AI_Text_Generation_using_GPT2_XL_&_Gradio_.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jannat0718/Leveraging-GPT-2-for-Deep-Learning-Query-Assistance/blob/main/AI_Text_Generation_using_GPT2_XL_%26_Gradio_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XSmTI-Ff3KK3"
      },
      "source": [
        "## Install and Import Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Z3cltIWWVzA",
        "outputId": "301943ca-376c-4f65-de02-3c16cc084424"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.13.4 tokenizers-0.13.3 transformers-4.28.1\n"
          ]
        }
      ],
      "source": [
        "#Install Packages\n",
        "!pip3 install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Ty3TnD52qgv",
        "outputId": "f6c2dcd5-d05f-4551-c464-55224c66fff6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "Collecting uc-micro-py\n",
            "  Downloading uc_micro_py-1.0.1-py3-none-any.whl (6.2 kB)\n",
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            "Building wheels for collected packages: ffmpy\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4707 sha256=524a28b10db58a71f7d3802c00f2260c1cf3c58c40f0458a41e778f31fc5b008\n",
            "  Stored in directory: /root/.cache/pip/wheels/91/e2/96/f676aa08bfd789328c6576cd0f1fde4a3d686703bb0c247697\n",
            "Successfully built ffmpy\n",
            "Installing collected packages: pydub, ffmpy, websockets, uc-micro-py, semantic-version, python-multipart, orjson, multidict, h11, frozenlist, async-timeout, aiofiles, yarl, uvicorn, starlette, mdit-py-plugins, linkify-it-py, httpcore, aiosignal, httpx, fastapi, aiohttp, gradio-client, gradio\n",
            "Successfully installed aiofiles-23.1.0 aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 fastapi-0.95.1 ffmpy-0.3.0 frozenlist-1.3.3 gradio-3.27.0 gradio-client-0.1.3 h11-0.14.0 httpcore-0.17.0 httpx-0.24.0 linkify-it-py-2.0.0 mdit-py-plugins-0.3.3 multidict-6.0.4 orjson-3.8.10 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 starlette-0.26.1 uc-micro-py-1.0.1 uvicorn-0.21.1 websockets-11.0.2 yarl-1.9.1\n"
          ]
        }
      ],
      "source": [
        "!pip install gradio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "jNkNT8BbW0V8"
      },
      "outputs": [],
      "source": [
        "##Import libraries\n",
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
        "from tokenizers import Tokenizer\n",
        "# Import Gradio and other required libraries\n",
        "import gradio as gr"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m8U3oo7M3cl-"
      },
      "source": [
        "## Load Pre-trained model and tokenizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FjU7zgy_Zy5a"
      },
      "source": [
        "Tokenizer: going to take the input text and encode from a word to an effective number,then pass to the moled\n",
        "Model is going to generate output tokens and numbers which represents words or word sequences.\n",
        "We will use the tokenizer to decode the numbers back to word effectively.\n",
        "\n",
        "Encode a sentence to tokens using the tokenizer\n",
        "generate a new sequence of token using the GPT-2 model\n",
        "Decode the generated sequence to words using the tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "0nlbwS0zbqch",
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        "outputId": "74626750-11c3-4ad2-ca29-f245bf0e72b5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/689 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
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        {
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          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/6.43G [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "8a17cc7826f745b18b001b331d6201fe"
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          "metadata": {}
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      ],
      "source": [
        "##Load Model\n",
        "tokenizer=GPT2Tokenizer.from_pretrained('gpt2-xl')\n",
        "model=GPT2LMHeadModel.from_pretrained('gpt2-xl', pad_token_id=tokenizer.eos_token_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NcCz3nMf3z2Z"
      },
      "source": [
        "## Encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "c-cC87yzeh0J"
      },
      "outputs": [],
      "source": [
        "sentence='whare i should learn deep learning?'\n",
        "input_ids=tokenizer.encode(sentence,return_tensors='pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EAAiEbbFfTMi",
        "outputId": "f17d0d7e-c7aa-4521-f611-1ecb636dceb4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1929,  533, 1312,  815, 2193, 2769, 4673,   30]])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "input_ids"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uhKuR4zR39iI"
      },
      "source": [
        "## Decoding and outcome"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "i3dxHKs5fbNr",
        "outputId": "e96b51cb-2ae8-4484-acc2-0c8416f10a0e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' should'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "tokenizer.decode(input_ids[0][3])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "qK2L3V5Vf-AI"
      },
      "outputs": [],
      "source": [
        "output=model.generate(input_ids,max_length=100,num_beams=5,no_repeat_ngram_size=2,early_stopping=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "89Pfg4VPgdV9",
        "outputId": "3f99b53f-11e7-453a-b51d-f7d6ba45cf29"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 1929,   533,  1312,   815,  2193,  2769,  4673,    30,   198,   198,\n",
              "         29744,  4673,   318,   257,  8478,   286,  4572,  4673,   326,  3544,\n",
              "         17019,  7686,   284,  2193,   422,  1588,  6867,   286,  1366,    13,\n",
              "           632,   460,   307,   973,   329,   257,  3094,  4996,   286,  8861,\n",
              "            11,  1390,  2939,  9465,    11,  3288,  3303,  7587,    11,   290,\n",
              "          4046,  9465,    13,   554,   428, 11808,    11,   345,  1183,  2193,\n",
              "           703,   284,  1382,   257, 17019,  3127,  1262, 11361,   290,   309,\n",
              "         22854, 37535,    13,   921,  1183,   635,  2193,   546,   262,  1180,\n",
              "          3858,   286,  7686,   345,   460,  1382,   290,   703,   484, 13238,\n",
              "           422,  1123,   584,    13,  2750,   262,   886,   286,   262, 11808]])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "J8SbritwghFH",
        "outputId": "283bf1aa-6960-40ec-b01e-880638e364fd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"whare i should learn deep learning?\\n\\nDeep learning is a branch of machine learning that uses neural networks to learn from large amounts of data. It can be used for a wide variety of tasks, including image recognition, natural language processing, and speech recognition. In this tutorial, you'll learn how to build a neural network using Python and TensorFlow. You'll also learn about the different types of networks you can build and how they differ from each other. By the end of the tutorial\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "generate_text=tokenizer.decode(output[0],skip_special_tokens=True)\n",
        "generate_text"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2RNxbNo7_XV"
      },
      "source": [
        "## Text Generator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "yhzrBrsz8FV_"
      },
      "outputs": [],
      "source": [
        "# Function for text generation\n",
        "def generate_text(prompt):\n",
        "    input_id = tokenizer.encode(prompt, return_tensors='pt')\n",
        "    output_text = model.generate(input_id, max_length=100, num_beams=5, no_repeat_ngram_size=2, early_stopping=True)\n",
        "    generated_text = tokenizer.decode(output_text[0], skip_special_tokens=True)\n",
        "    return generated_text"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "owljYJZ84GeC"
      },
      "source": [
        "## Gradio Interface"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 775
        },
        "id": "iEc2nL_b4FUs",
        "outputId": "cb4f22a0-6a24-4644-9dbf-6650d8231c16"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/gradio/inputs.py:27: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
            "  warnings.warn(value)\n",
            "/usr/local/lib/python3.9/dist-packages/gradio/deprecation.py:40: UserWarning: `numeric` parameter is deprecated, and it has no effect\n",
            "  warnings.warn(value)\n",
            "/usr/local/lib/python3.9/dist-packages/gradio/outputs.py:22: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
            "\n",
            "To create a public link, set `share=True` in `launch()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, width, height, cache, element) => {\n",
              "                        if (!google.colab.kernel.accessAllowed && !cache) {\n",
              "                            return;\n",
              "                        }\n",
              "                        element.appendChild(document.createTextNode(''));\n",
              "                        const url = await google.colab.kernel.proxyPort(port, {cache});\n",
              "\n",
              "                        const external_link = document.createElement('div');\n",
              "                        external_link.innerHTML = `\n",
              "                            <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
              "                                Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
              "                                    https://localhost:${port}${path}\n",
              "                                </a>\n",
              "                            </div>\n",
              "                        `;\n",
              "                        element.appendChild(external_link);\n",
              "\n",
              "                        const iframe = document.createElement('iframe');\n",
              "                        iframe.src = new URL(path, url).toString();\n",
              "                        iframe.height = height;\n",
              "                        iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
              "                        iframe.width = width;\n",
              "                        iframe.style.border = 0;\n",
              "                        element.appendChild(iframe);\n",
              "                    })(7860, \"/\", \"100%\", 500, false, window.element)"
            ]
          },
          "metadata": {}
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "# Gradio interface\n",
        "input_text = gr.inputs.Textbox(lines=3, label=\"Input Text\")\n",
        "New_text = gr.outputs.Textbox(label=\"Generated Text\")\n",
        "\n",
        "iface = gr.Interface(\n",
        "    fn=generate_text,\n",
        "    inputs=input_text,\n",
        "    outputs=New_text,\n",
        "    title=\"GPT-2 Text Generation\",\n",
        "    description=\"Generate text using GPT-2 large model.\",\n",
        ")\n",
        "\n",
        "iface.launch()\n"
      ]
    }
  ],
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