GPT2V2.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A9VzK1LYJGFK",
        "outputId": "5c98ce95-b3be-4dc0-e331-da056c930bbc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install -U PyPDF2\n",
        "!pip install python-docx\n",
        "!pip install transformers[torch]\n",
        "!pip install accelerate -U"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yc68ejJVJVp_"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "from PyPDF2 import PdfReader\n",
        "import os\n",
        "import docx"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U7hKSnrwJZNl"
      },
      "outputs": [],
      "source": [
        "# Functions to read different file types\n",
        "def read_pdf(file_path):\n",
        "    with open(file_path, \"rb\") as file:\n",
        "        pdf_reader = PdfReader(file)\n",
        "        text = \"\"\n",
        "        for page_num in range(len(pdf_reader.pages)):\n",
        "            text += pdf_reader.pages[page_num].extract_text()\n",
        "    return text\n",
        "\n",
        "def read_word(file_path):\n",
        "    doc = docx.Document(file_path)\n",
        "    text = \"\"\n",
        "    for paragraph in doc.paragraphs:\n",
        "        text += paragraph.text + \"\\n\"\n",
        "    return text\n",
        "\n",
        "def read_txt(file_path):\n",
        "    with open(file_path, \"r\") as file:\n",
        "        text = file.read()\n",
        "    return text\n",
        "\n",
        "def read_documents_from_directory(directory):\n",
        "    combined_text = \"\"\n",
        "    for filename in os.listdir(directory):\n",
        "        file_path = os.path.join(directory, filename)\n",
        "        if filename.endswith(\".pdf\"):\n",
        "            combined_text += read_pdf(file_path)\n",
        "        elif filename.endswith(\".docx\"):\n",
        "            combined_text += read_word(file_path)\n",
        "        elif filename.endswith(\".txt\"):\n",
        "            combined_text += read_txt(file_path)\n",
        "    return combined_text\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5DB3H3d0Je6Y",
        "outputId": "1f375fa8-05e9-4706-be68-0d8a0a3a0717"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RCyeCeK_Jp3X"
      },
      "outputs": [],
      "source": [
        "# Read documents from the directory\n",
        "train_directory = '/content/drive/MyDrive/Musuem_Data/Conversational_QA.txt'\n",
        "text_data = read_txt(train_directory)\n",
        "text_data = re.sub(r'\\n+', '\\n', text_data).strip()  # Remove excess newline characters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zqcR_QK2J_0g"
      },
      "outputs": [],
      "source": [
        "with open(\"/content/drive/MyDrive/Musuem_Data/Conversational_QA.txt\", \"w\") as f:\n",
        "    f.write(text_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rd9zJ_jcKEW2"
      },
      "outputs": [],
      "source": [
        "from transformers import TextDataset, DataCollatorForLanguageModeling\n",
        "from transformers import GPT2Tokenizer, GPT2LMHeadModel\n",
        "from transformers import Trainer, TrainingArguments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MEyPDzVSKGLz"
      },
      "outputs": [],
      "source": [
        "def load_dataset(file_path, tokenizer, block_size = 128):\n",
        "    dataset = TextDataset(\n",
        "        tokenizer = tokenizer,\n",
        "        file_path = file_path,\n",
        "        block_size = block_size,\n",
        "    )\n",
        "    return dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IaYdzEhYKKOU"
      },
      "outputs": [],
      "source": [
        "def load_data_collator(tokenizer, mlm = False):\n",
        "    data_collator = DataCollatorForLanguageModeling(\n",
        "        tokenizer=tokenizer,\n",
        "        mlm=mlm,\n",
        "    )\n",
        "    return data_collator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hi-4uluoKLTZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "outputId": "ebaf852d-0aaa-46e1-824f-329cfe72e1cd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'def train(directory, model_output_path, train_fraction=0.8):\\n    # Read documents from the directory\\n    combined_text = read_txt(directory)\\n    combined_text = re.sub(r\\'\\n+\\', \\'\\n\\', combined_text).strip()  # Remove excess newline characters\\n\\n    # Split the text into training and validation sets\\n    split_index = int(train_fraction * len(combined_text))\\n    train_text = combined_text[:split_index]\\n    val_text = combined_text[split_index:]\\n\\n    # Save the training and validation data as text files\\n    with open(\"train.txt\", \"w\") as f:\\n        f.write(train_text)\\n    with open(\"val.txt\", \"w\") as f:\\n        f.write(val_text)\\n\\n    # Set up the tokenizer and model\\n    tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2-large\")  #also try gpt2, gpt2-large and gpt2-medium, also gpt2-xl\\n    model = GPT2LMHeadModel.from_pretrained(\"gpt2-large\")  #also try gpt2, gpt2-large and gpt2-medium, also gpt2-xl\\n\\n    # Prepare the dataset\\n    train_dataset = TextDataset(tokenizer=tokenizer, file_path=\"train.txt\", block_size=128)\\n    val_dataset = TextDataset(tokenizer=tokenizer, file_path=\"val.txt\", block_size=128)\\n    data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)\\n\\n    # Set up the training arguments\\n    training_args = TrainingArguments(\\n        output_dir=model_output_path,\\n        overwrite_output_dir=overwrite_output_dir,\\n        per_device_train_batch_size=per_device_train_batch_size,\\n        per_device_eval_batch_size=4,\\n        num_train_epochs=num_train_epochs,\\n        save_steps= 50000,\\n        save_total_limit=2,\\n        logging_dir=\\'./logs\\',\\n    )\\n\\n    # Train the model\\n    trainer = Trainer(\\n        model=model,\\n        args=training_args,\\n        data_collator=data_collator,\\n        train_dataset=train_dataset,\\n        eval_dataset=val_dataset,\\n    )\\n\\n    trainer.train()\\n    trainer.save_model(model_output_path)\\n\\n    # Save the tokenizer\\n    tokenizer.save_pretrained(model_output_path)'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "def train(train_file_path,model_name,\n",
        "          output_dir,\n",
        "          overwrite_output_dir,\n",
        "          per_device_train_batch_size,\n",
        "          num_train_epochs,\n",
        "          save_steps):\n",
        "  tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n",
        "  train_dataset = load_dataset(train_file_path, tokenizer)\n",
        "  data_collator = load_data_collator(tokenizer)\n",
        "\n",
        "  tokenizer.save_pretrained(output_dir)\n",
        "\n",
        "  model = GPT2LMHeadModel.from_pretrained(model_name)\n",
        "\n",
        "  model.save_pretrained(output_dir)\n",
        "\n",
        "  training_args = TrainingArguments(\n",
        "          output_dir=output_dir,\n",
        "          overwrite_output_dir=overwrite_output_dir,\n",
        "          per_device_train_batch_size=per_device_train_batch_size,\n",
        "          num_train_epochs=num_train_epochs,\n",
        "      )\n",
        "\n",
        "  trainer = Trainer(\n",
        "          model=model,\n",
        "          args=training_args,\n",
        "          data_collator=data_collator,\n",
        "          train_dataset=train_dataset,\n",
        "  )\n",
        "\n",
        "  trainer.train()\n",
        "  trainer.save_model()\n",
        "\n",
        "\n",
        "'''def train(directory, model_output_path, train_fraction=0.8):\n",
        "    # Read documents from the directory\n",
        "    combined_text = read_txt(directory)\n",
        "    combined_text = re.sub(r'\\n+', '\\n', combined_text).strip()  # Remove excess newline characters\n",
        "\n",
        "    # Split the text into training and validation sets\n",
        "    split_index = int(train_fraction * len(combined_text))\n",
        "    train_text = combined_text[:split_index]\n",
        "    val_text = combined_text[split_index:]\n",
        "\n",
        "    # Save the training and validation data as text files\n",
        "    with open(\"train.txt\", \"w\") as f:\n",
        "        f.write(train_text)\n",
        "    with open(\"val.txt\", \"w\") as f:\n",
        "        f.write(val_text)\n",
        "\n",
        "    # Set up the tokenizer and model\n",
        "    tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2-large\")  #also try gpt2, gpt2-large and gpt2-medium, also gpt2-xl\n",
        "    model = GPT2LMHeadModel.from_pretrained(\"gpt2-large\")  #also try gpt2, gpt2-large and gpt2-medium, also gpt2-xl\n",
        "\n",
        "    # Prepare the dataset\n",
        "    train_dataset = TextDataset(tokenizer=tokenizer, file_path=\"train.txt\", block_size=128)\n",
        "    val_dataset = TextDataset(tokenizer=tokenizer, file_path=\"val.txt\", block_size=128)\n",
        "    data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)\n",
        "\n",
        "    # Set up the training arguments\n",
        "    training_args = TrainingArguments(\n",
        "        output_dir=model_output_path,\n",
        "        overwrite_output_dir=overwrite_output_dir,\n",
        "        per_device_train_batch_size=per_device_train_batch_size,\n",
        "        per_device_eval_batch_size=4,\n",
        "        num_train_epochs=num_train_epochs,\n",
        "        save_steps= 50000,\n",
        "        save_total_limit=2,\n",
        "        logging_dir='./logs',\n",
        "    )\n",
        "\n",
        "    # Train the model\n",
        "    trainer = Trainer(\n",
        "        model=model,\n",
        "        args=training_args,\n",
        "        data_collator=data_collator,\n",
        "        train_dataset=train_dataset,\n",
        "        eval_dataset=val_dataset,\n",
        "    )\n",
        "\n",
        "    trainer.train()\n",
        "    trainer.save_model(model_output_path)\n",
        "\n",
        "    # Save the tokenizer\n",
        "    tokenizer.save_pretrained(model_output_path)'''\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9z1ehty4KPMC"
      },
      "outputs": [],
      "source": [
        "train_file_path = \"/content/drive/MyDrive/Musuem_Data/Conversational_QA.txt\"\n",
        "model_name = 'gpt2'\n",
        "output_dir = '/content/drive/MyDrive/Musuem_Data/Models/Conversational_Model'\n",
        "overwrite_output_dir = False\n",
        "per_device_train_batch_size = 8\n",
        "num_train_epochs = 60.0\n",
        "save_steps = 50000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "iIz_OcTZMFt6",
        "outputId": "b4fae2c7-3f94-454c-ddc0-53a453350672"
      },
      "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": {
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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]"
            ],
            "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 (…)lve/main/config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/data/datasets/language_modeling.py:53: FutureWarning: This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets library. You can have a look at this example script for pointers: https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "60f47fd4097841b08b5f32d6e4a6768c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0efb02375d0b4239a33db73f62187f31"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='360' max='360' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [360/360 01:32, Epoch 60/60]\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",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "train(\n",
        "    train_file_path=train_file_path,\n",
        "    model_name=model_name,\n",
        "    output_dir=output_dir,\n",
        "    overwrite_output_dir=overwrite_output_dir,\n",
        "    per_device_train_batch_size=per_device_train_batch_size,\n",
        "    num_train_epochs=num_train_epochs,\n",
        "    save_steps=save_steps\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xR9VDV0aNHvG"
      },
      "outputs": [],
      "source": [
        "from transformers import PreTrainedTokenizerFast, GPT2LMHeadModel, GPT2TokenizerFast, GPT2Tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8SGtoytYNHxX"
      },
      "outputs": [],
      "source": [
        "def load_model(model_path):\n",
        "    model = GPT2LMHeadModel.from_pretrained(model_path)\n",
        "    return model\n",
        "\n",
        "\n",
        "def load_tokenizer(tokenizer_path):\n",
        "    tokenizer = GPT2Tokenizer.from_pretrained(tokenizer_path)\n",
        "    return tokenizer\n",
        "\n",
        "'''def generate_text(model_path, sequence, max_length):\n",
        "\n",
        "    model = load_model(model_path)\n",
        "    tokenizer = load_tokenizer(model_path)\n",
        "    ids = tokenizer.encode(f'{sequence}', return_tensors='pt')\n",
        "    final_outputs = model.generate(\n",
        "        ids,\n",
        "        do_sample=True,\n",
        "        max_length=max_length,\n",
        "        pad_token_id=model.config.eos_token_id,\n",
        "        top_k=50,\n",
        "        top_p=0.95,\n",
        "    )\n",
        "    print(tokenizer.decode(final_outputs[0], skip_special_tokens=True))'''\n",
        "\n",
        "def generate_text(model_path, sequence, max_length):\n",
        "    model = load_model(model_path)\n",
        "    tokenizer = load_tokenizer(model_path)\n",
        "    ids = tokenizer.encode(f'{sequence}', return_tensors='pt')\n",
        "    final_outputs = model.generate(\n",
        "        ids,\n",
        "        do_sample=True,\n",
        "        max_length=max_length,\n",
        "        pad_token_id=model.config.eos_token_id,\n",
        "        top_k=50,\n",
        "        top_p=0.95,\n",
        "    )\n",
        "    generated_text = tokenizer.decode(final_outputs[0], skip_special_tokens=True)\n",
        "\n",
        "    # Find the first occurrence of [Guide] and [Visitor] in the generated text\n",
        "    guide_start_index = generated_text.find('[Guide]')\n",
        "    visitor_start_index = generated_text.find('[Visitor]')\n",
        "\n",
        "    # Extract the generated text between [Guide] and [Visitor]\n",
        "    if guide_start_index != -1 and visitor_start_index != -1:\n",
        "        generated_text = generated_text[guide_start_index + 8:visitor_start_index].strip()\n",
        "    elif guide_start_index != -1:\n",
        "        generated_text = generated_text[guide_start_index + 8:].strip()\n",
        "\n",
        "    return generated_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vLXO_FUoNH0F",
        "outputId": "719f49f0-4802-4654-d01f-77a104174e86"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Welcome to the Louvre Museum! I'm here to provide you with information and answer any questions you may have\n",
            "User: Where is this Museum located?\n",
            "Guide: The Louvre Museum is located in Paris, France. It's a magnificent museum with a rich collection of art.\n",
            "Guide: Do you need anything else?\n",
            "User: thanks\n",
            "Enjoy your time exploring the incredible art at the Louvre Museum!\n"
          ]
        }
      ],
      "source": [
        "model_path = \"/content/drive/MyDrive/Musuem_Data/Models/Conversational_Model\"\n",
        "print(\"Welcome to the Louvre Museum! I'm here to provide you with information and answer any questions you may have\")\n",
        "end_keywords = ['no', 'thank you', 'thanks','thank you!','thanks!']\n",
        "\n",
        "while True:\n",
        "    sequence = input(\"User: \")\n",
        "    if sequence.lower() in end_keywords:\n",
        "      print(\"Enjoy your time exploring the incredible art at the Louvre Museum!\")\n",
        "      break\n",
        "    else:\n",
        "      max_len = 100\n",
        "      answer = generate_text(model_path, sequence, max_len)\n",
        "      if answer == '':\n",
        "          print(\"Sorry, but I have no information about your question.\")\n",
        "      else:\n",
        "          print(\"Guide: \" + answer)\n",
        "          print(\"Guide: Do you need anything else?\")\n",
        "          exit_command = input(\"User: \").lower()\n",
        "          if exit_command in end_keywords:\n",
        "              print(\"Enjoy your time exploring the incredible art at the Louvre Museum!\")\n",
        "              break"
      ]
    },
    {
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