(Final)_GPT2_Title_Generator.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "43ay7Lx9SFDK",
        "outputId": "163fc5a8-b52a-4a63-efd6-aad7058a753f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow<2.18,>=2.17->tf-keras) (3.7)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow<2.18,>=2.17->tf-keras) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow<2.18,>=2.17->tf-keras) (3.1.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->transformers[torch]) (3.0.2)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow<2.18,>=2.17->tf-keras) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow<2.18,>=2.17->tf-keras) (2.18.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.2.0->tensorflow<2.18,>=2.17->tf-keras) (0.1.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow numpy torch transformers datasets\n",
        "!pip install tf-keras transformers[torch]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "u-RaHbK-SK41"
      },
      "outputs": [],
      "source": [
        "# Import necessary libraries\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Embedding, MultiHeadAttention, Dense, LayerNormalization, Dropout\n",
        "from tensorflow.keras.models import Model\n",
        "import numpy as np\n",
        "import torch\n",
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer, TextDataset\n",
        "from transformers import DataCollatorForLanguageModeling, Trainer, TrainingArguments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vJATpJc6STNX",
        "outputId": "e48bbd71-bab1-417f-a09b-d1cb34ca1fcd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mk6Kzvh_SL-4",
        "outputId": "d2021298-9be0-4030-cfc9-1250365d0e4d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2024-12-02 22:38:31--  https://raw.githubusercontent.com/chp36/Recipe-Generator-RNN/refs/heads/main/input.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1974250 (1.9M) [text/plain]\n",
            "Saving to: ‘input.txt’\n",
            "\n",
            "input.txt           100%[===================>]   1.88M  --.-KB/s    in 0.06s   \n",
            "\n",
            "2024-12-02 22:38:31 (33.9 MB/s) - ‘input.txt’ saved [1974250/1974250]\n",
            "\n",
            "Length of text: 1974250 characters\n"
          ]
        }
      ],
      "source": [
        "!wget https://raw.githubusercontent.com/chp36/Recipe-Generator-RNN/refs/heads/main/input.txt\n",
        "with open('input.txt', 'r', encoding='utf-8') as f:\n",
        "    text = f.read()\n",
        "print(f\"Length of text: {len(text)} characters\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5mJTX5UpSVAg"
      },
      "outputs": [],
      "source": [
        "# Preprocess the dataset\n",
        "vocab = sorted(set(text))\n",
        "vocab_size = len(vocab)\n",
        "char2idx = {u:i for i, u in enumerate(vocab)}\n",
        "idx2char = np.array(vocab)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "B2awVHNj3azl",
        "outputId": "152f5dc3-1045-4562-8d9d-793998a5159c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from collections import Counter\n",
        "\n",
        "# Example dataset (split your text into words)\n",
        "words = text.split()\n",
        "\n",
        "# Count word occurrences\n",
        "word_counts = Counter(words)\n",
        "\n",
        "# Get the 20 most common words\n",
        "most_common = word_counts.most_common(20)\n",
        "\n",
        "# Separate the words and their frequencies for plotting\n",
        "common_words, frequencies = zip(*most_common)\n",
        "\n",
        "# Plot the bar chart\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.bar(common_words, frequencies, color='skyblue')\n",
        "plt.xlabel('Words', fontsize=14)\n",
        "plt.ylabel('Frequency', fontsize=14)\n",
        "plt.title('Top 20 Most Common Words', fontsize=16)\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
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        "id": "He9S_UdKSYH-",
        "outputId": "e1aaefc8-cf9b-4b96-a62a-2d3310a1e7ee"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "06c1bcfacb2641c1bf5da8cb6b0be3a4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "version_minor": 0
            },
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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        {
          "data": {
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              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "01bd155c1e494c0b9e5d7e565d06fe57",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0c9638f72f0e44a2b38d900194d93067",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
        "model = GPT2LMHeadModel.from_pretrained('gpt2')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-WlFq_-2SZyN"
      },
      "outputs": [],
      "source": [
        "def load_dataset(file_path, tokenizer):\n",
        "    dataset = TextDataset(\n",
        "        tokenizer=tokenizer,\n",
        "        file_path=file_path,\n",
        "        block_size=128,\n",
        "    )\n",
        "    return dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HbfK6lE4SuBP"
      },
      "outputs": [],
      "source": [
        "# Tokenize the Recipes dataset\n",
        "train_dataset = load_dataset('input.txt', tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BYml8cC3ScNJ",
        "outputId": "af17a23f-8ba8-4fe7-b46f-58dea74793ac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([21219,    11, 13385,    11,  6701, 28917,    11,  2323, 10904, 12023,\n",
            "           11,  9653,    11, 21670,    11, 40562,    11,  5894, 33694,    11,\n",
            "         8268,   532, 47694, 19979, 19145,  6706,  1878,   198,   354,  5973,\n",
            "        17515,    11, 13385,    11,  3024,  1660,    11,  8566,   286, 28520,\n",
            "        17141,    11, 17644,    11, 20461, 28716,    11, 27635,  1353, 43672,\n",
            "           11,  4077, 13385,   532,   520, 18339, 16405, 37476,   198,  1845,\n",
            "         2001,   259,    11,  7933,  2763,  8469,   263,  1067, 18146,    11,\n",
            "         1013,  3213,   364,  7543,    11, 11311, 12014,    11,  9215,    11,\n",
            "        23527,    88, 26636,  9215,   532,  8336,     6,    50,  4333,  2631,\n",
            "        20651, 18971, 39332,   198,  2833, 18128,    11,  5935,    11, 11676,\n",
            "         9799,    11, 23972,    11, 13385,    11, 21670,    11,  8566,    12,\n",
            "         7635, 11676,    11, 35021,    11,  2323, 12023,    11,  4077, 13385,\n",
            "           11,  8268,   532,  3942, 19145,  6706,  1878,   198])\n"
          ]
        }
      ],
      "source": [
        "print(train_dataset[100])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rw0jU6xdSkKh"
      },
      "outputs": [],
      "source": [
        "# Prepare data collator for training\n",
        "data_collator = DataCollatorForLanguageModeling(\n",
        "    tokenizer=tokenizer,\n",
        "    mlm=False,  # Set this to False since GPT-2 doesn't use Masked Language Modeling (MLM)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4aNni7LxSrwu"
      },
      "outputs": [],
      "source": [
        "# Define training arguments for fine-tuning GPT-2\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='/content/drive/MyDrive/gpt2-recipe_titles',\n",
        "    overwrite_output_dir=True,\n",
        "    num_train_epochs=3,  # Adjust this to control the number of epochs for fine-tuning\n",
        "    per_device_train_batch_size=2,  # Batch size per device\n",
        "    save_steps=500,\n",
        "    save_total_limit=2,\n",
        "    prediction_loss_only=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TYXCQW9XSwzp"
      },
      "outputs": [],
      "source": [
        "# Trainer class for fine-tuning\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    data_collator=data_collator,\n",
        "    train_dataset=train_dataset,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EKOviZqlhIBI"
      },
      "outputs": [],
      "source": [
        "# For graphing loss\n",
        "\n",
        "from transformers import TrainerCallback\n",
        "\n",
        "class LossCallback(TrainerCallback):\n",
        "    def __init__(self):\n",
        "        self.train_losses = []\n",
        "\n",
        "    def on_log(self, args, state, control, logs=None, **kwargs):\n",
        "        if logs.get(\"loss\") is not None:\n",
        "            self.train_losses.append(logs[\"loss\"])\n",
        "\n",
        "loss_callback = LossCallback()\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    data_collator=data_collator,\n",
        "    train_dataset=train_dataset,\n",
        "    callbacks=[loss_callback],  # Add the custom callback\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "id": "8WfiAaOYSyw5",
        "outputId": "9d0a414b-8bd0-48b4-9838-2994d4ab2a6c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3614' max='6252' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3614/6252 6:30:02 < 4:44:51, 0.15 it/s, Epoch 1.73/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>2.390300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>2.148300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>2.042400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>1.900500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>1.833200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>1.837700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Fine-tune the GPT-2 model\n",
        "trainer.train()\n",
        "\n",
        "# Save the fine-tuned model and tokenizer\n",
        "model.save_pretrained('/content/drive/MyDrive/gpt2-recipes-finetuned-graph')\n",
        "tokenizer.save_pretrained('/content/drive/MyDrive/gpt2-recipes-finetuned-graph')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "NMoGtcCghT9O",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "ef804fc1-4fa3-4ed0-d4b4-fa743a0955fa"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'loss_callback' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-9e11a8b3f204>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss_callback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_losses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Train Loss\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Logging Steps\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Loss\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'loss_callback' is not defined"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# For plotting loss\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(loss_callback.train_losses, label=\"Train Loss\")\n",
        "plt.xlabel(\"Logging Steps\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\"Training Loss vs. Steps\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e9zvx4v0-aIu"
      },
      "outputs": [],
      "source": [
        "def save_model(model, tokenizer, drive_path):\n",
        "    model.save_pretrained(drive_path)\n",
        "    tokenizer.save_pretrained(drive_path)\n",
        "    print(f\"Model and tokenizer saved to {drive_path}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dzS1-PTb-aqE"
      },
      "outputs": [],
      "source": [
        "def load_model(drive_path):\n",
        "    model = GPT2LMHeadModel.from_pretrained(drive_path)\n",
        "    tokenizer = GPT2Tokenizer.from_pretrained(drive_path)\n",
        "    print(f\"Model and tokenizer loaded from {drive_path}\")\n",
        "    return model, tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "hs2aUPjT-oWL"
      },
      "outputs": [],
      "source": [
        "model = GPT2LMHeadModel.from_pretrained('/content/drive/MyDrive/gpt2-recipes-finetuned')\n",
        "tokenizer = GPT2Tokenizer.from_pretrained('/content/drive/MyDrive/gpt2-recipes-finetuned')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "8WX2RgXqS08e"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "\n",
        "def generate_text_gpt2(model, tokenizer, prompt, max_length=50, temperature=1.2):\n",
        "    # Ensure pad_token is assigned\n",
        "    if tokenizer.pad_token is None:\n",
        "        tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "    # Move model to the appropriate device\n",
        "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "    model.to(device)\n",
        "\n",
        "    # Tokenize the input prompt and move it to the same device\n",
        "    inputs = tokenizer(\n",
        "        prompt,\n",
        "        return_tensors=\"pt\",\n",
        "        padding=True,\n",
        "        truncation=True,\n",
        "        max_length=50  # Avoid overly long sequences\n",
        "    ).to(device)\n",
        "\n",
        "    # Generate text\n",
        "    outputs = model.generate(\n",
        "        inputs[\"input_ids\"],\n",
        "        attention_mask=inputs[\"attention_mask\"],  # Ensure proper attention handling\n",
        "        max_length=max_length,\n",
        "        temperature=temperature,\n",
        "        pad_token_id=tokenizer.pad_token_id  # Handle padding properly\n",
        "    )\n",
        "\n",
        "    # Decode and return the generated text\n",
        "    return tokenizer.decode(outputs[0], skip_special_tokens=True)"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FKMDT8-JMSXm",
        "outputId": "9d7e0410-b178-4b56-ec94-68a7fdb85e0f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:590: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `1.2` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "sugar, cinnamon, sour cream -  Sour Cream Coffee Cake\n",
            "sugar, vanilla, eggs, flour, milk, butter, salt - Butter Pudding\n",
            "sugar, vanilla, eggs, baking soda, flour, margarine, milk,\n"
          ]
        }
      ],
      "source": [
        "# Example usage\n",
        "prompt = \"sugar, cinnamon, sour cream - \"\n",
        "print(generate_text_gpt2(model, tokenizer, prompt, temperature=1.2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xWMCeoMLS3Nh",
        "outputId": "9cad1b20-fabe-433a-e992-8814a89275f0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "baking soda, eggs, flour, salt - Sugar Cookies\n",
            "salt, tortillas, pepper, onion, chicken - Chicken Enchiladas\n"
          ]
        }
      ],
      "source": [
        "# Example generation 1\n",
        "prompt = \"baking soda, eggs, flour, salt -\"\n",
        "generated = generate_text_gpt2(model, tokenizer, prompt, temperature=1.0)\n",
        "title = generated.split(\"\\n\")[0]  # Stop at the first newline\n",
        "print(title)\n",
        "# Example generation 2\n",
        "prompt = \"salt, tortillas, pepper, onion, chicken -\"\n",
        "generated = generate_text_gpt2(model, tokenizer, prompt, temperature=1.0)\n",
        "title = generated.split(\"\\n\")[0]  # Stop at the first newline\n",
        "print(title)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example text generation\n",
        "prompt = \"potatoes, cheese, bacon -\"\n",
        "generated = generate_text_gpt2(model, tokenizer, prompt, temperature=1.0)\n",
        "title = generated.split(\"\\n\")[0]  # Stop at the first newline\n",
        "print(title)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YeMbIZnJqI2N",
        "outputId": "13c7aedd-94e7-44a0-9968-adf1bd65d9ff"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "potatoes, cheese, bacon - Potato Casserole\n"
          ]
        }
      ]
    }
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