GoogleAutoMLPredictionsviaAPI.ipy

GoogleAutoMLPredictionsviaAPI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries, including Google Cloud Auto ML libraries\n",
    "import sys\n",
    "\n",
    "from google.cloud import automl_v1beta1\n",
    "from google.cloud.automl_v1beta1.proto import service_pb2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that we're explicitly defining our service account credentials in order to make API calls to the prediction \n",
    "# service. This is a path to a JSON file containing keys to a service account that I set up, with access to Auto ML\n",
    "# You can create that via the GCP console\n",
    "service_account_path ='/Users/michaelsadowski/Desktop/cancer-image-recognition-1298fcf33184.json'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define our prediction client\n",
    "def get_prediction(content, project_id, model_id):\n",
    "\n",
    "  prediction_client = automl_v1beta1.PredictionServiceClient.from_service_account_file(service_account_path)\n",
    "  name = 'projects/{}/locations/us-central1/models/{}'.format(project_id, model_id)\n",
    "  payload = {'image': {'image_bytes': content }}\n",
    "  params = {}\n",
    "  request = prediction_client.predict(name, payload, params)\n",
    "  return request  # waits till request is returned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define some variables: file path to our test image, project ID in Google Cloud Platform\n",
    "# and your model ID in AutoML, which is available after training the model\n",
    "file_path = '/Users/michaelsadowski/Desktop/cell_images/Parasitized/C100P61ThinF_IMG_20150918_144104_cell_162.png'\n",
    "project_id = 'cancer-image-recognition'\n",
    "model_id = 'ICN1395947982826315941'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the image file and put it in content\n",
    "with open(file_path, 'rb') as ff:\n",
    "    content = ff.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "payload {\n",
       "  classification {\n",
       "    score: 0.9994016885757446\n",
       "  }\n",
       "  display_name: \"parasitized\"\n",
       "}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Call the Auto ML prediction service (rest API) and see what it predicts for this image\n",
    "get_prediction(content, project_id,  model_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "payload {\n",
       "  classification {\n",
       "    score: 0.9986653327941895\n",
       "  }\n",
       "  display_name: \"uninfected\"\n",
       "}"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's try another one, which is uninfected\n",
    "file_path = '/Users/michaelsadowski/Desktop/cell_images/Uninfected/C1_thinF_IMG_20150604_104722_cell_73.png'\n",
    "# read the image file and put it in content\n",
    "with open(file_path, 'rb') as ff:\n",
    "    content = ff.read()\n",
    "get_prediction(content, project_id,  model_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OK good, it thinks that one's not infected"
   ]
  }
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