIL.ImageDraw as ImageDraw
+    im = image.copy()
+    draw = ImageDraw.Draw(im)
+    w, h = im.size
+    box = (int(w*0.25), int(h*0.25), int(w*0.75), int(h*0.75))
+    draw.rectangle(box, outline="#00ffff", width=5)
+    draw.text((box[0], box[1]-25), "Detected: Possible Condition (Dummy)", fill="#00ffff")
+    return im
+
+# --- Gradio Interface ---
+iface = gr.Interface(
+    fn=detect_ear,
+    inputs=[
+        gr.Image(type="pil", label="Upload Otoscopic Image"),
+        gr.Slider(0.0, 1.0, value=0.25, step=0.01, label="Confidence Threshold")
+    ],
+    outputs=gr.Image(type="pil", label="Detection Result"),
+    title="Automated Ear Disease Detection",
+    description="Detect ear conditions using YOLOv12",
+)
+
+# --- Launch App ---
+iface.launch()