Update app.py

app.py

@@ -12,11 +12,11 @@ def download_models(model_id):
 MODEL_PATH = 'yolov10n.pt'
 model = YOLOv10(MODEL_PATH)
 box_annotator = sv.BoxAnnotator()
-model_path = download_models(model_id)
 
 @spaces.GPU(duration=200)
-def detect(image):
-    results = model(source=image, conf=0.25, verbose=False)[0]
+def detect(image, model_id, image_size, conf_threshold, iou_threshold):
+    model_path = download_models(model_id)
+    results = model(source=image, conf=conf, imgsz=image_size, iou, verbose=False)[0]
     detections = sv.Detections.from_ultralytics(results)
     
     labels = [
@@ -27,29 +27,113 @@ def detect(image):
 
     return annotated_image
 
+def app():
+    with gr.Blocks():
+        with gr.Row():
+            with gr.Column():
+                image = gr.Image(type="numpy", label="Image")
+                
+                model_id = gr.Dropdown(
+                    label="Model",
+                    choices=[
+                        "yolov10n.pt",
+                        "yolov10s.pt",
+                        "yolov10m.pt",
+                        "yolov10b.pt",
+                        "yolov10x.pt",
+                    ],
+                    value="yolov10s.pt",
+                )
+                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="numpy", 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=[
+                [
+                    "images/example1.jpg",
+                    "yolov10s.pt",
+                    640,
+                    0.25,
+                    0.45,
+                ],
+                [
+                    "images/example2.jpg",
+                    "yolov10m.pt",
+                    640,
+                    0.25,
+                    0.45,
+                ],
+            ],
+            fn=yolov10_inference,
+            inputs=[
+                image,
+                model_path,
+                image_size,
+                conf_threshold,
+                iou_threshold,
+            ],
+            outputs=[output_image],
+            cache_examples=True,
+        )
+
 gradio_app = gr.Blocks()
 with gradio_app:
-    gr.HTML(
+    gr.Markdown(
         """
-    <h1 style='text-align: center'>
-    YOLOv10: Real-Time End-to-End Object Detection
-    </h1>
-    """)
-    gr.HTML(
+        # YOLOv10: State-of-the-Art Object Detection
         """
-        <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>
-        """)
-
-    input_image = gr.Image(type="numpy")
-    output_image = gr.Image(type="numpy", label="Annotated Image")
-    
-    gr.Interface(
-        fn=detect,
-        inputs=input_image,
-        outputs=output_image,
     )
+    gr.Markdown(
+        """
+        Detect objects in images using the YOLOv10 model. Select a pre-trained model, adjust the inference settings, and upload an image to see the detected objects.
+        """
+    )
+    with gr.Row():
+        gr.Markdown(
+            """
+            Follow me for more projects and updates:
+            - [Twitter](https://twitter.com/kadirnar_ai)
+            - [GitHub](https://github.com/kadirnar)
+            - [LinkedIn](https://www.linkedin.com/in/kadir-nar/)
+            - [HuggingFace](https://www.huggingface.co/kadirnar/)
+            """
+        )
+
+    app()
 
-gradio_app.launch()
+gradio_app.launch(debug=True)