nlp/test_endpoint.ipynb

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    "# Test endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import requests\n",
    "\n",
    "from captum.attr import visualization as viz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = <ENDPOINT URL>\n",
    "\n",
    "token = <ENDPOINT TOKEN>\n",
    "\n",
    "sentences = <LIST OF SENTENCES>"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "headers = {\n",
    "    \"Content-Type\": \"application/json\",\n",
    "    \"X-Bedrock-Api-Token\": token,\n",
    "}\n",
    "data = json.dumps({\"sentences\": sentences, \"bool_xai\": 1})\n",
    "\n",
    "response = requests.post(url, headers=headers, data=data)\n",
    "print(response.ok)\n",
    "print(response.json())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prob = response.json()[\"y_prob\"]\n",
    "attributes = response.json().get(\"attributes\")"
   ]
  },
  {
   "cell_type": "code",
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    "i = 0  # select sentence\n",
    "\n",
    "true_label = None\n",
    "result_vis = viz.VisualizationDataRecord(\n",
    "    attributes[i][\"attributions\"],\n",
    "    y_prob[i],\n",
    "    (y_prob[i] > 0.5),\n",
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