ibm-cloud-model-download.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0c77cf49",
   "metadata": {},
   "source": [
    "API keys are removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "continental-toilet",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install watson-machine-learning-client "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "elegant-maine",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ibm_watson_machine_learning import APIClient\n",
    "wml_credentials = {\n",
    "    \"url\" : \"https://us-south.ml.cloud.ibm.com\",\n",
    "    \"apikey\" : \"\"\n",
    "}\n",
    "client = APIClient(wml_credentials)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "multiple-marine",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "controversial-absence",
   "metadata": {},
   "outputs": [],
   "source": [
    "def guid_from_space_name(client, space_name):\n",
    "    space = client.spaces.get_details()\n",
    "    return(next(item for item in space['resources'] if item['entity']['name'] == space_name)['metadata']['id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "thick-addiction",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "space uid: d006ca40-889e-48e9-85e0-192ea77fdafb\n"
     ]
    }
   ],
   "source": [
    "space_uid = guid_from_space_name(client, 'Implement Deep Learning Techniques To Detect Malaria Using Ibm Cloud')\n",
    "print('space uid: ' + space_uid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "raising-factory",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'SUCCESS'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.set.default_space(space_uid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "surprised-petroleum",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------  ------------------------------------  ----\n",
      "NAME                           ASSET_ID                              TYPE\n",
      "default_py3.6                  0062b8c9-8b7d-44a0-a9b9-46c416adcbd9  base\n",
      "pytorch-onnx_1.3-py3.7-edt     069ea134-3346-5748-b513-49120e15d288  base\n",
      "scikit-learn_0.20-py3.6        09c5a1d0-9c1e-4473-a344-eb7b665ff687  base\n",
      "spark-mllib_3.0-scala_2.12     09f4cff0-90a7-5899-b9ed-1ef348aebdee  base\n",
      "ai-function_0.1-py3.6          0cdb0f1e-5376-4f4d-92dd-da3b69aa9bda  base\n",
      "shiny-r3.6                     0e6e79df-875e-4f24-8ae9-62dcc2148306  base\n",
      "tensorflow_2.4-py3.7-horovod   1092590a-307d-563d-9b62-4eb7d64b3f22  base\n",
      "pytorch_1.1-py3.6              10ac12d6-6b30-4ccd-8392-3e922c096a92  base\n",
      "tensorflow_1.15-py3.6-ddl      111e41b3-de2d-5422-a4d6-bf776828c4b7  base\n",
      "scikit-learn_0.22-py3.6        154010fa-5b3b-4ac1-82af-4d5ee5abbc85  base\n",
      "default_r3.6                   1b70aec3-ab34-4b87-8aa0-a4a3c8296a36  base\n",
      "pytorch-onnx_1.3-py3.6         1bc6029a-cc97-56da-b8e0-39c3880dbbe7  base\n",
      "tensorflow_2.1-py3.6           1eb25b84-d6ed-5dde-b6a5-3fbdf1665666  base\n",
      "tensorflow_2.4-py3.8-horovod   217c16f6-178f-56bf-824a-b19f20564c49  base\n",
      "do_py3.8                       295addb5-9ef9-547e-9bf4-92ae3563e720  base\n",
      "autoai-ts_3.8-py3.8            2aa0c932-798f-5ae9-abd6-15e0c2402fb5  base\n",
      "tensorflow_1.15-py3.6          2b73a275-7cbf-420b-a912-eae7f436e0bc  base\n",
      "pytorch_1.2-py3.6              2c8ef57d-2687-4b7d-acce-01f94976dac1  base\n",
      "spark-mllib_2.3                2e51f700-bca0-4b0d-88dc-5c6791338875  base\n",
      "pytorch-onnx_1.1-py3.6-edt     32983cea-3f32-4400-8965-dde874a8d67e  base\n",
      "spark-mllib_3.0-py37           36507ebe-8770-55ba-ab2a-eafe787600e9  base\n",
      "spark-mllib_2.4                390d21f8-e58b-4fac-9c55-d7ceda621326  base\n",
      "xgboost_0.82-py3.6             39e31acd-5f30-41dc-ae44-60233c80306e  base\n",
      "pytorch-onnx_1.2-py3.6-edt     40589d0e-7019-4e28-8daa-fb03b6f4fe12  base\n",
      "default_r36py38                41c247d3-45f8-5a71-b065-8580229facf0  base\n",
      "autoai-obm_3.0                 42b92e18-d9ab-567f-988a-4240ba1ed5f7  base\n",
      "spark-mllib_2.4-r_3.6          49403dff-92e9-4c87-a3d7-a42d0021c095  base\n",
      "xgboost_0.90-py3.6             4ff8d6c2-1343-4c18-85e1-689c965304d3  base\n",
      "pytorch-onnx_1.1-py3.6         50f95b2a-bc16-43bb-bc94-b0bed208c60b  base\n",
      "autoai-ts_3.9-py3.8            52c57136-80fa-572e-8728-a5e7cbb42cde  base\n",
      "spark-mllib_2.4-scala_2.11     55a70f99-7320-4be5-9fb9-9edb5a443af5  base\n",
      "spark-mllib_3.0                5c1b0ca2-4977-5c2e-9439-ffd44ea8ffe9  base\n",
      "autoai-obm_2.0                 5c2e37fa-80b8-5e77-840f-d912469614ee  base\n",
      "spss-modeler_18.1              5c3cad7e-507f-4b2a-a9a3-ab53a21dee8b  base\n",
      "cuda-py3.8                     5d3232bf-c86b-5df4-a2cd-7bb870a1cd4e  base\n",
      "autoai-kb_3.1-py3.7            632d4b22-10aa-5180-88f0-f52dfb6444d7  base\n",
      "pytorch-onnx_1.7-py3.8         634d3cdc-b562-5bf9-a2d4-ea90a478456b  base\n",
      "spark-mllib_2.3-r_3.6          6586b9e3-ccd6-4f92-900f-0f8cb2bd6f0c  base\n",
      "tensorflow_2.4-py3.7           65e171d7-72d1-55d9-8ebb-f813d620c9bb  base\n",
      "spss-modeler_18.2              687eddc9-028a-4117-b9dd-e57b36f1efa5  base\n",
      "pytorch-onnx_1.2-py3.6         692a6a4d-2c4d-45ff-a1ed-b167ee55469a  base\n",
      "do_12.9                        75a3a4b0-6aa0-41b3-a618-48b1f56332a6  base\n",
      "spark-mllib_2.3-scala_2.11     7963efe5-bbec-417e-92cf-0574e21b4e8d  base\n",
      "spark-mllib_2.4-py37           7abc992b-b685-532b-a122-a396a3cdbaab  base\n",
      "caffe_1.0-py3.6                7bb3dbe2-da6e-4145-918d-b6d84aa93b6b  base\n",
      "pytorch-onnx_1.7-py3.7         812c6631-42b7-5613-982b-02098e6c909c  base\n",
      "cuda-py3.6                     82c79ece-4d12-40e6-8787-a7b9e0f62770  base\n",
      "tensorflow_1.15-py3.6-horovod  8964680e-d5e4-5bb8-919b-8342c6c0dfd8  base\n",
      "hybrid_0.1                     8c1a58c6-62b5-4dc4-987a-df751c2756b6  base\n",
      "pytorch-onnx_1.3-py3.7         8d5d8a87-a912-54cf-81ec-3914adaa988d  base\n",
      "-----------------------------  ------------------------------------  ----\n",
      "Note: Only first 50 records were displayed. To display more use 'limit' parameter.\n"
     ]
    }
   ],
   "source": [
    "client.software_specifications.list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "athletic-jefferson",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'65e171d7-72d1-55d9-8ebb-f813d620c9bb'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "software_spec_uid = client.software_specifications.get_uid_by_name(\"tensorflow_2.4-py3.7\")\n",
    "software_spec_uid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dedicated-specialist",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_id = client.repository.get_model_uid(model_details)\n",
    "model_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "radical-liberal",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully saved model content to file: 'ibm-model.tar.gz'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/home/harshit/Desktop/ibm-model.tar.gz'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.repository.download('ce1cea32-0df9-47e4-be99-3116b047a91c', 'ibm-model.tar.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "swedish-brain",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "792e59ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from tensorflow.keras.preprocessing import image\n",
    "# # img = image.load_img(r'/home/harshit/Downloads/cell_images/cell_images/Uninfected/C208ThinF_IMG_20151029_155436_cell_127.png', target_size=(124,img_height))\n",
    "# #img = image.load_img('/home/harshit/Downloads/cell_images/cell_images/Uninfected/C208ThinF_IMG_20151029_155436_cell_127.png', target_size=(64,64))\n",
    "\n",
    "\n",
    "# import cv2\n",
    "# img = cv2.imread('/home/harshit/Downloads/cell_images/cell_images/Uninfected/C208ThinF_IMG_20151029_155436_cell_127.png')\n",
    "# # img = cv2.imread('/home/harshit/Downloads/cell_images/cell_images/Uninfected/C99P60ThinF_IMG_20150918_142128_cell_45.png')\n",
    "# img = cv2.resize(img,(64,64))\n",
    "# img = img.reshape(64,64,3) \n",
    "\n",
    "\n",
    "# x = image.img_to_array(img)\n",
    "# x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c22689fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# a = model.predict(x)\n",
    "# print(a)\n",
    "# if(a == 1):\n",
    "#     print(\"uninfected\")\n",
    "# else:\n",
    "#     print(\"infected\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "059de30f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d14b7e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "591dcd5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# img = cv2.imread('/home/harshit/Downloads/cell_images/cell_images/Parasitized/C182P143NThinF_IMG_20151201_172607_cell_43.png')\n",
    "# #img = img.reshape(64, 64, 3)\n",
    "# # img = cv2.resize(img,(64,64,3))\n",
    "# img = cv2.resize(img,(64,64))\n",
    "# img = img.reshape(64,64,3)\n",
    "\n",
    "# x = image.img_to_array(img)\n",
    "\n",
    "# a = model.predict(x)\n",
    "# print(a)\n",
    "# if(a == 0):\n",
    "#     print(\"uninfected\")\n",
    "# else:\n",
    "#     print(\"infected\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cbb2998e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.preprocessing import image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "589073c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model.h5\r\n"
     ]
    }
   ],
   "source": [
    "!tar -xvzf ibm-model.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c9d24a96",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-01-18 20:44:01.406150: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
      "2022-01-18 20:44:01.406186: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (arch): /proc/driver/nvidia/version does not exist\n",
      "2022-01-18 20:44:01.406592: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "model = load_model('model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "95ceeeeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "\n",
    "def predict(img):\n",
    "    x = image.img_to_array(img)\n",
    "    x = x/255.0\n",
    "    x = np.expand_dims(x,axis=0)\n",
    "    proba = model.predict(x)[0][0]\n",
    "    print(proba)\n",
    "    y = \"Uninfected\" if proba > 0.9 else \"Parasitized\"\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "civic-playback",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "streaming_body_9 = client_b72a3086530044cfb979750da0de0c96.get_object(Bucket='implementdeeplearningtechniquesto-donotdelete-pr-mwfkeai0saywwq', Key='C99P60ThinF_IMG_20150918_142128_cell_83.png')['Body']\n",
    "\n",
    "# Your data file was loaded into a botocore.response.StreamingBody object.\n",
    "# Please read the documentation of ibm_boto3 and pandas to learn more about the possibilities to load the data.\n",
    "# ibm_boto3 documentation: https://ibm.github.io/ibm-cos-sdk-python/\n",
    "# pandas documentation: http://pandas.pydata.org/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8dea420a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.97845244\n",
      "Uninfected\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing import image\n",
    "\n",
    "path = '/home/harshit/Downloads/cell_images/cell_images/Uninfected/C99P60ThinF_IMG_20150918_142128_cell_14.png'\n",
    "\n",
    "img = image.load_img(path,target_size=(64, 64))\n",
    "\n",
    "pred = predict(img)\n",
    "print(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0a4d5418",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mv model.h5 ibm-model.h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d23d9061",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r----- 1 harshit harshit 4923640 Jan 18 19:29 ibm-model.h5\r\n"
     ]
    }
   ],
   "source": [
    "!ls -la ibm-model.h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ad5c67e",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = image.load_img(path, target_size=(64,64))\n",
    "x = image.img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "pred = model.predict(x)\n",
    "print(\"prediction\", pred)\n",
    "if(pred == 1):\n",
    "    result = \"Uninfected\"\n",
    "else:\n",
    "    result = \"Infected\"\n",
    "\n",
    "print(\"prediction:\", result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f11428d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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