5_rest_requests_single_model.ipynb

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   "source": [
    "# REST Inference"
   ]
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   "source": [
    "## Setup\n",
    "\n",
    "Change that following variable settings match your deployed model's *Inference endpoint*. for example: \n",
    "\n",
    "```\n",
    "deployed_model_name = \"fraud\"\n",
    "infer_endpoint = \"https://fraud-predictor-userx-workshop.apps.clusterx.sandboxx.opentlc.com\"\n",
    "```"
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    "deployed_model_name = \"fraud\"\n",
    "infer_endpoint = \"https://fraud-predictor-<project-name>.<cluster>.com\"\n",
    "infer_url = f\"{infer_endpoint}/v2/models/{deployed_model_name}/infer\""
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## Request Function\n",
    "\n",
    "Build and submit the REST request. \n",
    "\n",
    "Note: You submit the data in the same format that you used for an ONNX inference."
   ]
  },
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   "source": [
    "import requests\n",
    "\n",
    "\n",
    "def rest_request(data):\n",
    "    json_data = {\n",
    "        \"inputs\": [\n",
    "            {\n",
    "                \"name\": \"dense_12_input\",\n",
    "                \"shape\": [1, 5],\n",
    "                \"datatype\": \"FP32\",\n",
    "                \"data\": data\n",
    "            }\n",
    "        ]\n",
    "    }\n",
    "\n",
    "    response = requests.post(infer_url, json=json_data, verify=False)\n",
    "    response_dict = response.json()\n",
    "    return response_dict['outputs'][0]['data']"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "data = [0.3111400080477545, 1.9459399775518593, 1.0, 0.0, 0.0]\n",
    "prediction = rest_request(data)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "threshhold = 0.995\n",
    "\n",
    "if (prediction[0] > threshhold):\n",
    "    print('fraud')\n",
    "else:\n",
    "    print('not fraud')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f7b17c0",
   "metadata": {},
   "source": [
    "## Example 1: user buys a coffee\n",
    "\n",
    "In this example, the user is buying a coffee. The parameters given to the model are:\n",
    "* same location as the last transaction (distance=0)\n",
    "* same median price as the last transaction (ratio_to_median=1)\n",
    "* using a pin number (pin=1)\n",
    "* using the credit card chip (chip=1)\n",
    "* not an online transaction (online=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0a68b67-b109-4a2f-b097-092f4a4d25ce",
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   "outputs": [],
   "source": [
    "data = [0.0, 1.0, 1.0, 1.0, 0.0]\n",
    "prediction = rest_request(data)\n",
    "prediction\n",
    "threshhold = 0.995\n",
    "\n",
    "if (prediction[0] > threshhold):\n",
    "    print('The model predicts that this is fraud')\n",
    "else:\n",
    "    print('The model predicts that this is not fraud')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db10b280",
   "metadata": {},
   "source": [
    "## Example 2: fraudulent transaction\n",
    "\n",
    "In this example, someone stole the user's credit card and is buying something online. The parameters given to the model are:\n",
    "* very far away from the last transaction (distance=100)\n",
    "* median price similar to the last transaction (ratio_to_median=1.2)\n",
    "* not using a pin number (pin=0)\n",
    "* not using the credit card chip (chip=0)\n",
    "* is an online transaction (online=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "219b8927",
   "metadata": {
    "tags": []
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   "source": [
    "data = [100, 1.2, 0.0, 0.0, 1.0]\n",
    "prediction = rest_request(data)\n",
    "prediction\n",
    "threshhold = 0.995\n",
    "\n",
    "if (prediction[0] > threshhold):\n",
    "    print('The model predicts that this is fraud')\n",
    "else:\n",
    "    print('The model predicts that this is not fraud')"
   ]
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