Update app.py

app.py

@@ -32,35 +32,35 @@ def yolov8_img_inference(
     model = YOLO(model_path)
     model.conf = conf_threshold
     model.iou = iou_threshold
-    # results = model.predict(image, imgsz=image_size, return_outputs=True)
-    results = model.predict(image)
-    # object_prediction_list = []
-    # 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]
-    #             object_prediction = ObjectPrediction(
-    #                 bbox=bbox,
-    #                 category_id=int(category_id),
-    #                 score=score,
-    #                 category_name=category_name,
-    #             )
-    #             object_prediction_list.append(object_prediction)
+    results = model.predict(image, imgsz=image_size, return_outputs=True)
+    # results = model.predict(image)
+    object_prediction_list = []
+    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]
+                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']
-    render = render_result(model=model, image=image, result=results[0])
+    image = read_image(image)
+    output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
+    return output_image['image']
+    # render = render_result(model=model, image=image, result=results[0])
     
     return render
         
@@ -83,7 +83,7 @@ interface_image = gr.Interface(
     outputs=outputs_image,
     title=title,
     examples=image_path,
-    cache_examples=False,
+    cache_examples=True,
     theme='huggingface'
 )
 
@@ -92,4 +92,4 @@ gr.TabbedInterface(
     tab_names=['Image inference']
 ).queue().launch()
 
-# interface_image.launch(debug=True, enable_queue=True)
+interface_image.launch(debug=True, enable_queue=True)