Projects/1_Autocorrect_.ipynb

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      "cell_type": "markdown",
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        "id": "oS5jExyuC3gW"
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      "source": [
        "#**1-Autocorrect with Python: How It Works?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCHC-QOhDKAh"
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      "source": [
        "- Before I get into the coding stuff let’s understand how autocorrect works.\n",
        "\n",
        "- Let’s say you typed a word in your keyboard if the word will exist in the vocabulary of our smartphone then it will assume that you have written the right word.\n",
        "\n",
        "- Now it doesn’t matter whether you write a name, a noun or any word on the planet."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V_T1OIpuGeI8"
      },
      "source": [
        "#**2-Build an Autocorrect with Python**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T33QNHwZGja2"
      },
      "source": [
        "- I hope you now know what autocorrect is and how it works.\n",
        "\n",
        "-  Now let’s see how we can build an autocorrect feature with Python. Like our smartphone uses history to match the type words whether it’s correct or not.\n",
        "\n",
        "- So here we also need to use some words to put the functionality in our autocorrect.\n",
        "\n",
        "- For this task, we need some libraries.\n",
        "\n",
        "- The libraries that I am going to use are very general as a machine learning practitioner.\n",
        " \n",
        "- So you must be having all the libraries installed in your system already except one.\n",
        "\n",
        "- You need to install a library known as textdistance, which can be easily installed by using the pip command; pip install textdistance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6OKQkI3yhiQp"
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      "source": [
        "!pip install textdistance"
      ],
      "execution_count": null,
      "outputs": []
    },
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              "ok": true,
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                  "content-type",
                  "application/javascript"
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              "status_text": ""
            }
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          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "cf8942d7-2387-4358-f38e-fda72da46e4c"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "\n",
        "\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d45d11c9-5eb6-4c84-be4f-fa15e7a60574\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-d45d11c9-5eb6-4c84-be4f-fa15e7a60574\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving book.txt to book.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X7qdrBMRHy8w"
      },
      "source": [
        "**Now let’s get started with this task by importing all the necessary packages and by reading our text file:**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6ReBTwpceuH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5bfabd18-b1e0-45a4-d568-b6524c87e6e0"
      },
      "source": [
        "import pandas as pd \n",
        "import numpy as np #numeric python lab\n",
        "import textdistance #python library for comparing distance between two or more\n",
        "import re           #you can start using regular expressionsSearch the string to see if it starts with \"The\" and ends with \"Spain\"\n",
        "from collections import Counter #A counter is a container that stores elements as dictionary keys, and their counts are stored as dictionary values\n",
        "words = []\n",
        "with open('book.txt', 'r') as f: #File handling with file name is book.\n",
        "    file_name_data = f.read() #Read the file.\n",
        "    file_name_data=file_name_data.lower()\n",
        "    words = re.findall('\\w+',file_name_data)\n",
        "# This is our vocabulary\n",
        "V = set(words)\n",
        "print(f\"The first ten words in the text are: \\n{words[0:10]}\")\n",
        "print(f\"There are {len(V)} unique words in the vocabulary.\")"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The first ten words in the text are: \n",
            "['the', 'project', 'gutenberg', 'ebook', 'of', 'moby', 'dick', 'or', 'the', 'whale']\n",
            "There are 17647 unique words in the vocabulary.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YiOHQkBqc3NF"
      },
      "source": [
        "The first ten words in the text are: \n",
        "['moby', 'dick', 'by', 'herman', 'melville', '1851', 'etymology', 'supplied', 'by', 'a']\n",
        "There are 17140 unique words in the vocabulary."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BO-RrGlUI_8Z",
        "outputId": "5e9b235a-3709-4ff4-888e-c1826b21f990"
      },
      "source": [
        "word_freq_dict = {}  \r\n",
        "word_freq_dict = Counter(words)\r\n",
        "print(word_freq_dict.most_common()[0:10])"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[('the', 14703), ('of', 6742), ('and', 6517), ('a', 4799), ('to', 4707), ('in', 4238), ('that', 3081), ('it', 2534), ('his', 2530), ('i', 2120)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VuAd-CzNJInn"
      },
      "source": [
        "probs = {}     \r\n",
        "Total = sum(word_freq_dict.values())    \r\n",
        "for k in word_freq_dict.keys():\r\n",
        "    probs[k] = word_freq_dict[k]/Total"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z_QznNVxc_b5"
      },
      "source": [
        "#**3-Finding Similar Words**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V2NTnchjdG3I"
      },
      "source": [
        "Now we will sort similar words according to the Jaccard distance by calculating the 2 grams Q of the words. Next, we will return the 5 most similar words ordered by similarity and probability:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N6XRXYofdSHR"
      },
      "source": [
        "\n",
        "input_word=input(\"neverteless\")\n",
        "input_word = input_word.lower()\n",
        "if input_word in V:\n",
        "  print('a is greater ')\n",
        "else:\n",
        "  similarities = [1-(textdistance.Jaccard(qval=2).distance(v,input_word)) for v in word_freq_dict.keys()]\n",
        "  df = pd.DataFrame.from_dict(probs, orient='index').reset_index()\n",
        "  df = df.rename(columns={'index':'Word', 0:'Prob'})\n",
        "  df['Similarity'] = similarities\n",
        "  output = df.sort_values(['Similarity', 'Prob'], ascending=False).head()\n",
        "  print(output)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v5GkZLh0YCt7"
      },
      "source": [
        "my_autocorrect('neverteless')\r\n"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s6rZsFKbClj7"
      },
      "source": [
        "[[1]Autocorrect with Python: How It Works?](https://thecleverprogrammer.com/2020/10/04/autocorrect-with-python/?fbclid=IwAR3HmcHiWRGYw9rst2kFOHeqsyfKQsg6322Dy25rTkVh54PnLm_Xe_Elf5w)"
      ]
    }
  ]
}