Upload app.py

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
+
+
+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
+
+
+def predict(pilimg, confidence, iou):
+    source = pilimg
+    result = detection_model.predict(source, conf=confidence, iou=iou)
+    img_bgr = result[0].plot()
+    out_pilimg = Image.fromarray(img_bgr[..., ::-1])  # RGB-order PIL image
+    return out_pilimg
+
+
+REPO_ID = "skngew/9053220B"
+detection_model = load_model(REPO_ID)
+
+# Student ID
+student_id = "Student ID: 9053220B"
+
+# Create the Gradio interface
+def create_interface():
+    # Persistent state for default values
+    confidence_default = gr.State(0.5)
+    iou_default = gr.State(0.6)
+
+    interface = gr.Interface(
+        fn=predict,
+        inputs=[
+            gr.Image(type="pil", label="Input Image"),
+            gr.Slider(0, 1, value=confidence_default.value, label="Confidence Threshold"),  # Default to 0.5
+            gr.Slider(0, 1, value=iou_default.value, label="IOU Threshold")  # Default to 0.6
+        ],
+        outputs=gr.Image(type="pil", label="Output Image"),
+        title="Object Detection with YOLOv8",
+        description=student_id,
+        live=False,
+    )
+
+    return interface
+
+
+# Launch the Gradio app
+app_interface = create_interface()
+app_interface.launch(share=True)