update app.py

app.py

@@ -43,11 +43,12 @@ def get_figure(in_pil_img, in_results):
     for score, label, box in zip(in_results["scores"], in_results["labels"], in_results["boxes"]):
         selected_color = choice(COLORS)
 
-        x, y, w, h = torch.round(box[0]).item(), torch.round(box[1]).item(), torch.round(box[2]-box[0]).item(), torch.round(box[3]-box[1]).item()
-        #x, y, w, h = int(box[0]), int(box[1]), int(box[2]-box[0]), int(box[3]-box[1])
+        box_int = [i.item() for i in torch.round(box).to(torch.int32)]
+        x, y, w, h = box[0], box[1], box[2]-box[0], box[3]-box[1]
+        #x, y, w, h = torch.round(box[0]).item(), torch.round(box[1]).item(), torch.round(box[2]-box[0]).item(), torch.round(box[3]-box[1]).item()
 
         ax.add_patch(plt.Rectangle((x, y), w, h, fill=False, color=selected_color, linewidth=2))
-        ax.text(x, y, f"{model_tiny.config.id2label[label.item()]}: {round(score.item()*100, 2)}%", fontdict=fdic)
+        ax.text(x, y, f"{model_tiny.config.id2label[label.item()]}: {round(score.item()*100, 2)}%", fontdict=fdic, alpha=0.8)
 
     plt.axis("off")
 
@@ -71,7 +72,6 @@ def infer(in_pil_img, in_model="yolos-tiny", in_threshold=0.9):
         # convert outputs (bounding boxes and class logits) to COCO API
         results = image_processor_tiny.post_process_object_detection(outputs, threshold=in_threshold, target_sizes=target_sizes)[0]
 
- 
     figure = get_figure(in_pil_img, results)
 
     buf = io.BytesIO()