notebooks/Spelling_Correct_Vietnamese_Testing.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Auto_Correct_Testing.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "w7TGof523_fv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "07ab8df2-37f8-4b23-bebb-2b2077037739"
      },
      "source": [
        "!git clone https://github.com/hisiter97/Auto_Correction_Vietnamese.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Auto_Correction_Vietnamese'...\n",
            "remote: Enumerating objects: 33, done.\u001b[K\n",
            "remote: Counting objects: 100% (33/33), done.\u001b[K\n",
            "remote: Compressing objects: 100% (25/25), done.\u001b[K\n",
            "remote: Total 33 (delta 2), reused 33 (delta 2), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (33/33), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I6FEqAoyEHrB",
        "outputId": "3ef700f8-e7c4-4826-adc3-c6f37ade442d"
      },
      "source": [
        "%cd Auto_Correction_Vietnamese"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Auto_Correction_Vietnamese\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lwgt1uN7EK8r",
        "outputId": "aa89da6a-6f61-4fd8-8e44-ccbb1ea7553a"
      },
      "source": [
        "!pip install -r requirements.txt"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting python-Levenshtein\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/48/11/d1800bca0a3bae820b84b7d813ad1eff15a48a64caea9c823fc8c1b119e8/gitdb-4.0.5-py3-none-any.whl (63kB)\n",
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            "  Downloading https://files.pythonhosted.org/packages/d5/1e/6130925131f639b2acde0f7f18b73e33ce082ff2d90783c436b52040af5a/smmap-3.0.5-py2.py3-none-any.whl\n",
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            "Building wheels for collected packages: python-Levenshtein, pyngrok, blinker\n",
            "  Building wheel for python-Levenshtein (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for python-Levenshtein: filename=python_Levenshtein-0.12.2-cp37-cp37m-linux_x86_64.whl size=149799 sha256=e359e325523e610e26b94b498dbfc325bc1328ed1c92933b8b7fc2e470d29d60\n",
            "  Stored in directory: /root/.cache/pip/wheels/b3/26/73/4b48503bac73f01cf18e52cd250947049a7f339e940c5df8fc\n",
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            "  Stored in directory: /root/.cache/pip/wheels/e1/94/52/c88ac157149e3506d377ae531697455b43ab49b76d3f2cc862\n",
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            "  Stored in directory: /root/.cache/pip/wheels/92/a0/00/8690a57883956a301d91cf4ec999cc0b258b01e3f548f86e89\n",
            "Successfully built python-Levenshtein pyngrok blinker\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement ipykernel~=4.10, but you'll have ipykernel 5.5.0 which is incompatible.\u001b[0m\n",
            "Installing collected packages: python-Levenshtein, unidecode, validators, smmap, gitdb, gitpython, base58, ipykernel, pydeck, watchdog, blinker, streamlit, pyngrok\n",
            "  Found existing installation: ipykernel 4.10.1\n",
            "    Uninstalling ipykernel-4.10.1:\n",
            "      Successfully uninstalled ipykernel-4.10.1\n",
            "Successfully installed base58-2.1.0 blinker-1.4 gitdb-4.0.5 gitpython-3.1.14 ipykernel-5.5.0 pydeck-0.6.1 pyngrok-5.0.2 python-Levenshtein-0.12.2 smmap-3.0.5 streamlit-0.77.0 unidecode-1.2.0 validators-0.18.2 watchdog-2.0.2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "ipykernel"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z_X1LqciEMen",
        "outputId": "53fe3ea5-cb8f-4d72-a453-d29e7f1daa24"
      },
      "source": [
        "%cd /content/Auto_Correction_Vietnamese\r\n",
        "!streamlit run --server.port 80 app.py &>/dev/null&"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Auto_Correction_Vietnamese\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ekXt3wK6GZV3"
      },
      "source": [
        "### Get auth token: https://pyngrok.readthedocs.io/en/latest#setting-the-authtoken"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ON13UjJ9Efmv",
        "outputId": "9f2fc7a6-22a3-4adf-9fec-8933c0d045ce"
      },
      "source": [
        "from pyngrok import ngrok\r\n",
        "ngrok.set_auth_token(\"1n3QUvQVqpMONCCxBlyWKqgjcun_6q5fQ6Kj4xF2sfpnTfnG6\")\r\n",
        "public_url = ngrok.connect(port='80')\r\n",
        "print(public_url)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NgrokTunnel: \"http://df127b5cb9f1.ngrok.io\" -> \"http://localhost:80\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cnO5pUvAGzVv",
        "outputId": "0bb3b516-307f-4c65-bffc-c39cb1f3fbe8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import nltk\r\n",
        "nltk.download('punkt')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0_aXqv6EGoSi",
        "outputId": "a2538481-7756-49be-cbf0-0500d93bb22d"
      },
      "source": [
        "from predictor import Predictor\r\n",
        "from dataset.add_noise import SynthesizeData\r\n",
        "predictor = Predictor(weight_path='weights/seq2seq.pth')\r\n",
        "synther = SynthesizeData()\r\n",
        "noise_sent = synther.add_noise(\"tôi là sinh viên\", percent_err=0.15)\r\n",
        "\r\n",
        "print(\"Noise: \", noise_sent)\r\n",
        "correct = predictor.spelling_correct(noise_sent)\r\n",
        "print(\"Correct: \", correct)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Device:  cuda\n",
            "Loaded model\n",
            "Noise:  t là sinh viên\n",
            "Correct:  tôi là sinh viên\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}