AMMICO/notebooks/get-text-from-image.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dcaa3da1",
   "metadata": {},
   "source": [
    "# Notebook for text extraction on image\n",
    "Inga Ulusoy, SSC, July 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f43f327c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if running on google colab\n",
    "# flake8-noqa-cell\n",
    "import os\n",
    "\n",
    "if \"google.colab\" in str(get_ipython()):\n",
    "    # update python version\n",
    "    # install setuptools\n",
    "    !pip install setuptools==61 -qqq\n",
    "    # install misinformation\n",
    "    !pip install git+https://github.com/ssciwr/misinformation.git -qqq\n",
    "    # mount google drive for data and API key\n",
    "    from google.colab import drive\n",
    "\n",
    "    drive.mount(\"/content/drive\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf362e60",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from IPython.display import Image, display\n",
    "import misinformation\n",
    "from misinformation import utils as mutils\n",
    "from misinformation import display as mdisplay\n",
    "import tensorflow as tf\n",
    "\n",
    "print(tf.config.list_physical_devices(\"GPU\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27675810",
   "metadata": {},
   "outputs": [],
   "source": [
    "# download the models if they are not there yet\n",
    "!python -m spacy download en_core_web_md\n",
    "!python -m textblob.download_corpora"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6da3a7aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "images = mutils.find_files(path=\"../data/all/\", limit=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf811ce0",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in images[0:3]:\n",
    "    display(Image(filename=i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b32409f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mydict = mutils.initialize_dict(images[0:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b8b929f",
   "metadata": {},
   "source": [
    "# google cloud vision API\n",
    "First 1000 images per month are free."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbf74c0b-52fe-4fb8-b617-f18611e8f986",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\n",
    "    \"GOOGLE_APPLICATION_CREDENTIALS\"\n",
    "] = \"../data/misinformation-campaign-981aa55a3b13.json\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0891b795-c7fe-454c-a45d-45fadf788142",
   "metadata": {},
   "source": [
    "## Inspect the elements per image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c6ecc88",
   "metadata": {},
   "outputs": [],
   "source": [
    "mdisplay.explore_analysis(mydict, identify=\"text-on-image\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c3e72b5-0e57-4019-b45e-3e36a74e7f52",
   "metadata": {},
   "source": [
    "## Or directly analyze for further processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "365c78b1-7ff4-4213-86fa-6a0a2d05198f",
   "metadata": {},
   "outputs": [],
   "source": [
    "for key in mydict:\n",
    "    print(key)\n",
    "    mydict[key] = misinformation.text.TextDetector(\n",
    "        mydict[key], analyse_text=True\n",
    "    ).analyse_image()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c063eda",
   "metadata": {},
   "source": [
    "## Convert to dataframe and write csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5709c2cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "outdict = mutils.append_data_to_dict(mydict)\n",
    "df = mutils.dump_df(outdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4f05637",
   "metadata": {},
   "outputs": [],
   "source": [
    "# check the dataframe\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf6c9ddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write the csv\n",
    "df.to_csv(\"./data_out.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bc8ac0a",
   "metadata": {},
   "source": [
    "# Topic analysis\n",
    "The topic analysis is carried out using [BERTopic](https://maartengr.github.io/BERTopic/index.html) using an embedded model through a [spaCy](https://spacy.io/) pipeline."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4931941b",
   "metadata": {},
   "source": [
    "BERTopic takes a list of strings as input. The more items in the list, the better for the topic modeling. If the below returns an error for `analyse_topic()`, the reason can be that your dataset is too small.\n",
    "### Option 1: Use the dictionary as obtained from the above analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3450a61",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a list of all the text_english entries per analysed image from the mydict variable as above\n",
    "topic_model, topic_df, most_frequent_topics = misinformation.text.PostprocessText(\n",
    "    mydict=mydict\n",
    ").analyse_topic()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95667342",
   "metadata": {},
   "source": [
    "### Option 2: Read in a csv\n",
    "Not to analyse too many images on google Cloud Vision, use the csv output to obtain the text (when rerunning already analysed images)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5530e436",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_file_path = \"data_out.csv\"\n",
    "topic_model, topic_df, most_frequent_topics = misinformation.text.PostprocessText(\n",
    "    use_csv=True, csv_path=input_file_path\n",
    ").analyse_topic(return_topics=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b6ef6d7",
   "metadata": {},
   "source": [
    "### Access frequent topics\n",
    "A topic of `-1` stands for an outlier and should be ignored. Topic count is the number of occurence of that topic. The output is structured from most frequent to least frequent topic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43288cda-61bb-4ff1-a209-dcfcc4916b1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(topic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3316770",
   "metadata": {},
   "source": [
    "### Get information for specific topic\n",
    "The most frequent topics can be accessed through `most_frequent_topics` with the most occuring topics first in the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db14fe03",
   "metadata": {},
   "outputs": [],
   "source": [
    "for topic in most_frequent_topics:\n",
    "    print(\"Topic:\", topic)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d10f701e",
   "metadata": {},
   "source": [
    "### Topic visualization\n",
    "The topics can also be visualized. Careful: This only works if there is sufficient data (quantity and quality)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2331afe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "topic_model.visualize_topics()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4eaf353",
   "metadata": {},
   "source": [
    "### Save the model\n",
    "The model can be saved for future use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5e8377c",
   "metadata": {},
   "outputs": [],
   "source": [
    "topic_model.save(\"misinfo_posts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c94edb9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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