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app.py

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+from ultralytics import YOLO
+from PIL import Image
+import gradio as gr
+from huggingface_hub import snapshot_download
+import os
+
+#public model path location
+MODEL_REPO_ID = "aunghlaing/6319250G"
+
+#Organizations model path location
+#MODEL_REPO_ID = "ITI107-2024S2/6319250G"
+
+#load model
+def load_model(repo_id):
+    download_dir = snapshot_download(repo_id)
+    print(download_dir)
+    path  = os.path.join(download_dir, "best_int8_openvino_model")
+    print(path)
+    detection_model = YOLO(path, task='detect')
+    return detection_model
+    
+detection_model = load_model(MODEL_REPO_ID)
+
+#Student ID
+student_info = "Student Id: 6319250G, Name: AHT"
+
+#prdeict 
+def predict(pilimg):
+    source = pilimg
+    result = detection_model.predict(source, conf=0.5, iou=0.5)
+    img_bgr = result[0].plot()
+    out_pilimg = Image.fromarray(img_bgr[..., ::-1])  # RGB-order PIL image
+    return out_pilimg
+    
+#UI interface
+gr.Markdown("# Mask & Shark Detection (Mask/Shark)")
+gr.Markdown(student_info)
+gr.Interface(fn=predict,
+             inputs=gr.Image(type="pil",label="Input"),
+             outputs=gr.Image(type="pil",label="Output"),
+             title="Mask & Shark  Detection (Mask/Shark)",
+             description=student_info,
+             ).launch(share=True)