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     "text": [
      "/tmp/ipykernel_4031598/48305459.py:16: GradioDeprecationWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
      "  outputs= gr.outputs.Image(type=\"pil\", label=\"hoi detection results\"),\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7860\n",
      "Running on public URL: https://fd9d0145926e3bdb6d.gradio.live\n",
      "\n",
      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co./spaces)\n"
     ]
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     "text": [
      "loading annotations into memory...\n",
      "Done (t=1.56s)\n",
      "creating index...\n",
      "index created!\n",
      "\n",
      "[Logger] DETR Arguments:\n",
      "\tlr: 0.0001\n",
      "\tlr_backbone: 1e-05\n",
      "\tlr_drop: 80\n",
      "\tfrozen_weights: None\n",
      "\tbackbone: resnet50\n",
      "\tdilation: False\n",
      "\tposition_embedding: sine\n",
      "\tenc_layers: 6\n",
      "\tdec_layers: 6\n",
      "\tnum_queries: 100\n",
      "\tdataset_file: vcoco\n",
      "\n",
      "[Logger] Number of params:  52413912\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jihwan/CPC_HOTR/hotr/models/position_encoding.py:41: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
      "  dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)\n"
     ]
    }
   ],
   "source": [
    "import gradio as gr\n",
    "from transformers import pipeline\n",
    "from visualization import visualization\n",
    "# pipeline = pipeline(task=\"image-classification\", model=\"julien-c/hotdog-not-hotdog\")\n",
    "# pipeline = pipeline(task=\"image-classification\", model=\"jhp/hoi\")\n",
    "\n",
    "def predict(image,threshold,topk):\n",
    "    vis_img = visualization(image,threshold,topk)\n",
    "    return vis_img\n",
    "\n",
    "gr.Interface(\n",
    "    predict,\n",
    "    inputs=[gr.Image(type='pil',label=\"input image\"),\n",
    "           gr.Slider(0, 1, value=0.4, label=\"Threshold\", info=\"Set detection score threshold between 0~1\"),\n",
    "           gr.Number(value=5,info='Topk prediction')],\n",
    "    outputs= gr.outputs.Image(type=\"pil\", label=\"hoi detection results\"),\n",
    "    title=\"HOI detection\",\n",
    ").launch(share=True,debug=True)"
   ]
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     "ename": "TypeError",
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      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m a\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msdsd\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 2\u001b[0m \u001b[43ma\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\n",
      "\u001b[0;31mTypeError\u001b[0m: string indices must be integers"
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    "a='sdsd'\n",
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