Update app.py with complete YOLOv10 code from GitHub

app.py

@@ -1,23 +1,167 @@
 import gradio as gr
+import spaces
+from PIL import Image
 from ultralytics import YOLO
-import PIL
-
-model = YOLO("yolov8n.pt")
-
-def predict_image(img):
-    results = model.predict(source=img)
-    annotated_img = results[0].plot()
-    annotated_img = PIL.Image.fromarray(annotated_img[...,::-1])
-    
-    return annotated_img
-
-gradio_app = gr.Interface(
-    fn=predict_image,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Image(type="pil"),
-    title="Object Detection",
-    examples=["dog.webp", "zidane.jpg", "huggingface.png"]
-)
+
+# Load Models
+models = {
+    "yolov10n": YOLO("jameslahm/yolov10n"),
+    "yolov10s": YOLO("jameslahm/yolov10s"),
+    "yolov10m": YOLO("jameslahm/yolov10m"),
+    "yolov10b": YOLO("jameslahm/yolov10b"),
+    "yolov10l": YOLO("jameslahm/yolov10l"),
+    "yolov10x": YOLO("jameslahm/yolov10x"),
+}
+
+
+@spaces.GPU(duration=30)
+def yolov10_inference(image, model_id, image_size, conf_threshold, iou_threshold):
+    model = models[model_id]
+    results = model.predict(
+        source=image,
+        imgsz=image_size,
+        conf=conf_threshold,
+        iou=iou_threshold,
+    )
+    annotated_image = results[0].plot()
+    return Image.fromarray(annotated_image[..., ::-1])
+
+
+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=0.0,
+                    maximum=1.0,
+                    step=0.05,
+                    value=0.25,
+                )
+                iou_threshold = gr.Slider(
+                    label="IoU Threshold",
+                    minimum=0.0,
+                    maximum=1.0,
+                    step=0.05,
+                    value=0.45,
+                )
+                yolov10_infer = gr.Button(value="Detect Objects")
+
+            with gr.Column():
+                output_image = gr.Image(type="pil", label="Annotated Image")
+
+        gr.Examples(
+            examples=[
+                ["dog.jpeg", "yolov10m", 640, 0.25, 0.45],
+                ["huggingface.jpg", "yolov10m", 640, 0.25, 0.45],
+                ["zidane.jpg", "yolov10m", 640, 0.25, 0.45],
+            ],
+            fn=yolov10_inference,
+            inputs=[image, model_id, image_size, conf_threshold, iou_threshold],
+            outputs=[output_image],
+            cache_examples='lazy',
+        )
+
+        yolov10_infer.click(
+            fn=yolov10_inference,
+            inputs=[image, model_id, image_size, conf_threshold, iou_threshold],
+            outputs=[output_image],
+        )
+
+
+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():
+            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=0.0,
+                maximum=1.0,
+                step=0.05,
+                value=0.25,
+            )
+            iou_threshold = gr.Slider(
+                label="IoU Threshold",
+                minimum=0.0,
+                maximum=1.0,
+                step=0.05,
+                value=0.45,
+            )
+            yolov10_infer = gr.Button(value="Detect Objects")
+
+        with gr.Column():
+            output_image = gr.Image(type="pil", label="Annotated Image")
+
+    gr.Examples(
+        examples=[
+            ["dog.jpeg", "yolov10m", 640, 0.25, 0.45],
+            ["huggingface.jpg", "yolov10m", 640, 0.25, 0.45],
+            ["zidane.jpg", "yolov10m", 640, 0.25, 0.45],
+        ],
+        fn=yolov10_inference,
+        inputs=[image, model_id, image_size, conf_threshold, iou_threshold],
+        outputs=[output_image],
+        cache_examples='lazy',
+    )
+
+    yolov10_infer.click(
+        fn=yolov10_inference,
+        inputs=[image, model_id, image_size, conf_threshold, iou_threshold],
+        outputs=[output_image],
+    )
 
 if __name__ == "__main__":
     gradio_app.launch()