chapter-09-kubernetes/09-image-preparation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import grpc\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow_serving.apis import predict_pb2\n",
    "from tensorflow_serving.apis import prediction_service_pb2_grpc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "host = 'localhost:8500'\n",
    "channel = grpc.insecure_channel(host)\n",
    "stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras_image_helper import create_preprocessor\n",
    "\n",
    "preprocessor = create_preprocessor('xception', target_size=(299, 299))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"http://bit.ly/mlbookcamp-pants\"\n",
    "X = preprocessor.from_url(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 299, 299, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def np_to_protobuf(data):\n",
    "    return tf.make_tensor_proto(data, shape=data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pb_request = predict_pb2.PredictRequest()\n",
    "\n",
    "pb_request.model_spec.name = 'clothing-model'\n",
    "pb_request.model_spec.signature_name = 'serving_default'\n",
    "pb_request.inputs['input_8'].CopyFrom(np_to_protobuf(X)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pb_result = stub.Predict(pb_request, timeout=20.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = pb_result.outputs['dense_7'].float_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = [\n",
    "    'dress',\n",
    "    'hat',\n",
    "    'longsleeve',\n",
    "    'outwear',\n",
    "    'pants',\n",
    "    'shirt',\n",
    "    'shoes',\n",
    "    'shorts',\n",
    "    'skirt',\n",
    "    't-shirt'\n",
    "] \n",
    "\n",
    "result = {c: p for c, p in zip(labels, pred)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dress': -1.8682907819747925,\n",
       " 'hat': -4.761244773864746,\n",
       " 'longsleeve': -2.316983461380005,\n",
       " 'outwear': -1.062570333480835,\n",
       " 'pants': 9.887161254882812,\n",
       " 'shirt': -2.812433958053589,\n",
       " 'shoes': -3.666283130645752,\n",
       " 'shorts': 3.200361728668213,\n",
       " 'skirt': -2.6023383140563965,\n",
       " 't-shirt': -4.835046768188477}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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