Update app.py

app.py

@@ -4,65 +4,61 @@ from sahi.prediction import ObjectPrediction
 from sahi.utils.cv import visualize_object_predictions, read_image
 from ultralyticsplus import YOLO, render_result
 
+# your example images
 image_path = [
     ['test/web form.jpg', 'foduucom/web-form-ui-field-detection', 640, 0.25, 0.45],
     ['test/web form2.jpg', 'foduucom/web-form-ui-field-detection', 640, 0.25, 0.45]
 ]
 
-
 def yolov8_inference(
-    image: gr.inputs.Image = None,
-    model_path: gr.inputs.Dropdown = None,
-    image_size: gr.inputs.Slider = 640,
-    conf_threshold: gr.inputs.Slider = 0.25,
-    iou_threshold: gr.inputs.Slider = 0.45,
+    image,           # will be a filepath string
+    model_path,      # string
+    image_size,      # int
+    conf_threshold,  # float
+    iou_threshold    # float
 ):
-    """
-    YOLOv8 inference function
-    Args:
-        image: Input image
-        model_path: Path to the model
-        image_size: Image size
-        conf_threshold: Confidence threshold
-        iou_threshold: IOU threshold
-    Returns:
-        Rendered image
-    """
+    # load and configure the model
     model = YOLO(model_path)
-    model.overrides['conf'] = conf_threshold
-    model.overrides['iou']= iou_threshold
-    model.overrides['agnostic_nms'] = False  # NMS class-agnostic
-    model.overrides['max_det'] = 1000 
-    image = read_image(image)
-    results = model.predict(image)
-    render = render_result(model=model, image=image, result=results[0])
-    
-    return render
-        
+    model.overrides.update({
+        'conf': conf_threshold,
+        'iou': iou_threshold,
+        'agnostic_nms': False,
+        'max_det': 1000
+    })
+
+    # read & run
+    img = read_image(image)
+    results = model.predict(img)
+    rendered = render_result(model=model, image=img, result=results[0])
+    return rendered
 
+# define components using the new API
 inputs = [
-    gr.inputs.Image(type="filepath", label="Input Image"),
-    gr.inputs.Dropdown(["foduucom/web-form-ui-field-detection"], 
-                       default="foduucom/web-form-ui-field-detection", label="Model"),
-    gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
-    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
-    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
+    gr.Image(type="filepath", label="Input Image"),
+    gr.Dropdown(
+        choices=["foduucom/web-form-ui-field-detection"],
+        value="foduucom/web-form-ui-field-detection",
+        label="Model"
+    ),
+    gr.Slider(320, 1280, step=32, value=640, label="Image Size"),
+    gr.Slider(0.0, 1.0, step=0.05, value=0.25, label="Confidence Threshold"),
+    gr.Slider(0.0, 1.0, step=0.05, value=0.45, label="IOU Threshold"),
 ]
 
-outputs = gr.outputs.Image(type="filepath", label="Output Image")
-title = "Ui form : web form ui field Detection in Images"
+outputs = gr.Image(type="filepath", label="Output Image")
+
+title = "Web-Form UI Field Detection"
 
-interface_image = gr.Interface(
+# single-tab interface
+interface = gr.Interface(
     fn=yolov8_inference,
     inputs=inputs,
     outputs=outputs,
     title=title,
     examples=image_path,
     cache_examples=False,
-    theme='huggingface'
+    theme="huggingface"
 )
 
-gr.TabbedInterface(
-    [interface_image],
-    tab_names=['Image inference']
-).queue().launch()
+if __name__ == "__main__":
+    interface.launch()