3_rest_requests_multi_model.ipynb

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   "source": [
    "# REST Inference"
   ]
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   "source": [
    "## Setup\n",
    "\n",
    "Verify that following variable settings match your deployed model's resource name and rest URL. The following code assumes that the kube service is in the same namespace, but you could refer to it in full with the namespace, for example: `http://modelmesh-serving.project-name.svc.cluster.local:8008/v2/models/fraud/infer`"
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   "source": [
    "deployed_model_name = \"fraud-detection\"\n",
    "rest_url = \"http://modelmesh-serving:8008\"\n",
    "infer_url = f\"{rest_url}/v2/models/{deployed_model_name}/infer\""
   ]
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    "## 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)\n",
    "    response_dict = response.json()\n",
    "    return response_dict['outputs'][0]['data']"
   ]
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  {
   "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"
   ]
  },
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   "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": "5697c2ff",
   "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": "0393a5a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [0.0, 1.0, 1.0, 1.0, 0.0]\n",
    "prediction = rest_request(data)\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": "e889cdd6",
   "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)"
   ]
  },
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   "execution_count": null,
   "id": "5deba1d5",
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   "source": [
    "data = [100, 1.2, 0.0, 0.0, 1.0]\n",
    "prediction = rest_request(data)\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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