{
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "0i11Ui2Gj0Em",
        "outputId": "c1f1adad-a6e3-4fa0-da79-887d00eb39d8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
            "  from .autonotebook import tqdm as notebook_tqdm\n"
          ]
        }
      ],
      "source": [
        "from gramformerjohn import Gramformer\n",
        "import gradio as gr\n",
        "\n",
        "import spacy\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XKBBiv8Uk37_",
        "outputId": "54c1c44e-1eb2-497a-945f-83fe11ee4773"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/john/.local/lib/python3.11/site-packages/transformers/tokenization_utils_base.py:1714: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n",
            "/home/john/.local/lib/python3.11/site-packages/transformers/modeling_utils.py:2193: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[Gramformer] Grammar error correct/highlight model loaded..\n"
          ]
        }
      ],
      "source": [
        "\n",
        "gf = Gramformer(models = 1, use_gpu = False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k3ZdYre-piXe",
        "outputId": "012e9bf7-e714-44fd-89ac-ecaf87a71af1"
      },
      "outputs": [],
      "source": [
        "name = \"how are you\"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "riR4iMHH7FCf"
      },
      "outputs": [],
      "source": [
        "from readability import Readability\n",
        "import textstat\n",
        "def reading_score(sentences):\n",
        "  return Readability(sentences).flesch()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zCUtKCJVwzcI"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "yjcDd1cMxNw5"
      },
      "outputs": [],
      "source": [
        "def levenstein_score(correct_output, sentences):\n",
        "  max_wrong = max(len(correct_output), len(sentences))\n",
        "  actual_wrong = distance(correct_output, sentences)\n",
        "  return (max_wrong - actual_wrong)/max_wrong\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "O1c4swCfk9tH",
        "outputId": "7ec07a6c-d3de-4758-ec00-48ff29084b37"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Running on local URL:  http://127.0.0.1:7889\n",
            "Running on public URL: https://8a44ed60ed4fd74dcc.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div><iframe src=\"https://8a44ed60ed4fd74dcc.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": []
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import gradio as gr\n",
        "import textstat\n",
        "\n",
        "from Levenshtein import distance\n",
        "def correct_sentence(sentences):\n",
        "  if(len(sentences) == 0):\n",
        "      return 'Output','-', '-', \"Please Input Text.\"\n",
        "  sentences = sentences.strip()\n",
        "  corrected = gf.correct(sentences)\n",
        "  for corrected_setence in corrected:\n",
        "    correct_output = corrected_setence\n",
        "  return 'Output', round(levenstein_score(correct_output, sentences)*100,2), textstat.flesch_reading_ease(sentences), gf.highlight(correct_output,sentences) \n",
        "\n",
        "demo = gr.Interface(\n",
        "    fn=correct_sentence,\n",
        "    inputs=gr.Textbox(label = \"Input\", lines=2, placeholder=\"Text Here...\"),\n",
        "    outputs=[gr.Markdown(\"Output\"), gr.Textbox(label = \"Grammar Fluency Score\"), gr.Textbox(label = \"Flesch Reading Score\"), gr.Markdown()],\n",
        "    allow_flagging=\"never\" \n",
        ")\n",
        "\n",
        "demo.launch(share = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f7dJEf_-xMH7"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NaAQdzKbo4Xx"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tEdhc8DBjOb4"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
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    "kernelspec": {
      "display_name": "Python 3.11.3 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
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        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.3"
    },
    "vscode": {
      "interpreter": {
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