Create app.py

app.py

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+from huggingface_hub import hf_hub_download
+from inference import YOLOv10
+
+model_file = hf_hub_download(
+    repo_id="onnx-community/yolov10n", filename="onnx/model.onnx"
+)
+
+model = YOLOv10(model_file)
+
+def detection(image, conf_threshold=0.3):
+    image = cv2.resize(image, (model.input_width, model.input_height))
+    new_image = model.detect_objects(image, conf_threshold)
+    return new_image
+
+import gradio as gr
+from gradio_webrtc import WebRTC
+
+css = """.my-group {max-width: 600px !important; max-height: 600px !important;}
+         .my-column {display: flex !important; justify-content: center !important; align-items: center !important;}"""
+
+with gr.Blocks(css=css) as demo:
+    gr.HTML(
+        """
+        <h1 style='text-align: center'>
+        YOLOv10 Webcam Stream (Powered by WebRTC ⚡️)
+        </h1>
+        """
+    )
+    with gr.Column(elem_classes=["my-column"]):
+        with gr.Group(elem_classes=["my-group"]):
+            image = WebRTC(label="Stream", rtc_configuration=rtc_configuration)
+            conf_threshold = gr.Slider(
+                label="Confidence Threshold",
+                minimum=0.0,
+                maximum=1.0,
+                step=0.05,
+                value=0.30,
+            )
+
+        image.stream(
+            fn=detection, inputs=[image, conf_threshold], outputs=[image], time_limit=10
+        )
+
+if __name__ == "__main__":
+    demo.launch()