Create app.py

app.py

@@ -0,0 +1,122 @@
+import gradio as gr
+from ultralytics import YOLO
+import spaces
+
+@spaces.GPU(duration=200)
+def LeYOLO_inference(image, model_id, image_size, conf_threshold, iou_threshold):
+    model = YOLO(f"{model_id}.pt")
+    results = model(source=image, imgsz=image_size, iou=iou_threshold, conf=conf_threshold, verbose=False)[0]
+    
+
+def app():
+    with gr.Blocks():
+        with gr.Row():
+            with gr.Column():
+                image = gr.Image(type="pil", label="Image")
+                
+                model_id = gr.Dropdown(
+                    label="Model",
+                    choices=[
+                        "yolov10n",
+                        "yolov10s",
+                        "yolov10m",
+                        "yolov10b",
+                        "yolov10l",
+                        "yolov10x",
+                    ],
+                    value="yolov10m",
+                )
+                image_size = gr.Slider(
+                    label="Image Size",
+                    minimum=320,
+                    maximum=1280,
+                    step=32,
+                    value=640,
+                )
+                conf_threshold = gr.Slider(
+                    label="Confidence Threshold",
+                    minimum=0.1,
+                    maximum=1.0,
+                    step=0.1,
+                    value=0.25,
+                )
+                iou_threshold = gr.Slider(
+                    label="IoU Threshold",
+                    minimum=0.1,
+                    maximum=1.0,
+                    step=0.1,
+                    value=0.45,
+                )
+                yolov10_infer = gr.Button(value="Detect Objects")
+
+            with gr.Column():
+                output_image = gr.Image(type="pil", label="Annotated Image")
+
+        yolov10_infer.click(
+            fn=yolov10_inference,
+            inputs=[
+                image,
+                model_id,
+                image_size,
+                conf_threshold,
+                iou_threshold,
+            ],
+            outputs=[output_image],
+        )
+
+        gr.Examples(
+            examples=[
+                [
+                    "dog.jpeg",
+                    "yolov10x",
+                    640,
+                    0.25,
+                    0.45,
+                ],
+                [
+                    "huggingface.jpg",
+                    "yolov10m",
+                    640,
+                    0.25,
+                    0.45,
+                ],
+                [
+                    "zidane.jpg",
+                    "yolov10b",
+                    640,
+                    0.25,
+                    0.45,
+                ],
+            ],
+            fn=LeYOLO_inference,
+            inputs=[
+                image,
+                model_id,
+                image_size,
+                conf_threshold,
+                iou_threshold,
+            ],
+            outputs=[output_image],
+            cache_examples="lazy",
+        )
+
+gradio_app = gr.Blocks()
+with gradio_app:
+    gr.HTML(
+        """
+    <h1 style='text-align: center'>
+    YOLOv10: Real-Time End-to-End Object Detection
+    </h1>
+    """)
+    gr.HTML(
+        """
+        <h3 style='text-align: center'>
+        Follow me for more!
+        <a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a>  | <a href='https://www.huggingface.co/kadirnar/' target='_blank'>HuggingFace</a>
+        </h3>
+        """)
+    with gr.Row():
+        with gr.Column():
+            app()
+
+gradio_app.launch(debug=True)