update app.py

app.py

@@ -1,18 +1,16 @@
 import gradio as gr
 import torch
 from ultralyticsplus import YOLO, render_result
-from ultralytics.yolo.utils.plotting import Annotator
 
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
-model = YOLO('eeshawn11/naruto_hand_seal_detection')
-model.conf = 0.50
-model.iou = 0.45
-model.overrides['max_det'] = 1
-model.to(device)
+yolo_model = YOLO('eeshawn11/naruto_hand_seal_detection')
+yolo_model.conf = 0.50
+yolo_model.iou = 0.45
+yolo_model.overrides['max_det'] = 1
+yolo_model.to(device)
 
 def yolov8_inference(
     image: gr.Image = None,
-    model = model,
     conf_threshold: gr.Slider = 0.50,
     iou_threshold: gr.Slider = 0.45,
 ):
@@ -26,49 +24,12 @@ def yolov8_inference(
     Returns:
         Rendered image
     """
-    # model = YOLO(model_path)
-    # model.conf = conf_threshold
-    # model.iou = iou_threshold
-    # model.overrides['max_det'] = 1
-    # results = model.predict(image, return_outputs=True)
-    results = model.predict(image)
-    print(results[0].boxes)
-    # object_prediction_list = []
-    # annotator = Annotator(image)
-    # for _, image_results in enumerate(results):
-    #     if len(image_results)!=0:
-    #         image_predictions_in_xyxy_format = image_results['det']
-    #         for pred in image_predictions_in_xyxy_format:
-    #             x1, y1, x2, y2 = (
-    #                 int(pred[0]),
-    #                 int(pred[1]),
-    #                 int(pred[2]),
-    #                 int(pred[3]),
-    #             )
-    #             bbox = [x1, y1, x2, y2]
-    #             score = pred[4]
-    #             category_name = model.model.names[int(pred[5])]
-    #             category_id = pred[5]
-    #             annotator.box_label(bbox, f"{category_name} {score}")
-                # object_prediction = ObjectPrediction(
-                #     bbox=bbox,
-                #     category_id=int(category_id),
-                #     score=score,
-                #     category_name=category_name,
-                # )
-                # object_prediction_list.append(object_prediction)
-
-    # image = read_image(image)
-    # output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
-    
-    # return output_image['image']
-    # return annotator.result()
-    render = render_result(model=model, image=image, result=results[0])
+    results = yolo_model.predict(image)
+    render = render_result(model=yolo_model, image=image, result=results[0])
     return render
         
 
 inputs = [
-    # gr.inputs.Image(type="filepath", label="Input Image"),
     gr.Image(source="upload", type="pil", label="Image Upload", interactive=True),
     gr.Slider(minimum=0.0, maximum=1.0, value=0.5, step=0.05, label="Confidence Threshold"),
     gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.05, label="IOU Threshold"),