CNNPredictiveModel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SuAVE Image to Label CNN Prediction Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "function getQueryStringValue (key)\n",
       "{  \n",
       "    return unescape(window.location.search.replace(new RegExp(\"^(?:.*[&\\\\?]\" + escape(key).replace(/[\\.\\+\\*]/g, \"\\\\$&\") + \"(?:\\\\=([^&]*))?)?.*$\", \"i\"), \"$1\"));\n",
       "}\n",
       "IPython.notebook.kernel.execute(\"survey_url='\".concat(getQueryStringValue(\"surveyurl\")).concat(\"'\"));\n",
       "IPython.notebook.kernel.execute(\"user='\".concat(getQueryStringValue(\"user\")).concat(\"'\"));\n",
       "IPython.notebook.kernel.execute(\"csv_file='\".concat(getQueryStringValue(\"csv\")).concat(\"'\")); \n",
       "IPython.notebook.kernel.execute(\"dzc_file='\".concat(getQueryStringValue(\"dzc\")).concat(\"'\")); \n",
       "IPython.notebook.kernel.execute(\"params='\".concat(getQueryStringValue(\"params\")).concat(\"'\")); \n",
       "IPython.notebook.kernel.execute(\"active_object='\".concat(getQueryStringValue(\"activeobject\")).concat(\"'\")); "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "function getQueryStringValue (key)\n",
    "{  \n",
    "    return unescape(window.location.search.replace(new RegExp(\"^(?:.*[&\\\\?]\" + escape(key).replace(/[\\.\\+\\*]/g, \"\\\\$&\") + \"(?:\\\\=([^&]*))?)?.*$\", \"i\"), \"$1\"));\n",
    "}\n",
    "IPython.notebook.kernel.execute(\"survey_url='\".concat(getQueryStringValue(\"surveyurl\")).concat(\"'\"));\n",
    "IPython.notebook.kernel.execute(\"user='\".concat(getQueryStringValue(\"user\")).concat(\"'\"));\n",
    "IPython.notebook.kernel.execute(\"csv_file='\".concat(getQueryStringValue(\"csv\")).concat(\"'\")); \n",
    "IPython.notebook.kernel.execute(\"dzc_file='\".concat(getQueryStringValue(\"dzc\")).concat(\"'\")); \n",
    "IPython.notebook.kernel.execute(\"params='\".concat(getQueryStringValue(\"params\")).concat(\"'\")); \n",
    "IPython.notebook.kernel.execute(\"active_object='\".concat(getQueryStringValue(\"activeobject\")).concat(\"'\")); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# import the necessary packages\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.optimizers import Adam\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.preprocessing.image import img_to_array\n",
    "from keras.utils import to_categorical\n",
    "from imutils import paths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import all packages (this might take a few seconds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the necessary packages\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.optimizers import Adam\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.preprocessing.image import img_to_array\n",
    "from keras.utils import to_categorical\n",
    "from imutils import paths\n",
    "\n",
    "from keras.utils import np_utils\n",
    "from keras.models import Sequential\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.convolutional import MaxPooling2D\n",
    "from keras.layers.core import Activation\n",
    "from keras.layers.core import Flatten\n",
    "from keras.layers.core import Dense\n",
    "from keras.layers.core import Dropout\n",
    "from keras import backend as K\n",
    "\n",
    "# More imports\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import argparse\n",
    "import random\n",
    "import csv\n",
    "import pandas as pd\n",
    "import re\n",
    "import cv2\n",
    "import os\n",
    "\n",
    "# import the necessary packages for SVM predictor\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "import imutils\n",
    "import warnings\n",
    "warnings.filterwarnings('always')  # \"error\", \"ignore\", \"always\", \"default\", \"module\" or \"once\"\n",
    "\n",
    "# Import widget functionality\n",
    "from __future__ import print_function\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This notebook uses the LeNet classifier model for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LeNet:\n",
    "\t@staticmethod\n",
    "\tdef build(width, height, depth, classes):\n",
    "\t\t# initialize the model\n",
    "\t\tmodel = Sequential()\n",
    "\t\tinputShape = (height, width, depth)\n",
    "\n",
    "\t\t# if we are using \"channels first\", update the input shape\n",
    "\t\tif K.image_data_format() == \"channels_first\":\n",
    "\t\t\tinputShape = (depth, height, width)\n",
    "\n",
    "\t\t# first set of CONV => RELU => POOL layers\n",
    "\t\tmodel.add(Conv2D(64, (3, 3), padding=\"same\",\n",
    "\t\t\tinput_shape=inputShape))\n",
    "\t\tmodel.add(Activation(\"relu\"))\n",
    "\t\tmodel.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
    "\n",
    "\t\t# second set of CONV => RELU => POOL layers\n",
    "\t\tmodel.add(Conv2D(128, (3, 3), padding=\"same\"))\n",
    "\t\tmodel.add(Activation(\"relu\"))\n",
    "\t\tmodel.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
    "        \n",
    "\t\t# third set of CONV => RELU => POOL layers\n",
    "\t\tmodel.add(Conv2D(256, (3, 3), padding=\"same\"))\n",
    "\t\tmodel.add(Activation(\"relu\"))\n",
    "\t\tmodel.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
    "              \n",
    "\t\t# first (and only) set of FC => RELU layers\n",
    "\t\tmodel.add(Flatten())\n",
    "\t\tmodel.add(Dense(1000))\n",
    "\t\tmodel.add(Activation(\"relu\"))\n",
    "\t\tmodel.add(Dropout(0.5))\n",
    "\n",
    "\t\t# softmax classifier\n",
    "\t\tmodel.add(Dense(classes))\n",
    "\t\tmodel.add(Activation(\"softmax\"))\n",
    "\n",
    "\t\t# return the constructed network architecture\n",
    "\t\treturn model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initializing the number of times the model will loop and its batch size for learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f51c7f31ee7a4078a2053a7a0114108b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='epoch_count', options={'75 Iterations': 75, '25 Iterations': 25, '…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochCount = {\n",
    "    '25 Iterations': 25,\n",
    "    '50 Iterations': 50,\n",
    "    '75 Iterations': 75\n",
    "}\n",
    "\n",
    "def f(epoch_count):\n",
    "    return epoch_count\n",
    "\n",
    "epochNum = interact(f, epoch_count=epochCount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "37a6a5bd64fa4d0e8c62601c93202e9a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='batch_size', options={'128 Batch Size': 128, '32 Batch Size': 32, …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batchS = {\n",
    "    '32 Batch Size': 32,\n",
    "    '64 Batch Size': 64,\n",
    "    '128 Batch Size': 128\n",
    "}\n",
    "\n",
    "def f(batch_size):\n",
    "    return batch_size\n",
    "\n",
    "batchNum = interact(f, batch_size=batchS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# init the number of epochs to train for, init learning rate and batch size\n",
    "EPOCHS = epochNum.widget.result\n",
    "INIT_LR = 1e-3\n",
    "BS = batchNum.widget.result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# init the image suffix, yset, and image list\n",
    "suffix = '.jpg'\n",
    "img_list = []\n",
    "yset = []\n",
    "# create labels list and 2 dicts for 2 way mapping\n",
    "labels = []\n",
    "# key = label value = number\n",
    "label_yval = dict()\n",
    "# key = number value = label\n",
    "yval_label = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# testing cell for Zeppelin Vanbarriger. Will be removed in production\n",
    "survey_url = 'http://localhost:3001/main/file=Zeppelin-V-Alt_balthus_test.csv&views=1110000&view=grid'\n",
    "params =''\n",
    "\n",
    "# Testing Cells\n",
    "csv_file = 'zaslavsk_Cyclops_Cave_Ceramic_Petrography.csv'\n",
    "\n",
    "\n",
    "dzc_file = '/home/zep/WebstormProjects/SuAVE/app/public/imageserver/balthus/balthus.dzc'\n",
    "\n",
    "dzc_url = 'http://localhost:3001/imageserver/balthus/balthus.dzc'\n",
    "\n",
    "active_object = ''\n",
    "actual_imageFolder = 'app/public/imagestore/balthus'\n",
    "absolutePath = '/home/zep/WebstormProjects/SuAVE/app/public/imageserver/balthus/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run and choose the column name that coresponds with the Image column in the CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "File b'zaslavsk_Cyclops_Cave_Ceramic_Petrography.csv' does not exist",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-a82e2d010c89>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# use csv file to grab images/labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcsv_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtoImg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    707\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[1;32m    708\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 709\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    710\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    711\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    447\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    448\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    450\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    816\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    817\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 818\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    819\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    820\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1047\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1048\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1049\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1050\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1051\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'python'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1693\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'allow_leading_cols'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1694\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1695\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1696\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1697\u001b[0m         \u001b[0;31m# XXX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: File b'zaslavsk_Cyclops_Cave_Ceramic_Petrography.csv' does not exist"
     ]
    }
   ],
   "source": [
    "# use csv file to grab images/labels\n",
    "df = pd.read_csv(csv_file)\n",
    "\n",
    "toImg = list(df.columns.values)\n",
    "\n",
    "img_menu = {}\n",
    "for i in range(0, len(toImg)):\n",
    "    img_menu[toImg[i]] = toImg[i]\n",
    "\n",
    "def f(images_menu):\n",
    "    return images_menu\n",
    "# choose which label is for images (commonly '#img')\n",
    "out = interact(f, images_menu=img_menu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run and choose the column name that coresponds with the column label to be predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "59700a75cb1a4e92aa6ceeea22248940",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choose column of label for prediction\n",
    "toPredict = list(df.columns.values)\n",
    "\n",
    "pred_menu = {}\n",
    "for i in range(0, len(toPredict)):\n",
    "    pred_menu[toPredict[i]] = toPredict[i]\n",
    "\n",
    "def f(predictions_menu):\n",
    "    return predictions_menu\n",
    "# choose which label for predictions\n",
    "out2 = interact(f, predictions_menu=pred_menu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of labels to learn from: 252\n"
     ]
    }
   ],
   "source": [
    "# grab chosen column names\n",
    "nameCol = df[out.widget.result]\n",
    "predCol = df[out2.widget.result]\n",
    "\n",
    "# add all fabric columns to the y set\n",
    "for i in range (0,len(predCol)):\n",
    "    labels.append(predCol[i])\n",
    "\n",
    "# grab all unique labels\n",
    "uni_labels = set(labels)\n",
    "uni_labels = list(uni_labels)\n",
    "\n",
    "# assign each label a dict key number\n",
    "for i in range(0,len(uni_labels)):\n",
    "    yval_label[i] = uni_labels[i]\n",
    "    label_yval[uni_labels[i]] = i\n",
    "    \n",
    "print(\"Number of labels to learn from: \" + str(len(labels)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grab the images and configure them for predicting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This might take a little while depending on the size of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create list of keys associated with their labels\n",
    "for i in range (0, len(labels)):\n",
    "    yset.append(label_yval[labels[i]])\n",
    "    \n",
    "# gather images from path created from file names in csv file\n",
    "for i in range (0,len(nameCol)):\n",
    "    base_filename = nameCol[i]\n",
    "    fileName = os.path.join(\"./Cyclops Cave/images/\", base_filename + suffix)\n",
    "    im = cv2.imread(fileName)\n",
    "    im = cv2.resize(im, (28,28))\n",
    "    im = img_to_array(im)\n",
    "    img_list.append(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shuffle the data\n",
    "p = np.random.permutation(len(yset))\n",
    "\n",
    "# Relable for splitting sets\n",
    "Y = []\n",
    "X = []\n",
    "for i in range(0,len(yset)):\n",
    "    Y.append(yset[p[i]])\n",
    "    X.append(img_list[p[i]])\n",
    "    \n",
    "# split the test and training set 75:25\n",
    "split = int(len(X)*(.75))\n",
    "xtrain = X[:split]\n",
    "xtest = X[split:]\n",
    "ytrain = Y[:split]\n",
    "ytest = Y[split:]\n",
    "\n",
    "# transform to arrays\n",
    "trainX = np.array(xtrain, dtype=\"float\")/225.0\n",
    "testX = np.array(xtest, dtype =\"float\")/225.0\n",
    "\n",
    "ytrain = np.array(ytrain)\n",
    "ytest = np.array(ytest)\n",
    "\n",
    "# parsed Y data containers\n",
    "trainY = []\n",
    "testY = []\n",
    "\n",
    "# convert labels from int to vectors\n",
    "trainY = np_utils.to_categorical(ytrain,12)\n",
    "testY = np_utils.to_categorical(ytest,12)\n",
    "\n",
    "# construct the image generator for data augmentation\n",
    "aug = ImageDataGenerator(rotation_range=30, width_shift_range=0.1,\n",
    "                        height_shift_range=0.1, shear_range=0.2, zoom_range=0.2,\n",
    "                        horizontal_flip=True, fill_mode=\"nearest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cinde\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\tensorflow\\python\\util\\tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n",
      "C:\\Users\\cinde\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\tensorflow\\python\\util\\tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n",
      "C:\\Users\\cinde\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\tensorflow\\python\\util\\tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    }
   ],
   "source": [
    "# initialize the model\n",
    "model = LeNet.build(width=28, height=28, depth=3, classes=12)\n",
    "opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=opt,\n",
    "                metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the predictive model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This is relative to the size of the data set and may take a few minutes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "6/5 [====================================] - 2s 369ms/step - loss: 1.0239 - acc: 0.6819 - val_loss: 1.3708 - val_acc: 0.6349\n",
      "Epoch 2/25\n",
      "6/5 [====================================] - 2s 338ms/step - loss: 1.0725 - acc: 0.6690 - val_loss: 1.2444 - val_acc: 0.6508\n",
      "Epoch 3/25\n",
      "6/5 [====================================] - 2s 338ms/step - loss: 1.0787 - acc: 0.6933 - val_loss: 1.3723 - val_acc: 0.6032\n",
      "Epoch 4/25\n",
      "6/5 [====================================] - 2s 340ms/step - loss: 1.0956 - acc: 0.6522 - val_loss: 1.3060 - val_acc: 0.6508\n",
      "Epoch 5/25\n",
      "6/5 [====================================] - 2s 338ms/step - loss: 1.0316 - acc: 0.6975 - val_loss: 1.3196 - val_acc: 0.6349\n",
      "Epoch 6/25\n",
      "6/5 [====================================] - 2s 335ms/step - loss: 1.0576 - acc: 0.6721 - val_loss: 1.2421 - val_acc: 0.6825\n",
      "Epoch 7/25\n",
      "6/5 [====================================] - 2s 340ms/step - loss: 0.9749 - acc: 0.6927 - val_loss: 1.2590 - val_acc: 0.6667\n",
      "Epoch 8/25\n",
      "6/5 [====================================] - 2s 336ms/step - loss: 0.9546 - acc: 0.7124 - val_loss: 1.2498 - val_acc: 0.6825\n",
      "Epoch 9/25\n",
      "6/5 [====================================] - 2s 339ms/step - loss: 0.9898 - acc: 0.6784 - val_loss: 1.3224 - val_acc: 0.6825\n",
      "Epoch 10/25\n",
      "6/5 [====================================] - 2s 339ms/step - loss: 0.9853 - acc: 0.6901 - val_loss: 1.1898 - val_acc: 0.7143\n",
      "Epoch 11/25\n",
      "6/5 [====================================] - 2s 338ms/step - loss: 0.9741 - acc: 0.7167 - val_loss: 1.2386 - val_acc: 0.6667\n",
      "Epoch 12/25\n",
      "6/5 [====================================] - 2s 339ms/step - loss: 0.9143 - acc: 0.7062 - val_loss: 1.2323 - val_acc: 0.7143\n",
      "Epoch 13/25\n",
      "6/5 [====================================] - 2s 343ms/step - loss: 0.9619 - acc: 0.7028 - val_loss: 1.2636 - val_acc: 0.6667\n",
      "Epoch 14/25\n",
      "6/5 [====================================] - 2s 338ms/step - loss: 0.8991 - acc: 0.7344 - val_loss: 1.2727 - val_acc: 0.6508\n",
      "Epoch 15/25\n",
      "6/5 [====================================] - 2s 336ms/step - loss: 0.9198 - acc: 0.6940 - val_loss: 1.2372 - val_acc: 0.6825\n",
      "Epoch 16/25\n",
      "6/5 [====================================] - 2s 337ms/step - loss: 0.8826 - acc: 0.7107 - val_loss: 1.2730 - val_acc: 0.6667\n",
      "Epoch 17/25\n",
      "6/5 [====================================] - 2s 337ms/step - loss: 0.8120 - acc: 0.7267 - val_loss: 1.2283 - val_acc: 0.6984\n",
      "Epoch 18/25\n",
      "6/5 [====================================] - 2s 342ms/step - loss: 0.7207 - acc: 0.7672 - val_loss: 1.2922 - val_acc: 0.6984\n",
      "Epoch 19/25\n",
      "6/5 [====================================] - 2s 340ms/step - loss: 0.7582 - acc: 0.7904 - val_loss: 1.3182 - val_acc: 0.6825\n",
      "Epoch 20/25\n",
      "6/5 [====================================] - 2s 341ms/step - loss: 0.7302 - acc: 0.7438 - val_loss: 1.2263 - val_acc: 0.6825\n",
      "Epoch 21/25\n",
      "6/5 [====================================] - 2s 338ms/step - loss: 0.8407 - acc: 0.7202 - val_loss: 1.1809 - val_acc: 0.6825\n",
      "Epoch 22/25\n",
      "6/5 [====================================] - 2s 339ms/step - loss: 0.8917 - acc: 0.7304 - val_loss: 1.2300 - val_acc: 0.6825\n",
      "Epoch 23/25\n",
      "6/5 [====================================] - 2s 341ms/step - loss: 0.7859 - acc: 0.7321 - val_loss: 1.2719 - val_acc: 0.6984\n",
      "Epoch 24/25\n",
      "6/5 [====================================] - 2s 341ms/step - loss: 0.7174 - acc: 0.7626 - val_loss: 1.3026 - val_acc: 0.6984\n",
      "Epoch 25/25\n",
      "6/5 [====================================] - 2s 345ms/step - loss: 0.6825 - acc: 0.8001 - val_loss: 1.2185 - val_acc: 0.6825\n"
     ]
    }
   ],
   "source": [
    "# train the network\n",
    "H = model.fit_generator(aug.flow(trainX, trainY, batch_size=BS),\n",
    "    validation_data=(testX, testY), steps_per_epoch=len(trainX) // BS,\n",
    "    epochs=EPOCHS, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the loss and accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot the training loss and accuracy \n",
    "plt.style.use(\"ggplot\")\n",
    "plt.figure()\n",
    "N = EPOCHS\n",
    "plt.plot(np.arange(0, N), H.history[\"loss\"], label=\"train_loss\")\n",
    "plt.plot(np.arange(0, N), H.history[\"val_loss\"], label=\"val_loss\")\n",
    "plt.plot(np.arange(0, N), H.history[\"acc\"], label=\"train_acc\")\n",
    "plt.plot(np.arange(0, N), H.history[\"val_acc\"], label=\"val_acc\")\n",
    "plt.title(\"Training Loss and Accuracy on Label Prediction\")\n",
    "plt.xlabel(\"Epoch #\")\n",
    "plt.ylabel(\"Loss/Accuracy\")\n",
    "plt.legend(loc=\"lower left\")\n",
    "plt.savefig('figure.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbAAAAEgCAYAAADVKCZpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIpFIOiQyApNIJBJJh+T/A5Ip8LM6kZEOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displays chart\n",
    "from IPython.display import Image\n",
    "Image(\"figure.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Take the original data and predict based on the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape original input data images for predicting\n",
    "img_check = np.array(img_list, dtype =\"float\")/225.0\n",
    "\n",
    "predictionsMade = []\n",
    "preds = model.predict(img_check)\n",
    "\n",
    "# Run all data through the prediction model that was created\n",
    "for i in range (0,len(img_check)):\n",
    "    predIndex = np.where(preds[i] == np.amax(preds[i]))\n",
    "    prediction = int(predIndex[0][0])\n",
    "    predictionsMade.append(prediction)\n",
    "    \n",
    "# Count how many correct predictions were made\n",
    "correct = 0\n",
    "for i in range (0,len(predictionsMade)):\n",
    "    if(predictionsMade[i] == yset[i]):\n",
    "        correct += 1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the accuracy of the predictions made"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8055555555555556\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: \" + str(correct/len(yset)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enter a new header for the prediction column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input new column Header Label: \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f73f292fd2384101b7b499d105cbeb14",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c2179d7ae9954b55a3ab16f5ff9b6b02",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Translate back to original csv label names\n",
    "finalPred = []\n",
    "for i in range (0,len(predictionsMade)):\n",
    "    finalPred.append(yval_label[predictionsMade[i]])\n",
    "\n",
    "from IPython.display import display\n",
    "input_text = widgets.Text(\n",
    "    value=None,\n",
    "    placeholder='Type label here',\n",
    "    disabled=False\n",
    ")\n",
    "output_text = widgets.Text(\n",
    "    value=None,\n",
    "    placeholder='New Header will be displayed here',\n",
    "    disabled=False\n",
    ")\n",
    "\n",
    "def bind_input_to_output(sender):\n",
    "    output_text.value = input_text.value\n",
    "\n",
    "input_text.observe(bind_input_to_output)\n",
    "\n",
    "print(\"Input new column Header Label: \")\n",
    "\n",
    "display(input_text)\n",
    "display(output_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write the predictions back to the original CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Append the new column w/ it's new column name\n",
    "df[input_text.value] = finalPred\n",
    "\n",
    "# new file name\n",
    "new_file =  csv_file[:-4]+'_v1.csv'\n",
    "df.to_csv(new_file, index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input survey name here:\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a6826e34c9304975bfab58fe89eef549",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0001dddf42bd4db18c784d8e5777155a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Input survey name\n",
    "\n",
    "from IPython.display import display\n",
    "input_text = widgets.Text()\n",
    "output_text = widgets.Text()\n",
    "\n",
    "def bind_input_to_output(sender):\n",
    "    output_text.value = input_text.value\n",
    "\n",
    "# Tell the text input widget to call bind_input_to_output() on submit\n",
    "input_text.observe(bind_input_to_output)\n",
    "\n",
    "print(\"Input survey name here:\")\n",
    "# Display input text box widget for input\n",
    "display(input_text)\n",
    "\n",
    "display(output_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survey name is: Cyclops2\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "#Print survey name\n",
    "survey_name = output_text.value\n",
    "print(\"Survey name is:\", survey_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "ename": "ConnectionError",
     "evalue": "HTTPConnectionPool(host='localhost', port=3001): Max retries exceeded with url: /uploadCSV (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x000001C8E6B1D588>: Failed to establish a new connection: [WinError 10061] No connection could be made because the target machine actively refused it',))",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mConnectionRefusedError\u001b[0m                    Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    140\u001b[0m             conn = connection.create_connection(\n\u001b[1;32m--> 141\u001b[1;33m                 (self.host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[0;32m    142\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\urllib3\\util\\connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     82\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 83\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     84\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\urllib3\\util\\connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     72\u001b[0m                 \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 73\u001b[1;33m             \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msa\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     74\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mConnectionRefusedError\u001b[0m: [WinError 10061] No connection could be made because the target machine actively refused it",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mNewConnectionError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    600\u001b[0m                                                   \u001b[0mbody\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 601\u001b[1;33m                                                   chunked=chunked)\n\u001b[0m\u001b[0;32m    602\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    356\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 357\u001b[1;33m             \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mhttplib_request_kw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    358\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\http\\client.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1238\u001b[0m         \u001b[1;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1239\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1240\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_request\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1284\u001b[0m             \u001b[0mbody\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'body'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1285\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1286\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\http\\client.py\u001b[0m in \u001b[0;36mendheaders\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1233\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1234\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1235\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_output\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1025\u001b[0m         \u001b[1;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1026\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1027\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\http\\client.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    963\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 964\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    965\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    165\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 166\u001b[1;33m         \u001b[0mconn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_new_conn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    167\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prepare_conn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    149\u001b[0m             raise NewConnectionError(\n\u001b[1;32m--> 150\u001b[1;33m                 self, \"Failed to establish a new connection: %s\" % e)\n\u001b[0m\u001b[0;32m    151\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNewConnectionError\u001b[0m: <urllib3.connection.HTTPConnection object at 0x000001C8E6B1D588>: Failed to establish a new connection: [WinError 10061] No connection could be made because the target machine actively refused it",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    439\u001b[0m                     \u001b[0mretries\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 440\u001b[1;33m                     \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    441\u001b[0m                 )\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    638\u001b[0m             retries = retries.increment(method, url, error=e, _pool=self,\n\u001b[1;32m--> 639\u001b[1;33m                                         _stacktrace=sys.exc_info()[2])\n\u001b[0m\u001b[0;32m    640\u001b[0m             \u001b[0mretries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\urllib3\\util\\retry.py\u001b[0m in \u001b[0;36mincrement\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m    387\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 388\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcause\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    389\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMaxRetryError\u001b[0m: HTTPConnectionPool(host='localhost', port=3001): Max retries exceeded with url: /uploadCSV (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x000001C8E6B1D588>: Failed to establish a new connection: [WinError 10061] No connection could be made because the target machine actively refused it',))",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mConnectionError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-55-a08a7450f2b6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mupload_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'name'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0msurvey_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'dzc'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mdzc_url\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'user'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0muser\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mfiles\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m\"file\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"rb\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpost\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mupload_url\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfiles\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfiles\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mupload_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mpost\u001b[1;34m(url, data, json, **kwargs)\u001b[0m\n\u001b[0;32m    110\u001b[0m     \"\"\"\n\u001b[0;32m    111\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 112\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'post'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    113\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    114\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     56\u001b[0m     \u001b[1;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 58\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     59\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    506\u001b[0m         }\n\u001b[0;32m    507\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 508\u001b[1;33m         \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    509\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    510\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    616\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    617\u001b[0m         \u001b[1;31m# Send the request\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 618\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    619\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    620\u001b[0m         \u001b[1;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    506\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mSSLError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    507\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 508\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    509\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    510\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mClosedPoolError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mConnectionError\u001b[0m: HTTPConnectionPool(host='localhost', port=3001): Max retries exceeded with url: /uploadCSV (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x000001C8E6B1D588>: Failed to establish a new connection: [WinError 10061] No connection could be made because the target machine actively refused it',))"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "upload_url = \"http://localhost:3001/uploadCSV\"\n",
    "upload_data = {'name': survey_name, 'dzc': dzc_url, 'user':user}\n",
    "files = {\"file\": open(new_file, \"rb\")}\n",
    "r = requests.post(upload_url, files=files, data=upload_data)\n",
    "print(r.status_code, r.reason)\n",
    "\n",
    "regex = re.compile('[^0-9a-zA-Z_]')\n",
    "survey_url = survey_name\n",
    "survey_url =  regex.sub('_', survey_url)\n",
    "\n",
    "url = \"http://localhost:3001/main/file=\" + user + \"_\" + survey_url + \".csv\" + \"&views=111000&view=grid\"\n",
    "print(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}