Create app.py

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+import torch
+from ultralyticsplus import YOLO, render_result
+
+def yoloFunc(image: gr.inputs.Image = None,
+             image_size: int = 640,
+             conf_threshold: float = 0.4,
+             iou_threshold: float = 0.5):
+    model_path = 'best.pt'
+    model = YOLO(model_path)
+    
+    results = model.predict(image,
+                            image_size=image_size,
+                            conf_threshold=conf_threshold,
+                            iou_threshold=iou_threshold
+                            )
+    
+    box = results[0].boxes
+    
+    render = render_result(model=model, image=image, results=results[0])
+    return render
+
+inputs = [
+    gr.inputs.Image(type='filepath', label="Input Image"),
+    gr.inputs.Slider(minimum=320, maximum=1024, default=640, step=32, label="Image Size"),
+    gr.inputs.Slider(minimum=0.1, maximum=1.0, default=0.4, steps=0.05, label="Confidence Threshold"),
+    gr.inputs.Slider(minimum=0.1, maximum=1.0, default=0.5, steps=0.05, label="IOU Threshold")
+]
+
+outputs = gr.outputs.Image(type='filepath', label="Output Image")
+
+title = "Pothole Detection"
+
+yolo_app = gr.Interface(
+    fn=yoloFunc,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,   
+)
+
+yolo_app.launch(debug=True, enable_queue=True)