SWE-590-Client.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kip7dNB0lPiv"
      },
      "outputs": [],
      "source": [
        "!pip install google-cloud-aiplatform"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VHXQb7jClOrJ"
      },
      "outputs": [],
      "source": [
        "!pip uninstall google-cloud-bigquery\n",
        "!pip install google-cloud-bigquery==1.15.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ITr3GywflSHk"
      },
      "outputs": [],
      "source": [
        "from google.cloud import aiplatform"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P12Aj02cvkwg"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "from PIL import Image\n",
        "import cv2\n",
        "import numpy as np\n",
        "from io import BytesIO\n",
        "import json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZNXcrc1kwC2t"
      },
      "outputs": [],
      "source": [
        "CONVERTER_API_ENDPOINT = AWS_LAMBDA_ENPOINT\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GV9jFQIwv77Y"
      },
      "outputs": [],
      "source": [
        "# the convert function takes an img file and API endpoint as inputs, when you call the function the image is going to AWS\n",
        "# LAMBDA function for image processing, the processed image will converted to np array at the end of the function and returned to the client."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NQM0L0AMvbBL"
      },
      "outputs": [],
      "source": [
        "def convert(img, converter_api):\n",
        "    API_ENDPOINT = converter_api\n",
        "\n",
        "    is_success, im_buf_arr = cv2.imencode(\".png\", img)\n",
        "    byte_im = im_buf_arr.tobytes()\n",
        "\n",
        "    r = requests.post(url=API_ENDPOINT, data=byte_im)\n",
        "\n",
        "    img_ = Image.open(BytesIO(r.content))\n",
        "\n",
        "    return np.asarray(img_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HKLmh8yKxNta"
      },
      "source": [
        "## 1. Download the fundus image from the website above:\n",
        "## https://upload.wikimedia.org/wikipedia/commons/thumb/3/37/Fundus_photograph_of_normal_right_eye.jpg/800px-Fundus_photograph_of_normal_right_eye.jpg\n",
        "\n",
        "## 3. save it with the name with fundus.jpeg and then copy your own current notebook session."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "inDPf6WKxuyY"
      },
      "outputs": [],
      "source": [
        "read_path = \"/content/fundus.jpeg\"\n",
        "write_path = \"/content/fundus_converted.png\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b8KBaAqevwJ3",
        "outputId": "ecaf4d58-53fa-45d3-b317-8b6a8aea9839"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "img_path = read_path \n",
        "\n",
        "img = cv2.imread(img_path)\n",
        "\n",
        "img_processed = convert(img, CONVERTER_API_ENDPOINT)\n",
        "\n",
        "cv2.imwrite(write_path, img_processed)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dTadIT1xxZuD",
        "outputId": "37a783e4-c837-4fee-f471-f62aa4dc838f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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          ]
        }
      ],
      "source": [
        "for _ in range(200):\n",
        "  img_path = read_path \n",
        "\n",
        "  img = cv2.imread(img_path)\n",
        "\n",
        "  img_processed = convert(img, CONVERTER_API_ENDPOINT)\n",
        "\n",
        "  cv2.imwrite(write_path, img_processed)\n",
        "  print(_)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oBNhzn7zCjVq",
        "outputId": "dc366039-4a6b-441f-85d8-0e23286399f5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[[10,  7, 11],\n",
              "        [10,  7, 11],\n",
              "        [10,  6, 11],\n",
              "        ...,\n",
              "        [10,  6, 11],\n",
              "        [10,  6, 11],\n",
              "        [10,  6, 11]],\n",
              "\n",
              "       [[10,  6, 11],\n",
              "        [10,  6, 11],\n",
              "        [10,  6, 11],\n",
              "        ...,\n",
              "        [10,  6, 11],\n",
              "        [10,  6, 11],\n",
              "        [10,  6, 11]],\n",
              "\n",
              "       [[10,  6, 11],\n",
              "        [10,  6, 11],\n",
              "        [10,  6, 11],\n",
              "        ...,\n",
              "        [10,  6, 11],\n",
              "        [10,  6, 11],\n",
              "        [10,  6, 11]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[10,  6, 11],\n",
              "        [10,  6, 11],\n",
              "        [10,  6, 11],\n",
              "        ...,\n",
              "        [10,  6, 11],\n",
              "        [10,  6, 11],\n",
              "        [10,  6, 11]],\n",
              "\n",
              "       [[10,  6, 11],\n",
              "        [10,  6, 11],\n",
              "        [10,  6, 11],\n",
              "        ...,\n",
              "        [10,  6, 11],\n",
              "        [10,  6, 11],\n",
              "        [10,  6, 11]],\n",
              "\n",
              "       [[10,  6, 11],\n",
              "        [10,  6, 11],\n",
              "        [10,  6, 11],\n",
              "        ...,\n",
              "        [10,  6, 11],\n",
              "        [10,  6, 11],\n",
              "        [10,  6, 11]]], dtype=uint8)"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "img_processed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5cEKcgGN0Kzb"
      },
      "outputs": [],
      "source": [
        "list_ = img_processed.tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k3DlL5MKQKYD"
      },
      "outputs": [],
      "source": [
        "from google.cloud import aiplatform\n",
        "import os\n",
        "os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"]=\"/content/serve-prediction-2a5263bf6ffd.json\"\n",
        "\n",
        "aip_endpoint_name = (\n",
        "    f\"projects/661387613610/locations/us-central1/endpoints/8385865233884774400\"\n",
        ")\n",
        "\n",
        "endpoint = aiplatform.Endpoint(aip_endpoint_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GN0enTMfMzH0"
      },
      "outputs": [],
      "source": [
        "results = endpoint.predict(instances=list_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxBHgTxkxumi",
        "outputId": "716f0d4f-f9f6-481d-9c26-bde875e22562"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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            "61 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
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            "63 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "64 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "65 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
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            "72 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "73 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
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            "75 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "76 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
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            "463 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "464 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "465 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "466 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "467 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "468 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "469 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "470 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "471 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "472 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "473 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "474 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "475 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "476 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "477 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "478 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "479 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "480 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "481 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "482 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "483 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "484 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "485 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "486 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "487 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "488 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "489 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "490 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "491 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "492 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "493 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "494 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "495 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "496 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "497 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "498 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n",
            "499 [0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]\n"
          ]
        }
      ],
      "source": [
        "for _ in range(500):\n",
        "  results = endpoint.predict(instances=list_)\n",
        "  print(_, results[0][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h_kQD-mSNAuS",
        "outputId": "e0657115-b896-47c5-cae3-89ca6ac5a4b5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Prediction(predictions=[[0.560936809, 0.491741657, 0.456840098, 0.475522518, 0.53235352]], deployed_model_id='1078379014290145280', model_version_id='1', model_resource_name='projects/661387613610/locations/us-central1/models/8309460170870620160', explanations=None)"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QMMQGX5GNPzV",
        "outputId": "e5b03671-a76f-40c3-f857-278a45f33573"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.argmax(results[0][0])"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}