Create app.py

app.py

@@ -0,0 +1,46 @@
+import gradio as gr
+import PIL.Image as Image
+
+from ultralytics import ASSETS, YOLO
+
+model = YOLO("yolov8n.pt")
+
+
+def predict_image(img, conf_threshold, iou_threshold):
+    """Predicts objects in an image using a YOLOv8 model with adjustable confidence and IOU thresholds."""
+    results = model.predict(
+        source=img,
+        conf=conf_threshold,
+        iou=iou_threshold,
+        show_labels=True,
+        show_conf=True,
+        imgsz=640,
+    )
+
+    for r in results:
+        im_array = r.plot()
+        im = Image.fromarray(im_array[..., ::-1])
+
+    return im
+
+
+with gr.Blocks() as demo:
+  with gr.Row():
+    with gr.Column():
+      input_image = gr.Image(type="pil", label="Upload Image")
+      conf = gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold")
+      iou = gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold")
+      with gr.Row():
+        reset = gr.ClearButton([input_image])
+        submit = gr.Button("Submit")
+    with gr.Column():
+      output_image = gr.Image(type="pil", label="Result")
+      submit.click(fn=predict_image, inputs=[input_image, conf,iou], outputs=[output_image])
+  examples = gr.Examples(([
+          ['https://ultralytics.com/images/zidane.jpg', 0.25, 0.45],
+          ['https://unsplash.com/photos/2pPw5Glro5I/download?ixid=M3wxMjA3fDB8MXxzZWFyY2h8Mnx8dXJsfGVufDB8fHx8MTcyMTgwNzkyMnww&force=true', 0.5, 0.3],
+          ['https://unsplash.com/photos/5CUyfyde_io/download?ixid=M3wxMjA3fDB8MXxzZWFyY2h8OHx8dG9reW98ZW58MHx8fHwxNzIxODY4MzQzfDA&force=true', 0.3, 0.3]
+      ]),[input_image,conf,iou]),
+
+if __name__ == "__main__":
+    demo.launch()