Heba_Rouk__Machine_Translation_Final_ENG_FR).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/HebaRouk/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code/blob/main/Heba_Rouk__Machine_Translation_Final_ENG_FR).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Machine Translation Using a Seq2Seq Architecture\n",
        "© 2024, Zaka AI, Inc. All Rights Reserved.\n",
        "\n",
        "---\n",
        "The goal of this colab is to get you more familiar with the Seq2Seq models and their challenges. For this reason, you will be working on machine translation problem where we would have a sentence as input (in english), and the output is gonna be the translated sentence (in french). So just like what happens with Google Translate.\n"
      ],
      "metadata": {
        "id": "xiC75uo6u_Of"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Just to give you a heads up:** We won't be having a model performing like Google translate, but at least we will have an idea about how Google Translate works and the challenges that exist with a translation problem.  "
      ],
      "metadata": {
        "id": "TeK4LPupvg_c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing Libraries"
      ],
      "metadata": {
        "id": "SBTvDTzBv293"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We start by importing numpy and pandas and then we can add the rest"
      ],
      "metadata": {
        "id": "4_j1ZzS3v6N3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install Flask\n",
        "!pip install waitress\n",
        "!pip install flask-ngrok\n",
        "!pip install flask\n",
        "!pip install tensorflow\n",
        "!pip install gradio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tmgiFXB7VOE3",
        "outputId": "51e07882-d6ba-465f-ad34-e8dbc425d664"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: pydub, uvicorn, tomlkit, semantic-version, ruff, python-multipart, markupsafe, ffmpy, aiofiles, starlette, safehttpx, gradio-client, fastapi, gradio\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "n0IARXAX1e1m"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We clone the github repository where our data exists. Here is the github link: https://github.com/zaka-ai/machine_learning_certification/tree/main/Challenge%207"
      ],
      "metadata": {
        "id": "vAcLqZ7uv-SJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Getting the data"
      ],
      "metadata": {
        "id": "i3hLN42axOjn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "from git import Repo  # To clone the repository\n",
        "\n",
        "# Clone the GitHub repository\n",
        "repo_url = \"https://github.com/zaka-ai/machine_learning_certification.git\"\n",
        "repo_dir = \"Challenge_7\"  # Directory to clone the repository into\n",
        "\n",
        "# Check if the directory exists before cloning\n",
        "if not os.path.exists(repo_dir):\n",
        "    print(f\"Cloning repository from {repo_url} into {repo_dir}...\")\n",
        "    Repo.clone_from(repo_url, repo_dir)\n",
        "else:\n",
        "    print(f\"Repository already exists in the directory: {repo_dir}\")\n",
        "\n",
        "# List files in the cloned repository\n",
        "print(\"\\nContents of the cloned repository:\")\n",
        "repo_contents = os.listdir(repo_dir)\n",
        "print(repo_contents)\n",
        "\n",
        "# Specify the CSV file path (update the file name if necessary)\n",
        "csv_file_name = \"Advertising.csv\"  # Replace with the correct file name if needed\n",
        "data_file_path = os.path.join(repo_dir, csv_file_name)\n",
        "\n",
        "# Check if the file exists before attempting to load it\n",
        "if os.path.exists(data_file_path):\n",
        "    # Load the data\n",
        "    data = pd.read_csv(data_file_path)\n",
        "    print(\"\\nDataset successfully loaded!\")\n",
        "\n",
        "    # Show the first few rows of the data\n",
        "    print(\"\\nFirst few rows of the dataset:\")\n",
        "    print(data.head())\n",
        "else:\n",
        "    print(f\"\\nError: The file '{csv_file_name}' was not found in the directory '{repo_dir}'. Please check the file name or path.\")"
      ],
      "metadata": {
        "id": "0-M7cFxTPpqy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27018649-9fd2-47b2-ee86-71a5d061b2da"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning repository from https://github.com/zaka-ai/machine_learning_certification.git into Challenge_7...\n",
            "\n",
            "Contents of the cloned repository:\n",
            "['titanic.csv', 'Challenge 7', 'breast-cancer-wisconsin.csv', 'flu.csv', 'README.md', 'hotel_bookings.csv', 'store_data.csv', 'Advertising.csv', '.git', 'model-deployment-lab']\n",
            "\n",
            "Dataset successfully loaded!\n",
            "\n",
            "First few rows of the dataset:\n",
            "   Unnamed: 0     TV  Radio  Newspaper  Sales\n",
            "0           1  230.1   37.8       69.2   22.1\n",
            "1           2   44.5   39.3       45.1   10.4\n",
            "2           3   17.2   45.9       69.3    9.3\n",
            "3           4  151.5   41.3       58.5   18.5\n",
            "4           5  180.8   10.8       58.4   12.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/zaka-ai/machine_learning_certification"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "znIRnmszVuU-",
        "outputId": "4df4b26d-c9bb-4dcc-f0ac-caf1c33608d4"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'machine_learning_certification'...\n",
            "remote: Enumerating objects: 43, done.\u001b[K\n",
            "remote: Counting objects: 100% (43/43), done.\u001b[K\n",
            "remote: Compressing objects: 100% (39/39), done.\u001b[K\n",
            "remote: Total 43 (delta 10), reused 10 (delta 1), pack-reused 0 (from 0)\u001b[K\n",
            "Receiving objects: 100% (43/43), 43.23 MiB | 20.28 MiB/s, done.\n",
            "Resolving deltas: 100% (10/10), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/machine_learning_certification/Challenge 7"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8HntL5R3VyQC",
        "outputId": "4459bee2-ab55-40c5-b1f6-ed2e4b6f7602"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/machine_learning_certification/Challenge 7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We read the english sentences in a dataframe named \"english\", and the french sentences in a dataframe named \"french\""
      ],
      "metadata": {
        "id": "BaPr0N8cwGAv"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "kFj8gkP01lGT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "cb747991-a181-4fae-c71e-175f3a7ae269"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "english"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "english = pd.read_csv('en.csv',header=None)\n",
        "english.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "P4A7ZKt32A7s",
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        },
        "outputId": "d9578cdd-3e70-45bd-81dd-93d43710f886"
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      "outputs": [
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          "data": {
            "text/plain": [
              "                                                   0\n",
              "0  new jersey est parfois calme pendant l' automn...\n",
              "1  les états-unis est généralement froid en juill...\n",
              "2  california est généralement calme en mars , et...\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  @keyframes spin {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "french"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "french=pd.read_csv('fr.csv',header=None)\n",
        "french.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "english = pd.read_csv('en.csv',header=None)\n",
        "english.head()\n",
        "french=pd.read_csv('fr.csv',header=None)\n",
        "french.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6cjIcwaNV9Ad",
        "outputId": "8885954a-1957-4df1-9db8-116acccdaf25"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                                   0\n",
              "0  new jersey est parfois calme pendant l' automn...\n",
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              "2  california est généralement calme en mars , et...\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "    </g>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "    box-shadow: none;\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ce27285c-701e-464d-9510-e87b35e6dd46 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "french"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**How many sentences does each of the files contain?**"
      ],
      "metadata": {
        "id": "jr8OO1OhwSp4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "XhWJP-b02HKq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec3bf834-b4f8-49dc-de0c-9fadda8f392a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We have 137860 english sentences and 137860 french sentences\n"
          ]
        }
      ],
      "source": [
        "print(\"We have {} english sentences and {} french sentences\".format(len(english),len(french)))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let us concatenate the 2 dataframes into one dataframe that we call **df** where one column has the english senetnces and the other has the french sentences"
      ],
      "metadata": {
        "id": "ITGJN5tIwkDO"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "-ZXxahsB2njn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "277c3faf-9beb-4747-bdb4-783ba4662d0d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                                   0  \\\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
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              "\n",
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              "    border: 2px solid var(--fill-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
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              "</style>\n",
              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "</div>\n",
              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df=pd.concat([english,french],axis=1)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's name the columns as **English** and **French** so that we access them easier."
      ],
      "metadata": {
        "id": "nAr_caXkwwE7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 11,
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        "id": "eOHiQDXx3jFS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "e404c8fc-72dd-48bd-c00d-0e8670b81a9d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             English  \\\n",
              "0  new jersey is sometimes quiet during autumn , ...   \n",
              "1  the united states is usually chilly during jul...   \n",
              "2  california is usually quiet during march , and...   \n",
              "3  the united states is sometimes mild during jun...   \n",
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              "\n",
              "                                              French  \n",
              "0  new jersey est parfois calme pendant l' automn...  \n",
              "1  les états-unis est généralement froid en juill...  \n",
              "2  california est généralement calme en mars , et...  \n",
              "3  les états-unis est parfois légère en juin , et...  \n",
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              "        const element = document.querySelector('#df-b1111998-6e6f-4107-a760-d3f29ae87e5e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-76127802-b38f-41ad-a4b9-a9da00c4060e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-76127802-b38f-41ad-a4b9-a9da00c4060e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-76127802-b38f-41ad-a4b9-a9da00c4060e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df.columns=['English','French']\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pick a sentence and print it in both languages"
      ],
      "metadata": {
        "id": "4xc1TsEHw9yC"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "QuRVWch23ujo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d28e0e8d-dcb2-446b-a400-6b25d7fda404"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "English california is usually quiet during march , and it is usually hot in june .\n",
            "French california est généralement calme en mars , et il est généralement chaud en juin .\n"
          ]
        }
      ],
      "source": [
        "print(\"English\",df['English'][2])\n",
        "print(\"French\",df['French'][2])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Cleaning Data"
      ],
      "metadata": {
        "id": "FQjXYP1txFCi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The data that we have is almost clean as we can see, we just need to remove the punctuations inside of it."
      ],
      "metadata": {
        "id": "xgz6jIoVxHUF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "6YYOt5QftcFI"
      },
      "outputs": [],
      "source": [
        "import regex as re\n",
        "def remove_punctuation(text ):\n",
        "  return re.sub(r'[.!?;,]','',text)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make sure that the punctuation is removed by printing the example that you printed earlier."
      ],
      "metadata": {
        "id": "0C1qsC9LxZPb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "T80tiWxe84G7"
      },
      "outputs": [],
      "source": [
        "df['English'] = df['English'].apply(lambda x: remove_punctuation(x))\n",
        "df['French']=df['French'].apply(lambda x: remove_punctuation(x))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"English\",df['English'][2])\n",
        "print(\"French\",df['French'][2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MMB62kOoWVvC",
        "outputId": "761035f0-00af-4ffe-9ea0-cfe3c3fa2c6b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "English california is usually quiet during march  and it is usually hot in june \n",
            "French california est généralement calme en mars  et il est généralement chaud en juin \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Exploring the Data"
      ],
      "metadata": {
        "id": "ZuFNjoBAx4oN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Add a column **ENG Length** to the dataset that shows how many words does a sentence contain, and do the same for french in a column called **FR Length**"
      ],
      "metadata": {
        "id": "ATfefzPExi2k"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "Dakeo81s352S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "5063fa44-9113-49af-e1f5-fd46beb7b333"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             English  \\\n",
              "0  new jersey is sometimes quiet during autumn  a...   \n",
              "1  the united states is usually chilly during jul...   \n",
              "2  california is usually quiet during march  and ...   \n",
              "3  the united states is sometimes mild during jun...   \n",
              "4  your least liked fruit is the grape  but my le...   \n",
              "\n",
              "                                              French  ENG Length  \n",
              "0  new jersey est parfois calme pendant l' automn...          13  \n",
              "1  les états-unis est généralement froid en juill...          15  \n",
              "2  california est généralement calme en mars  et ...          13  \n",
              "3  les états-unis est parfois légère en juin  et ...          14  \n",
              "4  votre moins aimé fruit est le raisin  mais mon...          14  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5db0a0fb-7113-4c30-a363-d15ed1176c90\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>English</th>\n",
              "      <th>French</th>\n",
              "      <th>ENG Length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>new jersey is sometimes quiet during autumn  a...</td>\n",
              "      <td>new jersey est parfois calme pendant l' automn...</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>the united states is usually chilly during jul...</td>\n",
              "      <td>les états-unis est généralement froid en juill...</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>california is usually quiet during march  and ...</td>\n",
              "      <td>california est généralement calme en mars  et ...</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>the united states is sometimes mild during jun...</td>\n",
              "      <td>les états-unis est parfois légère en juin  et ...</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>your least liked fruit is the grape  but my le...</td>\n",
              "      <td>votre moins aimé fruit est le raisin  mais mon...</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5db0a0fb-7113-4c30-a363-d15ed1176c90')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5db0a0fb-7113-4c30-a363-d15ed1176c90 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5db0a0fb-7113-4c30-a363-d15ed1176c90');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d06a327c-924c-4214-ab53-fdf08b370dca\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d06a327c-924c-4214-ab53-fdf08b370dca')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d06a327c-924c-4214-ab53-fdf08b370dca button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "df['ENG Length']=df['English'].apply(lambda x: len(x.split()))\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "49keasjaPaaK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "0764f32c-2b3e-4174-f835-569bce7cef78"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             English  \\\n",
              "0  new jersey is sometimes quiet during autumn  a...   \n",
              "1  the united states is usually chilly during jul...   \n",
              "2  california is usually quiet during march  and ...   \n",
              "3  the united states is sometimes mild during jun...   \n",
              "4  your least liked fruit is the grape  but my le...   \n",
              "\n",
              "                                              French  ENG Length  FR Length  \n",
              "0  new jersey est parfois calme pendant l' automn...          13         14  \n",
              "1  les états-unis est généralement froid en juill...          15         13  \n",
              "2  california est généralement calme en mars  et ...          13         13  \n",
              "3  les états-unis est parfois légère en juin  et ...          14         13  \n",
              "4  votre moins aimé fruit est le raisin  mais mon...          14         14  "
            ],
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              "        document.querySelector('#df-8fc400c7-e7a8-44f4-9759-e41791823e8a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8fc400c7-e7a8-44f4-9759-e41791823e8a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-29e46935-ba79-4c6c-ae4f-2c49634fcaf9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "df['FR Length']=df['French'].apply(lambda x: len(x.split()))\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualize the distribution of the lengths of english sentences and french sentences."
      ],
      "metadata": {
        "id": "AjQLW0K5xwx1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "_q_UIMJ09L24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "outputId": "b98a13e3-25d5-41ef-9587-870599ff5776"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df[\"ENG Length\"].hist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "TSn4L7kW9R7g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "outputId": "ce83c507-b81a-4bae-dc53-a6ba943a9792"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df[\"FR Length\"].hist()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Get the maximum length of an english sentence and the maximum length of a french sentence."
      ],
      "metadata": {
        "id": "BDXb2d9ix9DV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "BpnBB04U_lHd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b5c3a53-cd91-4936-b5be-80d8d68a9eb3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The maximum length of an english sentence is 15 and the maximum length of a french sentence is 21\n"
          ]
        }
      ],
      "source": [
        "max_eng=max(df['ENG Length'])\n",
        "max_fr=max(df['FR Length'])\n",
        "print(\"The maximum length of an english sentence is {} and the maximum length of a french sentence is {}\".format(max_eng,max_fr))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Preprocessing the Data"
      ],
      "metadata": {
        "id": "s4s-spsRyGJv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order for the data to be fed to the model, it has to be tokenized and padded."
      ],
      "metadata": {
        "id": "N0ZmIT2GyJMU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Tokenization"
      ],
      "metadata": {
        "id": "R0r9z-eErm9H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**To tokenize english and french sentences, we can use only one tokenizer. True or False?**"
      ],
      "metadata": {
        "id": "X5L_zkhfyQuX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[Share Your Zaka]"
      ],
      "metadata": {
        "id": "1Z0ZcNOeyauD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tokenize the sentences that we have."
      ],
      "metadata": {
        "id": "814mKDFiymcY"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "aiXlciqFuQzW"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "\n",
        "eng_tokenizer= Tokenizer()\n",
        "eng_tokenizer.fit_on_texts(df['English'])\n",
        "fr_tokenizer=Tokenizer()\n",
        "fr_tokenizer.fit_on_texts(df['French'])\n",
        "fr_tokenizer=fr_tokenizer.texts_to_sequences(df['French'])\n",
        "eng_tokenizer=eng_tokenizer.texts_to_sequences(df['English'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**How many unique words do we have in english and in french?**"
      ],
      "metadata": {
        "id": "aUN01jDXys9B"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "1WahkdzKvIlO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91b6b848-5cfa-418a-8149-3ec12c73e18a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We have 200 unique words in English and 345 unique words in French.\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "\n",
        "# Initialize tokenizers\n",
        "eng_tokenizer = Tokenizer()\n",
        "fr_tokenizer = Tokenizer()\n",
        "\n",
        "# Fit tokenizers on text data\n",
        "eng_tokenizer.fit_on_texts(df['English'])\n",
        "fr_tokenizer.fit_on_texts(df['French'])\n",
        "\n",
        "# Get vocabulary sizes\n",
        "eng_vocab_size = len(eng_tokenizer.word_index) + 1  # Adding 1 for padding/indexing\n",
        "fr_vocab_size = len(fr_tokenizer.word_index) + 1\n",
        "\n",
        "print(f\"We have {eng_vocab_size} unique words in English and {fr_vocab_size} unique words in French.\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Padding"
      ],
      "metadata": {
        "id": "g0C2RJjArtJd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**What should be the length of the sequences that we have after padding?**"
      ],
      "metadata": {
        "id": "vXdTXMo5y8oB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[Share your Zaka]"
      ],
      "metadata": {
        "id": "9wtHQsgXzImq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Perform padding on the sequences that we have."
      ],
      "metadata": {
        "id": "hRXayRzVzQD4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "oNdO9EZrxvmN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3321fa2c-3a3a-48ae-c44d-7565de5ff16a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Padding completed successfully!\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "# Ensure text data is properly loaded\n",
        "df['English'] = df['English'].astype(str)\n",
        "df['French'] = df['French'].astype(str)\n",
        "\n",
        "# Initialize tokenizers\n",
        "eng_tokenizer = Tokenizer()\n",
        "fr_tokenizer = Tokenizer()\n",
        "\n",
        "# Fit tokenizers on text data\n",
        "eng_tokenizer.fit_on_texts(df['English'].tolist())\n",
        "fr_tokenizer.fit_on_texts(df['French'].tolist())\n",
        "\n",
        "# Convert text to sequences\n",
        "eng_tokenized = eng_tokenizer.texts_to_sequences(df['English'].tolist())\n",
        "fr_tokenized = fr_tokenizer.texts_to_sequences(df['French'].tolist())\n",
        "\n",
        "# Determine max sequence lengths\n",
        "max_eng = max(len(seq) for seq in eng_tokenized)\n",
        "max_fr = max(len(seq) for seq in fr_tokenized)\n",
        "\n",
        "# Pad sequences\n",
        "fr_tokenized_padded = pad_sequences(fr_tokenized, maxlen=max_fr, padding='post')\n",
        "eng_tokenized_padded = pad_sequences(eng_tokenized, maxlen=max_eng, padding='post')\n",
        "\n",
        "print(\"Padding completed successfully!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Modeling"
      ],
      "metadata": {
        "id": "JxvvVU3ezUHR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "After preprrocessing the data, we can build our model. Start by building a baseline architecture relying on one directional RNNs, LSTMs, or GRUs. It will be good to lookup how to build Seq2Seq models, there are some new layers that will help you like RepeatVector and TimeDistributed."
      ],
      "metadata": {
        "id": "FEKujJUEzVux"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "9oydzHkr3zDC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "09d14ee1-a3e5-4d8a-af7f-ba380fb9825c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m256\u001b[0m)             │          \u001b[38;5;34m51,456\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ bidirectional (\u001b[38;5;33mBidirectional\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │         \u001b[38;5;34m789,504\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ repeat_vector (\u001b[38;5;33mRepeatVector\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m512\u001b[0m)             │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ gru_1 (\u001b[38;5;33mGRU\u001b[0m)                          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m256\u001b[0m)             │         \u001b[38;5;34m591,360\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ time_distributed (\u001b[38;5;33mTimeDistributed\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m346\u001b[0m)             │          \u001b[38;5;34m88,922\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ embedding (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)             │          <span style=\"color: #00af00; text-decoration-color: #00af00\">51,456</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ bidirectional (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │         <span style=\"color: #00af00; text-decoration-color: #00af00\">789,504</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ repeat_vector (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">RepeatVector</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)             │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ gru_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GRU</span>)                          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">591,360</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ time_distributed (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">346</span>)             │          <span style=\"color: #00af00; text-decoration-color: #00af00\">88,922</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,521,242\u001b[0m (5.80 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,521,242</span> (5.80 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,521,242\u001b[0m (5.80 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,521,242</span> (5.80 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from keras.layers import Dense, LSTM, GRU, Embedding, RepeatVector, TimeDistributed, Bidirectional\n",
        "from keras.models import Sequential\n",
        "\n",
        "# Define model\n",
        "model = Sequential()\n",
        "\n",
        "# Encoder: Embedding + Bidirectional GRU\n",
        "model.add(Embedding(input_dim=eng_vocab_size + 1, output_dim=256))  # Removed input_length\n",
        "model.add(Bidirectional(GRU(256, return_sequences=False)))  # return_sequences=False for encoder\n",
        "\n",
        "# Repeat vector for decoder\n",
        "model.add(RepeatVector(max_fr))\n",
        "\n",
        "# Decoder: GRU + Dense output\n",
        "model.add(GRU(256, return_sequences=True))\n",
        "model.add(TimeDistributed(Dense(fr_vocab_size + 1, activation=\"softmax\")))\n",
        "\n",
        "# Build the model by specifying input shape\n",
        "model.build(input_shape=(None, max_eng))  # Manually specify input shape\n",
        "\n",
        "# Print summary\n",
        "model.summary()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Compile and train the model.\n",
        "**FYI:** While specifying the architecture of your model and the number of epochs for training, keeep in your mind that your model might take A LOT of time to train."
      ],
      "metadata": {
        "id": "aP10HtNBzpT0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "lWw4nBNIFp9D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41790c0e-7646-44a3-bb81-f401c3f76412"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "\u001b[1m1724/1724\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m825s\u001b[0m 473ms/step - accuracy: 0.5911 - loss: 1.7580 - val_accuracy: 0.7984 - val_loss: 0.6919\n",
            "Epoch 2/5\n",
            "\u001b[1m1724/1724\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m828s\u001b[0m 453ms/step - accuracy: 0.8344 - loss: 0.5579 - val_accuracy: 0.9043 - val_loss: 0.3386\n",
            "Epoch 3/5\n",
            "\u001b[1m1724/1724\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m802s\u001b[0m 453ms/step - accuracy: 0.9318 - loss: 0.2433 - val_accuracy: 0.9493 - val_loss: 0.1737\n",
            "Epoch 4/5\n",
            "\u001b[1m1724/1724\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m834s\u001b[0m 472ms/step - accuracy: 0.9553 - loss: 0.1519 - val_accuracy: 0.9571 - val_loss: 0.1438\n",
            "Epoch 5/5\n",
            "\u001b[1m1724/1724\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m867s\u001b[0m 475ms/step - accuracy: 0.9644 - loss: 0.1202 - val_accuracy: 0.9642 - val_loss: 0.1269\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7e74a7a8dc10>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
        "model.fit(x=eng_tokenized_padded,y=fr_tokenized_padded,validation_split=0.2,epochs=5,batch_size=64)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define a function that gets an input sentence in english and gives the output sentence in the french language."
      ],
      "metadata": {
        "id": "9UoEcxyJztiQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "def translate_sentence(model, sentence, eng_tokenizer, fr_tokenizer, max_eng, max_fr):\n",
        "    \"\"\"\n",
        "    Translates an English sentence into French using a trained model.\n",
        "\n",
        "    Parameters:\n",
        "    - model: The trained sequence-to-sequence model.\n",
        "    - sentence: The input English sentence (string).\n",
        "    - eng_tokenizer: Tokenizer for English words.\n",
        "    - fr_tokenizer: Tokenizer for French words.\n",
        "    - max_eng: Maximum input sequence length.\n",
        "    - max_fr: Maximum output sequence length.\n",
        "\n",
        "    Returns:\n",
        "    - Translated French sentence (string).\n",
        "    \"\"\"\n",
        "    # Tokenize and pad the input sentence\n",
        "    sequence = eng_tokenizer.texts_to_sequences([sentence])\n",
        "    padded_sequence = pad_sequences(sequence, maxlen=max_eng, padding=\"post\")\n",
        "\n",
        "    # Predict the output sequence\n",
        "    prediction = model.predict(padded_sequence)\n",
        "\n",
        "    # Convert the predicted token indices to words\n",
        "    predicted_indices = np.argmax(prediction, axis=-1)[0]  # Take the first sample\n",
        "\n",
        "    # Convert indices to words\n",
        "    index_to_word = {index: word for word, index in fr_tokenizer.word_index.items()}\n",
        "    translated_words = [index_to_word.get(idx, \"\") for idx in predicted_indices if idx > 0]\n",
        "\n",
        "    return \" \".join(translated_words)\n",
        "\n",
        "# Example usage:\n",
        "# translated_text = translate_sentence(model_new, \"hello how are you\", eng_tokenizer, fr_tokenizer, max_eng, max_fr)\n",
        "# print(translated_text)\n"
      ],
      "metadata": {
        "id": "fUU_RdCxYpM6"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def final_predictions_modeli(sentence):\n",
        "   y_id_to_word={value:key for key,value in fr_tokenizer.word_index.items()}\n",
        "   y_id_to_word[0]='<PAD>'\n",
        "   sentence=eng_tokenizer.texts_to_sequences([sentence])\n",
        "   sentence=pad_sequences(sentence,maxlen=max_eng,padding='post')\n",
        "   prediction=model.predict(sentence,len(sentence))\n",
        "\n",
        "   print(''.join([y_id_to_word[np.argmax(x)] for x in prediction[0]]))"
      ],
      "metadata": {
        "id": "LBlsuAiOmMSw"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "import tensorflow as tf\n",
        "import os\n",
        "\n",
        "def save_model_artifacts(model, eng_tokenizer, fr_tokenizer, max_eng, max_fr, base_path='.'):\n",
        "    \"\"\"\n",
        "    Save all necessary model artifacts:\n",
        "    - Model weights\n",
        "    - Tokenizers\n",
        "    - Configuration parameters\n",
        "\n",
        "    Args:\n",
        "        model: Trained Keras model\n",
        "        eng_tokenizer: Fitted English tokenizer\n",
        "        fr_tokenizer: Fitted French tokenizer\n",
        "        max_eng: Maximum English sequence length\n",
        "        max_fr: Maximum French sequence length\n",
        "        base_path: Directory to save files\n",
        "    \"\"\"\n",
        "    # Create directory if it doesn't exist\n",
        "    os.makedirs(base_path, exist_ok=True)\n",
        "\n",
        "    # 1. Save the model\n",
        "    model_path = os.path.join(base_path, 'translation_model.h5')\n",
        "    model.save(model_path)\n",
        "    print(f\"Model saved to {model_path}\")\n",
        "\n",
        "    # 2. Save the English tokenizer\n",
        "    eng_tokenizer_path = os.path.join(base_path, 'english_tokenizer.pkl')\n",
        "    with open(eng_tokenizer_path, 'wb') as f:\n",
        "        pickle.dump(eng_tokenizer, f)\n",
        "    print(f\"English tokenizer saved to {eng_tokenizer_path}\")\n",
        "\n",
        "    # 3. Save the French tokenizer\n",
        "    fr_tokenizer_path = os.path.join(base_path, 'french_tokenizer.pkl')\n",
        "    with open(fr_tokenizer_path, 'wb') as f:\n",
        "        pickle.dump(fr_tokenizer, f)\n",
        "    print(f\"French tokenizer saved to {fr_tokenizer_path}\")\n",
        "\n",
        "    # 4. Save configuration parameters\n",
        "    config = {\n",
        "        'max_eng_length': max_eng,\n",
        "        'max_fr_length': max_fr,\n",
        "        'eng_vocab_size': len(eng_tokenizer.word_index) + 1,\n",
        "        'fr_vocab_size': len(fr_tokenizer.word_index) + 1\n",
        "    }\n",
        "\n",
        "    config_path = os.path.join(base_path, 'model_config.pkl')\n",
        "    with open(config_path, 'wb') as f:\n",
        "        pickle.dump(config, f)\n",
        "    print(f\"Model configuration saved to {config_path}\")\n",
        "\n",
        "def load_model_artifacts(base_path='.'):\n",
        "    \"\"\"\n",
        "    Load all saved model artifacts.\n",
        "\n",
        "    Args:\n",
        "        base_path: Directory containing saved files\n",
        "\n",
        "    Returns:\n",
        "        tuple: (model, eng_tokenizer, fr_tokenizer, config)\n",
        "    \"\"\"\n",
        "    # 1. Load the model\n",
        "    model_path = os.path.join(base_path, 'translation_model.h5')\n",
        "    model = tf.keras.models.load_model(model_path)\n",
        "    print(f\"Model loaded from {model_path}\")\n",
        "\n",
        "    # 2. Load the English tokenizer\n",
        "    eng_tokenizer_path = os.path.join(base_path, 'english_tokenizer.pkl')\n",
        "    with open(eng_tokenizer_path, 'rb') as f:\n",
        "        eng_tokenizer = pickle.load(f)\n",
        "    print(f\"English tokenizer loaded from {eng_tokenizer_path}\")\n",
        "\n",
        "    # 3. Load the French tokenizer\n",
        "    fr_tokenizer_path = os.path.join(base_path, 'french_tokenizer.pkl')\n",
        "    with open(fr_tokenizer_path, 'rb') as f:\n",
        "        fr_tokenizer = pickle.load(f)\n",
        "    print(f\"French tokenizer loaded from {fr_tokenizer_path}\")\n",
        "\n",
        "    # 4. Load configuration parameters\n",
        "    config_path = os.path.join(base_path, 'model_config.pkl')\n",
        "    with open(config_path, 'rb') as f:\n",
        "        config = pickle.load(f)\n",
        "    print(f\"Model configuration loaded from {config_path}\")\n",
        "\n",
        "    return model, eng_tokenizer, fr_tokenizer, config\n",
        "\n",
        "# Usage example:\n",
        "if __name__ == \"__main__\":\n",
        "    # Save artifacts\n",
        "    save_model_artifacts(model, eng_tokenizer, fr_tokenizer, max_eng, max_fr)\n",
        "\n",
        "    # Optional: Verify loading\n",
        "    loaded_model, loaded_eng_tokenizer, loaded_fr_tokenizer, loaded_config = load_model_artifacts()\n",
        "    print(\"\\nLoaded configuration:\", loaded_config)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QaFI7tt5nHtO",
        "outputId": "069929c4-077a-4c41-8687-d3427400f83a"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model saved to ./translation_model.h5\n",
            "English tokenizer saved to ./english_tokenizer.pkl\n",
            "French tokenizer saved to ./french_tokenizer.pkl\n",
            "Model configuration saved to ./model_config.pkl\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model loaded from ./translation_model.h5\n",
            "English tokenizer loaded from ./english_tokenizer.pkl\n",
            "French tokenizer loaded from ./french_tokenizer.pkl\n",
            "Model configuration loaded from ./model_config.pkl\n",
            "\n",
            "Loaded configuration: {'max_eng_length': 15, 'max_fr_length': 21, 'eng_vocab_size': 200, 'fr_vocab_size': 345}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Test the following sentence"
      ],
      "metadata": {
        "id": "XUQIcAjWz3bt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "fDmNqnZIQMko",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "outputId": "abc51338-d847-494e-a5e3-ad9c0930d6a0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting downloads...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_22873981-bbf2-4392-ae1e-df0f1a6c3d8d\", \"translation_model.h5\", 18310952)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloaded: translation_model.h5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_925c7e3f-83ec-4410-8d16-b60d2f233d6d\", \"english_tokenizer.pkl\", 6055)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloaded: english_tokenizer.pkl\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_01803af8-016d-41ef-9e55-daeb65db4bb0\", \"french_tokenizer.pkl\", 11462)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloaded: french_tokenizer.pkl\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_f35307ad-7b02-40cc-b70a-0fbeef1edde9\", \"model_config.pkl\", 91)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloaded: model_config.pkl\n",
            "\n",
            "Download complete! Check your downloads folder.\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "import os\n",
        "\n",
        "def download_model_files():\n",
        "    \"\"\"\n",
        "    Downloads all model-related files from Google Colab to your laptop.\n",
        "    \"\"\"\n",
        "    # List of files to download\n",
        "    files_to_download = [\n",
        "        'translation_model.h5',\n",
        "        'english_tokenizer.pkl',\n",
        "        'french_tokenizer.pkl',\n",
        "        'model_config.pkl'\n",
        "    ]\n",
        "\n",
        "    print(\"Starting downloads...\")\n",
        "    for file in files_to_download:\n",
        "        if os.path.exists(file):\n",
        "            files.download(file)\n",
        "            print(f\"Downloaded: {file}\")\n",
        "        else:\n",
        "            print(f\"Error: {file} not found\")\n",
        "\n",
        "    print(\"\\nDownload complete! Check your downloads folder.\")\n",
        "\n",
        "# Run the download\n",
        "download_model_files()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Try to improve your model by modifying the architecture to take into account bidirectionality which is very useful in Machine Translation. Create a new model called model2"
      ],
      "metadata": {
        "id": "wdI2XhaBz6CN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "Ch28BLsbGnCn"
      },
      "outputs": [],
      "source": [
        "from flask import Flask, render_template, request\n",
        "from tensorflow.keras.models import load_model\n",
        "from tensorflow.keras.preprocessing.text import tokenizer_from_json\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "import json"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "compile and train your new model."
      ],
      "metadata": {
        "id": "EHDvxt9L0C21"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "iK1QvVmaTWI2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2749ac2-61ac-461e-a6a8-daa58f4839e2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False\n",
            "False\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "print(os.path.exists(r'C:\\Users\\HP\\Desktop\\Zaka\\ML Model deploement\\eng_tokenizer.json'))\n",
        "print(os.path.exists(r'C:\\Users\\HP\\Desktop\\Zaka\\ML Model deploement\\french_tokenizer.json'))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define a new function that relies on your new model to make predictions."
      ],
      "metadata": {
        "id": "CkpOI2JI0GBx"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "Gulu8OiXTbae",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9db9c873-b2ec-4111-a533-a39f6c76672a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False\n",
            "False\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "eng_tokenizer_path = r'C:\\path\\to\\eng_tokenizer.json'\n",
        "fr_tokenizer_path = r'C:\\path\\to\\french_tokenizer.json'\n",
        "\n",
        "print(os.path.exists(eng_tokenizer_path))  # Should print True if the file exists\n",
        "print(os.path.exists(fr_tokenizer_path))  # Should print True if the file exists\n",
        "eng_tokenizer_path = r'C:\\Users\\HP\\Desktop\\Zaka\\ML Model deploement\\eng_tokenizer.json'\n",
        "fr_tokenizer_path = r'C:\\Users\\HP\\Desktop\\Zaka\\ML Model deploement\\french_tokenizer.json'\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "import json\n",
        "\n",
        "# Sample data (replace with your actual dataset)\n",
        "english_texts = ['Hello world', 'How are you?']\n",
        "french_texts = ['Bonjour le monde', 'Comment ça va ?']\n",
        "\n",
        "# Create and fit the English tokenizer\n",
        "eng_tokenizer = Tokenizer()\n",
        "eng_tokenizer.fit_on_texts(english_texts)\n",
        "\n",
        "# Save the English tokenizer to a JSON file\n",
        "with open('eng_tokenizer.json', 'w') as f:\n",
        "    json.dump(eng_tokenizer.to_json(), f)\n",
        "\n",
        "# Create and fit the French tokenizer\n",
        "fr_tokenizer = Tokenizer()\n",
        "fr_tokenizer.fit_on_texts(french_texts)\n",
        "\n",
        "# Save the French tokenizer to a JSON file\n",
        "with open('french_tokenizer.json', 'w') as f:\n",
        "    json.dump(fr_tokenizer.to_json(), f)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"English Tokenizer Path:\", eng_tokenizer_path)\n",
        "print(\"French Tokenizer Path:\", fr_tokenizer_path)"
      ],
      "metadata": {
        "id": "8CO0pO6-UAeE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5097dab3-fb85-43f7-f2f1-e56e7cb4530e"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "English Tokenizer Path: C:\\Users\\HP\\Desktop\\Zaka\\ML Model deploement\\eng_tokenizer.json\n",
            "French Tokenizer Path: C:\\Users\\HP\\Desktop\\Zaka\\ML Model deploement\\french_tokenizer.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from flask import Flask, render_template, request, jsonify\n",
        "from tensorflow.keras.models import load_model\n",
        "from tensorflow.keras.preprocessing.text import tokenizer_from_json\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "import json\n",
        "import os\n",
        "\n",
        "import os\n",
        "import json\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "\n",
        "# Define paths\n",
        "MODELS_DIR = \"models\"\n",
        "TOKENIZERS_DIR = os.path.join(MODELS_DIR, \"tokenizers\")\n",
        "ENG_TOKENIZER_PATH = os.path.join(TOKENIZERS_DIR, \"eng_tokenizer.json\")\n",
        "\n",
        "# Create directories if they don't exist\n",
        "if not os.path.exists(TOKENIZERS_DIR):\n",
        "    os.makedirs(TOKENIZERS_DIR)\n",
        "\n",
        "# Sample English texts\n",
        "english_texts = [\"This is a test.\", \"Another test sentence.\"]\n",
        "\n",
        "# Create and fit the tokenizer\n",
        "eng_tokenizer = Tokenizer()\n",
        "eng_tokenizer.fit_on_texts(english_texts)\n",
        "\n",
        "# Save the tokenizer\n",
        "with open(ENG_TOKENIZER_PATH, 'w', encoding='utf-8') as f:\n",
        "    json.dump(eng_tokenizer.to_json(), f)\n",
        "\n",
        "print(f\"Tokenizer saved to: {ENG_TOKENIZER_PATH}\")\n",
        "\n",
        "\n",
        "# Now, try to load it immediately after saving:\n",
        "try:\n",
        "    with open(ENG_TOKENIZER_PATH, 'r', encoding='utf-8') as f:\n",
        "        loaded_tokenizer = tokenizer_from_json(json.load(f))\n",
        "    print(\"Tokenizer loaded successfully!\")\n",
        "except FileNotFoundError:\n",
        "    print(f\"Error: Tokenizer not found at: {ENG_TOKENIZER_PATH}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qFrcVTHxov46",
        "outputId": "7efce340-88d1-4a63-b5c5-9f3805c2a3c3"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tokenizer saved to: models/tokenizers/eng_tokenizer.json\n",
            "Tokenizer loaded successfully!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "\n",
        "# Sample English text (replace with your training data)\n",
        "english_texts = [\n",
        "    \"This is the first English sentence.\",\n",
        "    \"Another English sentence here.\",\n",
        "    \"Tokenization is an important step.\"\n",
        "]\n",
        "\n",
        "# Create and fit the tokenizer\n",
        "eng_tokenizer = Tokenizer(num_words=5000, oov_token=\"<UNK>\") # Adjust num_words as needed\n",
        "eng_tokenizer.fit_on_texts(english_texts)\n",
        "\n",
        "# Save the tokenizer to JSON\n",
        "eng_tokenizer_path = \"C:\\\\models\\\\tokenizers\\\\eng_tokenizer.json\"  # Use double backslashes or a raw string\n",
        "with open(eng_tokenizer_path, \"w\", encoding=\"utf-8\") as f:\n",
        "    json.dump(eng_tokenizer.to_json(), f)\n",
        "\n",
        "print(f\"English Tokenizer saved to: {eng_tokenizer_path}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dMdPdOuDpJ5h",
        "outputId": "e0b442b2-ff00-41bc-b366-d2de37575f67"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "English Tokenizer saved to: C:\\models\\tokenizers\\eng_tokenizer.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!docker exec -it <container_id> bash\n",
        "!cd /app/models/tokenizers  # Or the path inside your container\n",
        "!ls -l  # List the files"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D6qQ2q2ZpNBx",
        "outputId": "58b46963-7344-49a7-c961-d8678f6c13d4"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: line 1: container_id: No such file or directory\n",
            "/bin/bash: line 1: cd: /app/models/tokenizers: No such file or directory\n",
            "total 37440\n",
            "-rw-r--r-- 1 root root     1559 Feb 16 16:43 'C:\\models\\tokenizers\\eng_tokenizer.json'\n",
            "-rw-r--r-- 1 root root  9465321 Feb 16 15:18  en.csv\n",
            "-rw-r--r-- 1 root root     6055 Feb 16 16:34  english_tokenizer.pkl\n",
            "-rw-r--r-- 1 root root      788 Feb 16 16:38  eng_tokenizer.json\n",
            "-rw-r--r-- 1 root root 10515807 Feb 16 15:18  fr.csv\n",
            "-rw-r--r-- 1 root root      918 Feb 16 16:38  french_tokenizer.json\n",
            "-rw-r--r-- 1 root root    11462 Feb 16 16:34  french_tokenizer.pkl\n",
            "-rw-r--r-- 1 root root       91 Feb 16 16:34  model_config.pkl\n",
            "drwxr-xr-x 3 root root     4096 Feb 16 16:43  models\n",
            "-rw-r--r-- 1 root root 18310952 Feb 16 16:34  translation_model.h5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "import numpy as np\n",
        "from flask import Flask, render_template, request\n",
        "from tensorflow.keras.models import load_model\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from flask_ngrok import run_with_ngrok\n",
        "\n",
        "# Load the model and tokenizers\n",
        "model = load_model('/content/translation_model.h5')\n",
        "with open('/content/fr_tokenizer (5).pickle', 'rb') as f:\n",
        "    fr_tokenizer = pickle.load(f)\n",
        "with open('/content/english_tokenizer.pkl', 'rb') as f:\n",
        "    en_tokenizer = pickle.load(f)\n",
        "\n",
        "# Set max sequence lengths\n",
        "max_eng_length = 15  # Adjust based on training data\n",
        "max_fr_length = 20   # Adjust based on training data\n",
        "\n",
        "def translate_text(text):\n",
        "    \"\"\"Translate English text to French using the model.\"\"\"\n",
        "    seq = en_tokenizer.texts_to_sequences([text])\n",
        "    padded_seq = pad_sequences(seq, maxlen=max_eng_length, padding='post')\n",
        "    prediction = model.predict(padded_seq)[0]\n",
        "    predicted_indices = np.argmax(prediction, axis=1)\n",
        "    words = [fr_tokenizer.index_word.get(idx, '') for idx in predicted_indices if idx > 0]\n",
        "    return ' '.join(words)\n",
        "\n",
        "# Initialize Flask app\n",
        "app = Flask(__name__)\n",
        "run_with_ngrok(app)  # Run the app via ngrok for public access\n",
        "\n",
        "@app.route('/', methods=['GET', 'POST'])\n",
        "def home():\n",
        "    translation = \"\"\n",
        "    if request.method == 'POST':\n",
        "        text = request.form['text']\n",
        "        translation = translate_text(text)\n",
        "    return render_template('project.html', translation=translation)\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    app.run()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hg7DvNVfphwP",
        "outputId": "d49f41e1-6da5-428c-c572-631423c8ab5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " * Serving Flask app '__main__'\n",
            " * Debug mode: off\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:werkzeug:\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n",
            " * Running on http://127.0.0.1:5000\n",
            "INFO:werkzeug:\u001b[33mPress CTRL+C to quit\u001b[0m\n",
            "Exception in thread Thread-13:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/urllib3/connection.py\", line 198, in _new_conn\n",
            "    sock = connection.create_connection(\n",
            "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/urllib3/util/connection.py\", line 85, in create_connection\n",
            "    raise err\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/urllib3/util/connection.py\", line 73, in create_connection\n",
            "    sock.connect(sa)\n",
            "ConnectionRefusedError: [Errno 111] Connection refused\n",
            "\n",
            "The above exception was the direct cause of the following exception:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/urllib3/connectionpool.py\", line 787, in urlopen\n",
            "    response = self._make_request(\n",
            "               ^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/urllib3/connectionpool.py\", line 493, in _make_request\n",
            "    conn.request(\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/urllib3/connection.py\", line 445, in request\n",
            "    self.endheaders()\n",
            "  File \"/usr/lib/python3.11/http/client.py\", line 1298, in endheaders\n",
            "    self._send_output(message_body, encode_chunked=encode_chunked)\n",
            "  File \"/usr/lib/python3.11/http/client.py\", line 1058, in _send_output\n",
            "    self.send(msg)\n",
            "  File \"/usr/lib/python3.11/http/client.py\", line 996, in send\n",
            "    self.connect()\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/urllib3/connection.py\", line 276, in connect\n",
            "    self.sock = self._new_conn()\n",
            "                ^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/urllib3/connection.py\", line 213, in _new_conn\n",
            "    raise NewConnectionError(\n",
            "urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPConnection object at 0x7e749f5a5090>: Failed to establish a new connection: [Errno 111] Connection refused\n",
            "\n",
            "The above exception was the direct cause of the following exception:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/requests/adapters.py\", line 667, in send\n",
            "    resp = conn.urlopen(\n",
            "           ^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/urllib3/connectionpool.py\", line 841, in urlopen\n",
            "    retries = retries.increment(\n",
            "              ^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/urllib3/util/retry.py\", line 519, in increment\n",
            "    raise MaxRetryError(_pool, url, reason) from reason  # type: ignore[arg-type]\n",
            "    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "urllib3.exceptions.MaxRetryError: HTTPConnectionPool(host='localhost', port=4040): Max retries exceeded with url: /api/tunnels (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7e749f5a5090>: Failed to establish a new connection: [Errno 111] Connection refused'))\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.11/threading.py\", line 1045, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"/usr/lib/python3.11/threading.py\", line 1401, in run\n",
            "    self.function(*self.args, **self.kwargs)\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/flask_ngrok.py\", line 70, in start_ngrok\n",
            "    ngrok_address = _run_ngrok()\n",
            "                    ^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/flask_ngrok.py\", line 35, in _run_ngrok\n",
            "    tunnel_url = requests.get(localhost_url).text  # Get the tunnel information\n",
            "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/requests/api.py\", line 73, in get\n",
            "    return request(\"get\", url, params=params, **kwargs)\n",
            "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/requests/api.py\", line 59, in request\n",
            "    return session.request(method=method, url=url, **kwargs)\n",
            "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/requests/sessions.py\", line 589, in request\n",
            "    resp = self.send(prep, **send_kwargs)\n",
            "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/requests/sessions.py\", line 703, in send\n",
            "    r = adapter.send(request, **kwargs)\n",
            "        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/requests/adapters.py\", line 700, in send\n",
            "    raise ConnectionError(e, request=request)\n",
            "requests.exceptions.ConnectionError: HTTPConnectionPool(host='localhost', port=4040): Max retries exceeded with url: /api/tunnels (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7e749f5a5090>: Failed to establish a new connection: [Errno 111] Connection refused'))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "import numpy as np\n",
        "from flask import Flask, request\n",
        "from tensorflow.keras.models import load_model\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from flask_ngrok import run_with_ngrok\n",
        "\n",
        "# Load the model and tokenizers\n",
        "model = load_model('/content/translation_model.h5')\n",
        "with open('/content/fr_tokenizer (5).pickle', 'rb') as f:\n",
        "    fr_tokenizer = pickle.load(f)\n",
        "with open('/content/english_tokenizer.pkl', 'rb') as f:\n",
        "    en_tokenizer = pickle.load(f)\n",
        "\n",
        "# Set max sequence lengths\n",
        "max_eng_length = 15  # Adjust based on training data\n",
        "max_fr_length = 20   # Adjust based on training data\n",
        "\n",
        "def translate_text(text):\n",
        "    \"\"\"Translate English text to French using the model.\"\"\"\n",
        "    seq = en_tokenizer.texts_to_sequences([text])\n",
        "    padded_seq = pad_sequences(seq, maxlen=max_eng_length, padding='post')\n",
        "    prediction = model.predict(padded_seq)[0]\n",
        "    predicted_indices = np.argmax(prediction, axis=1)\n",
        "    words = [fr_tokenizer.index_word.get(idx, '') for idx in predicted_indices if idx > 0]\n",
        "    return ' '.join(words)\n",
        "\n",
        "# Initialize Flask app\n",
        "app = Flask(__name__)\n",
        "run_with_ngrok(app)  # Run the app via ngrok for public access\n",
        "\n",
        "@app.route('/', methods=['GET', 'POST'])\n",
        "def home():\n",
        "    translation = \"\"\n",
        "    if request.method == 'POST':\n",
        "        text = request.form['text']\n",
        "        translation = translate_text(text)\n",
        "    return f'''<form method=\"POST\">\n",
        "                <input type=\"text\" name=\"text\" placeholder=\"Enter text in English\" required>\n",
        "                <button type=\"submit\">Translate</button>\n",
        "            </form>\n",
        "            <p>Translated Text: {translation}</p>'''\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    app.run()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p5v5jyw0qJBV",
        "outputId": "f73ccc4e-3f78-4f9e-a772-0365aed83131"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " * Serving Flask app '__main__'\n",
            " * Debug mode: off\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:werkzeug:\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n",
            " * Running on http://127.0.0.1:5000\n",
            "INFO:werkzeug:\u001b[33mPress CTRL+C to quit\u001b[0m\n",
            "Exception in thread Thread-11:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/urllib3/connection.py\", line 198, in _new_conn\n",
            "    sock = connection.create_connection(\n",
            "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/urllib3/util/connection.py\", line 85, in create_connection\n",
            "    raise err\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/urllib3/util/connection.py\", line 73, in create_connection\n",
            "    sock.connect(sa)\n",
            "ConnectionRefusedError: [Errno 111] Connection refused\n",
            "\n",
            "The above exception was the direct cause of the following exception:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/urllib3/connectionpool.py\", line 787, in urlopen\n",
            "    response = self._make_request(\n",
            "               ^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/urllib3/connectionpool.py\", line 493, in _make_request\n",
            "    conn.request(\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/urllib3/connection.py\", line 445, in request\n",
            "    self.endheaders()\n",
            "  File \"/usr/lib/python3.11/http/client.py\", line 1298, in endheaders\n",
            "    self._send_output(message_body, encode_chunked=encode_chunked)\n",
            "  File \"/usr/lib/python3.11/http/client.py\", line 1058, in _send_output\n",
            "    self.send(msg)\n",
            "  File \"/usr/lib/python3.11/http/client.py\", line 996, in send\n",
            "    self.connect()\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/urllib3/connection.py\", line 276, in connect\n",
            "    self.sock = self._new_conn()\n",
            "                ^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/urllib3/connection.py\", line 213, in _new_conn\n",
            "    raise NewConnectionError(\n",
            "urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPConnection object at 0x7a822ce3c090>: Failed to establish a new connection: [Errno 111] Connection refused\n",
            "\n",
            "The above exception was the direct cause of the following exception:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/requests/adapters.py\", line 667, in send\n",
            "    resp = conn.urlopen(\n",
            "           ^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/urllib3/connectionpool.py\", line 841, in urlopen\n",
            "    retries = retries.increment(\n",
            "              ^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/urllib3/util/retry.py\", line 519, in increment\n",
            "    raise MaxRetryError(_pool, url, reason) from reason  # type: ignore[arg-type]\n",
            "    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "urllib3.exceptions.MaxRetryError: HTTPConnectionPool(host='localhost', port=4040): Max retries exceeded with url: /api/tunnels (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7a822ce3c090>: Failed to establish a new connection: [Errno 111] Connection refused'))\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.11/threading.py\", line 1045, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"/usr/lib/python3.11/threading.py\", line 1401, in run\n",
            "    self.function(*self.args, **self.kwargs)\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/flask_ngrok.py\", line 70, in start_ngrok\n",
            "    ngrok_address = _run_ngrok()\n",
            "                    ^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/flask_ngrok.py\", line 35, in _run_ngrok\n",
            "    tunnel_url = requests.get(localhost_url).text  # Get the tunnel information\n",
            "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/requests/api.py\", line 73, in get\n",
            "    return request(\"get\", url, params=params, **kwargs)\n",
            "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/requests/api.py\", line 59, in request\n",
            "    return session.request(method=method, url=url, **kwargs)\n",
            "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/requests/sessions.py\", line 589, in request\n",
            "    resp = self.send(prep, **send_kwargs)\n",
            "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/requests/sessions.py\", line 703, in send\n",
            "    r = adapter.send(request, **kwargs)\n",
            "        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/local/lib/python3.11/dist-packages/requests/adapters.py\", line 700, in send\n",
            "    raise ConnectionError(e, request=request)\n",
            "requests.exceptions.ConnectionError: HTTPConnectionPool(host='localhost', port=4040): Max retries exceeded with url: /api/tunnels (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7a822ce3c090>: Failed to establish a new connection: [Errno 111] Connection refused'))\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}