Update app.py

app.py

@@ -1,26 +1,29 @@
+from PIL import Image
+import numpy as np
 import gradio as gr
 from ultralytics import YOLO
-import os
 
 MODEL_PATH = "best.pt"
-
-if not os.path.exists(MODEL_PATH):
-    raise FileNotFoundError("Upload best.pt to the Space root directory.")
-
 model = YOLO(MODEL_PATH)
 
-def predict(image):
-    results = model(image)
-    return results[0].plot()
-
-with gr.Blocks() as demo:
-    gr.Markdown("# 🐶 DogFLW Pose Detection")
-    gr.Markdown("Upload a dog image to detect facial keypoints.")
-
-    input_img = gr.Image(type="pil")
-    output_img = gr.Image()
-
-    run_button = gr.Button("Run Detection")
-    run_button.click(predict, inputs=input_img, outputs=output_img)
-
-demo.launch()
+def predict(image, conf):
+    if image is None:
+        return None
+    results = model.predict(source=np.array(image), conf=conf, imgsz=640, verbose=False)[0]
+    plotted = results.plot()  # BGR numpy array
+    plotted = plotted[:, :, ::-1]  # BGR -> RGB
+    return Image.fromarray(plotted)
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Image(type="pil", label="Input Image"),
+        gr.Slider(0.05, 0.9, value=0.25, step=0.05, label="Confidence"),
+    ],
+    outputs=gr.Image(type="pil", label="Pose Result"),
+    title="DogFLW YOLOv8 Pose",
+    description="Upload a dog image to detect 46 facial landmarks."
+)
+
+if __name__ == "__main__":
+    demo.launch()