Jupyter_20210404_constructive_course_evalulations.ipynb

{
 "cells": [
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   "cell_type": "markdown",
   "metadata": {
    "id": "oYn3BRAMzjax"
   },
   "source": [
    "Welcome to the Constructive Course Evaluation Filter.  This software uses natural language processing to filter out unconstructive comments, leaving you with only constructive comments in an output .docx file in the \"results\" folder.\n",
    "\n",
    "\n",
    "**Please click the folder icon at the left of the screen** to connect and open a file browser.\n",
    "\n",
    "**Drag your qualitative course evaluations into the white space at left to upload them.  When you're ready, click \"Run all\" from the Runtime menu.**  Then scroll down to watch your progress below the Process Files block.\n",
    "\n",
    "Note that while this page accepts .pdf files, .txt files, and .docx files, we have gotten the best results by copying the desired comments, opening a new Word document, and choosing \"paste without formatting\" by right-clicking or from the Edit menu.\n",
    "\n",
    "**When the page finishes (typically after 5-10 minutes), click the folder icon at left, then click again to refresh.  Your detoxified course evaluation comments will be available to view or download as .docx files in the *results* folder.**  Now savor the joy of iterative improvement with a bit less vitriol!  \n",
    "\n",
    "Please note that the model also removes a small number of constructive comments as it does its work but is calibrated to leave behind enough for us to take useful feedback without having to read through any nasty ones.\n",
    "\n",
    "Please contact John at jgoeltz (at) csumb (dot) edu with feedback or questions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vv2-Ue6NTig3"
   },
   "source": [
    "# Install/Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "collapsed": true,
    "id": "EC2wwpPWE7Xr",
    "jupyter": {
     "outputs_hidden": true
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    "outputId": "f4a01887-cf39-4b4c-bb22-a9dfec0a072b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Collecting six==1.12.0\n",
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      "Installing collected packages: six\n",
      "  Attempting uninstall: six\n",
      "    Found existing installation: six 1.15.0\n",
      "    Uninstalling six-1.15.0:\n",
      "      Successfully uninstalled six-1.15.0\n",
      "Successfully installed six-1.12.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "tensorflow 2.4.1 requires six~=1.15.0, but you have six 1.12.0 which is incompatible.\n"
     ]
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "textract 1.6.3 requires six==1.12.0, but you have six 1.15.0 which is incompatible.\n"
     ]
    },
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     "name": "stdout",
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      "Installing collected packages: six\n",
      "  Attempting uninstall: six\n",
      "    Found existing installation: six 1.12.0\n",
      "    Uninstalling six-1.12.0:\n",
      "      Successfully uninstalled six-1.12.0\n",
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      "Requirement already satisfied: backcall in c:\\users\\jgoel\\anaconda3\\lib\\site-packages (from ipython->ktrain) (0.2.0)\n",
      "Requirement already satisfied: parso<0.9.0,>=0.8.0 in c:\\users\\jgoel\\anaconda3\\lib\\site-packages (from jedi>=0.16->ipython->ktrain) (0.8.1)\n",
      "Requirement already satisfied: wcwidth in c:\\users\\jgoel\\anaconda3\\lib\\site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython->ktrain) (0.2.5)\n",
      "Requirement already satisfied: ipython-genutils in c:\\users\\jgoel\\anaconda3\\lib\\site-packages (from traitlets>=4.2->ipython->ktrain) (0.2.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\jgoel\\anaconda3\\lib\\site-packages (from requests->ktrain) (2020.12.5)\n",
      "Requirement already satisfied: idna<3,>=2.5 in c:\\users\\jgoel\\anaconda3\\lib\\site-packages (from requests->ktrain) (2.10)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in c:\\users\\jgoel\\anaconda3\\lib\\site-packages (from requests->ktrain) (3.0.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\jgoel\\anaconda3\\lib\\site-packages (from requests->ktrain) (1.26.3)\n",
      "Requirement already satisfied: click in c:\\users\\jgoel\\anaconda3\\lib\\site-packages (from sacremoses->transformers<=4.3.3,>=4.0.0->ktrain) (7.1.2)\n",
      "Requirement already satisfied: googledrivedownloader in c:\\users\\jgoel\\anaconda3\\lib\\site-packages (0.4)\n"
     ]
    }
   ],
   "source": [
    "# install\n",
    "!pip install textract\n",
    "!pip install python-docx\n",
    "!pip install tensorflow\n",
    "!pip install ktrain\n",
    "!pip install googledrivedownloader\n",
    "\n",
    "# import\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import ktrain\n",
    "from ktrain import text\n",
    "import re\n",
    "import os\n",
    "import textract\n",
    "import docx\n",
    "from google_drive_downloader import GoogleDriveDownloader as gdd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "id": "RVETi5XfvpYR",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A subdirectory or file model already exists.\n",
      "A subdirectory or file results already exists.\n"
     ]
    }
   ],
   "source": [
    "# define paths and global variables\n",
    "\n",
    "PATH_MODEL = './model'\n",
    "LABELS = ['constructive', 'toxic']\n",
    "MODEL_NAME = 'bert-base-uncased'\n",
    "MAXLEN = 150\n",
    "\n",
    "!mkdir model\n",
    "!mkdir results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5vWCvQKTa8hu",
    "outputId": "bf42ddb8-7027-4d59-9f4c-1ddb4367781a"
   },
   "outputs": [],
   "source": [
    "gdd.download_file_from_google_drive(file_id='1IdaLnT4sNyXZ5M0jXXS76lPOh7FQ59Be',\n",
    "                                    dest_path='model/config.json')\n",
    "gdd.download_file_from_google_drive(file_id='1oFvCOk6y93u1sOdmmF7lF_Ep23P_3Xce',\n",
    "                                    dest_path='model/tf_model.h5')\n",
    "gdd.download_file_from_google_drive(file_id='1GsQAszVoiJG3qs-JBLsXeMj9IW7NPl_e',\n",
    "                                    dest_path='model/tf_model.preproc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0,
     "referenced_widgets": [
      "b13464d586a045929cf5079874aeddbf",
      "34c3ac2efad64600a886cd1a382224c6",
      "fda3bab01f884392af523f76ab93c5db",
      "1f53cb28fa564170a22e72fb409156ef",
      "8b0e16eb6e2745c4b3bc5e4e97bcc329",
      "51f6ef29f3b646e68c70fc5f7821ff8e",
      "c9b3b16505384a1988b788b28e2d18ce",
      "e9c0a2edfe8245a5863cfe68364a42e2"
     ]
    },
    "id": "H-Eemb19Jmtd",
    "outputId": "01fd50ed-ee61-46fb-ecc8-2a52aa903ea0"
   },
   "outputs": [],
   "source": [
    "# load model\n",
    "model = ktrain.load_predictor('./model').model\n",
    "\n",
    "# Get predictor object\n",
    "t = text.Transformer(MODEL_NAME, maxlen=MAXLEN)\n",
    "t.preprocess_train_called = True\n",
    "predictor = ktrain.get_predictor(model, preproc=t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qdx5nYVfTqVN"
   },
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "iyal5rCVpq9L"
   },
   "outputs": [],
   "source": [
    "def split_colon(x):\n",
    "    if re.search(\": \", x):\n",
    "        return str(x).split(': ')[1]\n",
    "    else:\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "BKrY45rYgSvy"
   },
   "outputs": [],
   "source": [
    "def import_pdf(pdf_path):\n",
    "    text = textract.process(pdf_path)\n",
    "\n",
    "    # split by new line\n",
    "    temp_word_list = re.split(\"\\\\\\\\n\", str(text))\n",
    "    \n",
    "    # convert list to dataframe \n",
    "    temp_df = pd.DataFrame(temp_word_list)\n",
    "    return temp_df\n",
    "\n",
    "def import_txt(txt_path):\n",
    "    temp_df = pd.read_csv(txt_path, delimiter=\"\\n\")\n",
    "\n",
    "    # convert to strings, remove text such as \"Assignments:\"\n",
    "    temp_df = temp_df.applymap(lambda x: str(x))\n",
    "    temp_df = temp_df.applymap(lambda x: split_colon(x))\n",
    "\n",
    "    # rename column\n",
    "    temp_df = temp_df.rename(columns=({list(temp_df.columns)[0]:'comments'}))\n",
    "    return temp_df\n",
    "\n",
    "def import_word(word_path):\n",
    "    # import and split by \\n using python-docx\n",
    "    doc = docx.Document(word_path)\n",
    "    paras = [p.text for p in doc.paragraphs if p.text]\n",
    "    \n",
    "    # make dataframe of strings; clean up text; rename column\n",
    "    temp_df = pd.DataFrame(paras)\n",
    "    temp_df = temp_df.applymap(lambda x: str(x))\n",
    "    temp_df = temp_df.applymap(lambda x: split_colon(x))\n",
    "    temp_df = temp_df.rename(columns=({list(temp_df.columns)[0]:'comments'}))\n",
    "    return temp_df\n",
    "\n",
    "def process_dataframe(temp_df, filename):\n",
    "    # convert to array (needed for ktrain predictor input)\n",
    "    eval_comments = np.asarray(temp_df.comments)\n",
    "    \n",
    "    # use model to classify comments\n",
    "    eval_tox = predictor.predict(eval_comments)\n",
    "\n",
    "    # concatenate results with comments; label and plot\n",
    "    eval_results_df = pd.concat([pd.DataFrame(eval_comments).rename(\n",
    "        columns={0:'comments'}), pd.DataFrame(eval_tox).rename(\n",
    "            columns={0:'constructive', 1:'toxic'})], axis=1)\n",
    "    eval_results_df.loc[eval_results_df['constructive'] >= \n",
    "                        eval_results_df['toxic'], 'class'] = 'constructive' \n",
    "    eval_results_df.loc[eval_results_df['constructive'] < \n",
    "                        eval_results_df['toxic'], 'class'] = 'toxic'\n",
    "    print('Constructive and Toxic comments found for ' + filename)\n",
    "    plt.bar(['constructive', 'toxic'], eval_results_df['class'].value_counts())\n",
    "    plt.show()\n",
    "\n",
    "    # keep only constructive, non-toxic comments\n",
    "    constructive_results = eval_results_df.where(\n",
    "        eval_results_df['class'] == 'constructive').dropna()['comments']\n",
    " \n",
    "    # convert from series to df so .shape helps set up table properly\n",
    "    constructive_results = pd.DataFrame(constructive_results)\n",
    "\n",
    "    # open an new document\n",
    "    doc = docx.Document()\n",
    "\n",
    "    # add a table; extra row is for the header\n",
    "    t = doc.add_table(constructive_results.shape[0]+1, \n",
    "                      constructive_results.shape[1])\n",
    "\n",
    "    # add the header row\n",
    "    for j in range(constructive_results.shape[-1]):t.cell(0,j).text = \\\n",
    "    constructive_results.columns[j]\n",
    "\n",
    "    # add the rest of the data frame\n",
    "    # may want to redo as nested list comprehension, but it works\n",
    "    for i in range(constructive_results.shape[0]):\n",
    "        for j in range(constructive_results.shape[-1]):\n",
    "            t.cell(i+1,j).text = str(constructive_results.values[i,j])\n",
    "\n",
    "    # save the doc\n",
    "    doc.save('./results/' + filename.split('.')[0] + '.docx')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BvULf--vTzzY"
   },
   "source": [
    "# Process files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 381,
     "referenced_widgets": [
      "5a8b0b139b284cc0a44f42a69ad22e35",
      "d2d459ff0b0b47e0b666d4c20d21f951",
      "70c4d3086f71490aa2e20dab223131b5",
      "16bb5c83c9cf463096ddf319a10c9798",
      "9acf78af098b43fe8773b66300c52d09",
      "8ea910a00bb24e82a553c6919bfa675d",
      "0bb09712f14040c1bc988bb98d3efacc",
      "ea11684c87394d7ea51004a1fcfb3e8a",
      "ff223d4845854bc1bb63e081fac1ba7a",
      "5289f014e808485a858dfbbe15548444",
      "bbd3603f661a477987aa494b5ce95691",
      "c49f585c8c744c789791dc8b0ba6487c",
      "97703c0ebeb04cd994d76949e05ba2bb",
      "d8b385d6ec1c43e1939c48c84c64cf6a",
      "6660de39f0da43c09efc40d40c6c944c",
      "04d18bc2223b4daeb24a7be5ee845a69"
     ]
    },
    "collapsed": true,
    "id": "TM_rMoAdfcM0",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "d5a9df5b-e2a5-424f-9a1d-86334dfd96ac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Constructive and Toxic comments found for short_course_eval_sample.docx\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title ## Plots showing the relative numbers of constructive and toxic comments for each document you uploaded will appear below.  Your output files will be in the \"results\" folder in the file browser at left.\n",
    "\n",
    "for filename in os.listdir(os.getcwd()):\n",
    "    # process any pdf files\n",
    "    if re.search('pdf$', filename):\n",
    "        try:\n",
    "            temp_df = import_pdf(filename)\n",
    "            process_dataframe(temp_df, filename)\n",
    "        except Exception as e:\n",
    "            print('An exception was raised for ' + filename + ':\\n')\n",
    "            print(e)\n",
    "\n",
    "    # process any txt files\n",
    "    if re.search('txt$', filename):\n",
    "        try:\n",
    "            temp_df = import_txt(filename)\n",
    "            process_dataframe(temp_df, filename)\n",
    "        except Exception as e:\n",
    "            print('An exception was raised for ' + filename + ':\\n')\n",
    "            print(e)\n",
    "\n",
    "    # process any Word .docx files\n",
    "    if re.search('docx$', filename):\n",
    "        try:\n",
    "            temp_df = import_word(filename)\n",
    "            process_dataframe(temp_df, filename)\n",
    "        except Exception as e:\n",
    "            print('An exception was raised for ' + filename + ':\\n')\n",
    "            print(e)\n"
   ]
  }
 ],
 "metadata": {
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