{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c08e675e-437e-4e7d-baee-bd55dda74611",
   "metadata": {},
   "source": [
    "# Abstractive Text Summarization with T5\n",
    "\n",
    "This implementation uses HuggingFace, especially utilizing `AutoModelForSeq2SeqLM` and `AutoTokenizer`. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a910e4b5-040d-4499-b5c2-32f3e1ac1c34",
   "metadata": {},
   "source": [
    "## Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d22ee5a9-1981-4883-a926-db37905ec8b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup done!\n"
     ]
    }
   ],
   "source": [
    "# Installs\n",
    "!pip install -q evaluate py7zr rouge_score absl-py\n",
    "\n",
    "# Imports here\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import nltk\n",
    "from nltk.tokenize import sent_tokenize\n",
    "nltk.download(\"punkt\")\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "import datasets\n",
    "import transformers\n",
    "from transformers import (\n",
    "        AutoModelForSeq2SeqLM,\n",
    "        Seq2SeqTrainingArguments,\n",
    "        Seq2SeqTrainer,\n",
    "        AutoTokenizer\n",
    ")\n",
    "import evaluate\n",
    "\n",
    "# Quality of life fixes\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from pprint import pprint\n",
    "\n",
    "import os\n",
    "os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
    "\n",
    "from IPython.display import clear_output\n",
    "\n",
    "print(f\"PyTorch version: {torch.__version__}\")\n",
    "print(f\"Transformers version: {transformers.__version__}\")\n",
    "print(f\"Datasets version: {datasets.__version__}\")\n",
    "print(f\"Evaluate version: {evaluate.__version__}\")\n",
    "\n",
    "# Get the samsum dataset\n",
    "samsum = datasets.load_dataset('samsum')\n",
    "clear_output()\n",
    "print(\"Setup done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bafa753c-0746-4ece-b5eb-4511c9138b09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.27.4'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verify transformers version\n",
    "transformers.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f15204cc-0f21-4dc9-a8e4-429c57b227a9",
   "metadata": {},
   "source": [
    "## Playing around with the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ba5c1425-a776-4201-97e2-bd420ec112fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['id', 'dialogue', 'summary'],\n",
       "        num_rows: 14732\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['id', 'dialogue', 'summary'],\n",
       "        num_rows: 819\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['id', 'dialogue', 'summary'],\n",
       "        num_rows: 818\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The samsum dataset shape\n",
    "samsum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5d53736c-a8c7-4fe3-b8f1-566c1d99162b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dialogue:\n",
      "Ollie: How is your Hebrew?\r\n",
      "Gabi: Not great. \r\n",
      "Ollie: Could you translate a letter?\r\n",
      "Gabi: From Hebrew to English maybe, the opposite I don’t think so\r\n",
      "Gabi: My writing sucks\r\n",
      "Ollie: Please help me. I don’t have anyone else to ask\r\n",
      "Gabi: Send it to me. I’ll try. \n",
      "\n",
      " -------------------------------------------------- \n",
      "\n",
      "Summary:\n",
      "Gabi knows a bit of Hebrew, though her writing isn't great. She will try to help Ollie translate a letter.\n"
     ]
    }
   ],
   "source": [
    "rand_idx = np.random.randint(0, len(samsum['train']))\n",
    "\n",
    "print(f\"Dialogue:\\n{samsum['train'][rand_idx]['dialogue']}\")\n",
    "print('\\n', '-'*50, '\\n')\n",
    "print(f\"Summary:\\n{samsum['train'][rand_idx]['summary']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f95359e-c9c4-4ed5-9130-5e2b4a0a83ad",
   "metadata": {},
   "source": [
    "## Preprocessing data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50b572e6-b37a-4688-94c9-9c45a2c67c51",
   "metadata": {},
   "source": [
    " I'm using the T5 Transformers model (Text-to-Text Transfer Transformer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "13634dfe-5b1a-4515-9476-8ac0637d0362",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ckpt = 't5-small'\n",
    "\n",
    "# TODO: Create the Tokenizer AutoTokenizer pretrained checkpoint\n",
    "tokenizer = AutoTokenizer.from_pretrained('t5-small')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6b0be9fc-029b-4057-9d08-29235e5b4573",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at C:\\Users\\QXLVR\\.cache\\huggingface\\datasets\\samsum\\samsum\\0.0.0\\f1d7c6b7353e6de335d444e424dc002ef70d1277109031327bc9cc6af5d3d46e\\cache-78c13bd5dd6a016a.arrow\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max source length: 512\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/15551 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max target length: 95\n"
     ]
    }
   ],
   "source": [
    "from datasets import concatenate_datasets\n",
    "# Find the max lengths of the source and target samples\n",
    "# The maximum total input sequence length after tokenization. \n",
    "# Sequences that are longer than this will be truncated, sequences shorter are be padded.\n",
    "tokenized_inputs = concatenate_datasets([samsum[\"train\"], samsum[\"test\"]]).map(lambda x: tokenizer(x[\"dialogue\"], truncation=True), batched=True, remove_columns=[\"dialogue\", \"summary\"])\n",
    "max_source_length = max([len(x) for x in tokenized_inputs[\"input_ids\"]])\n",
    "print(f\"Max source length: {max_source_length}\")\n",
    "\n",
    "# The maximum total sequence length for target text after tokenization. \n",
    "# Sequences that are longer than this will be truncated, sequences shorter are be padded.\n",
    "tokenized_targets = concatenate_datasets([samsum[\"train\"], samsum[\"test\"]]).map(lambda x: tokenizer(x[\"summary\"], truncation=True), batched=True, remove_columns=[\"dialogue\", \"summary\"])\n",
    "max_target_length = max([len(x) for x in tokenized_targets[\"input_ids\"]])\n",
    "print(f\"Max target length: {max_target_length}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c43b0864-8b92-4cb9-b159-bc8ec15bcc2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at C:\\Users\\QXLVR\\.cache\\huggingface\\datasets\\samsum\\samsum\\0.0.0\\f1d7c6b7353e6de335d444e424dc002ef70d1277109031327bc9cc6af5d3d46e\\cache-073bbcc8f496f07c.arrow\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/819 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at C:\\Users\\QXLVR\\.cache\\huggingface\\datasets\\samsum\\samsum\\0.0.0\\f1d7c6b7353e6de335d444e424dc002ef70d1277109031327bc9cc6af5d3d46e\\cache-a43b31cabc78c9c3.arrow\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys of tokenized dataset: ['input_ids', 'attention_mask', 'labels']\n"
     ]
    }
   ],
   "source": [
    "def preprocess_function(\n",
    "    sample, \n",
    "    padding=\"max_length\", \n",
    "    max_source_length=max_source_length,\n",
    "    max_target_length=max_target_length\n",
    "):\n",
    "    '''\n",
    "    A preprocessing function that will be applied across the dataset.\n",
    "    The inputs and targets will be tokenized and padded/truncated to the max lengths.\n",
    "\n",
    "    Args:\n",
    "        sample: A dictionary containing the source and target texts (keys are \"dialogue\" and \"summary\") in a list.\n",
    "        padding: Whether to pad the inputs and targets to the max lengths.\n",
    "        max_source_length: The maximum length of the source text.\n",
    "        max_target_length: The maximum length of the target text.\n",
    "    '''\n",
    "    # Add prefix to the input for t5\n",
    "    inputs = ['summarize: ' + s for s in sample['dialogue']]\n",
    "   \n",
    "    # Tokenize inputs, specifying the padding, truncation and max_length\n",
    "    model_inputs = tokenizer(inputs, max_length=max_source_length, padding=padding, truncation=True)\n",
    "\n",
    "    # Tokenize targets with the `text_target` keyword argument\n",
    "    labels = tokenizer(text_target=sample['summary'], max_length=max_target_length, padding=padding, truncation=True)\n",
    "\n",
    "    # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore padding in the loss\n",
    "    if padding == \"max_length\":\n",
    "        labels[\"input_ids\"] = [\n",
    "            [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels[\"input_ids\"]\n",
    "        ]\n",
    "\n",
    "    # Format and return\n",
    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
    "    return model_inputs\n",
    "\n",
    "# Map this preprocessing function to our datasets using .map on the samsum variable\n",
    "tokenized_dataset = samsum.map(preprocess_function, batched=True, remove_columns=[\"dialogue\", \"summary\", \"id\"])\n",
    "print(f\"Keys of tokenized dataset: {list(tokenized_dataset['train'].features)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3becd236-0097-4ae5-9bd6-a91ed332e748",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['input_ids', 'attention_mask', 'labels'],\n",
       "        num_rows: 14732\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['input_ids', 'attention_mask', 'labels'],\n",
       "        num_rows: 819\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['input_ids', 'attention_mask', 'labels'],\n",
       "        num_rows: 818\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "20110839-bb02-4d64-8de7-53253e3f7fe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "metric = evaluate.load(\"rouge\")\n",
    "clear_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ca00f91d-8453-4496-a064-525ef437198f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def postprocess_text(preds, labels):\n",
    "    '''\n",
    "    A simple post-processing function to clean up the predictions and labels\n",
    "\n",
    "    Args:\n",
    "        preds: List[str] of predictions\n",
    "        labels: List[str] of labels\n",
    "    '''\n",
    "    \n",
    "    # strip whitespace on all sentences in preds and labels\n",
    "    preds = [p.strip(' ') for p in preds]\n",
    "    labels = [l.strip(' ') for l in preds]\n",
    "    \n",
    "    # rougeLSum expects newline after each sentence\n",
    "    preds = [\"\\n\".join(sent_tokenize(pred)) for pred in preds]\n",
    "    labels = [\"\\n\".join(sent_tokenize(label)) for label in labels]\n",
    "\n",
    "    return preds, labels\n",
    "\n",
    "def compute_metrics(eval_preds):\n",
    "    \n",
    "    # Fetch the predictions and labels\n",
    "    preds, labels = eval_preds\n",
    "    if isinstance(preds, tuple):\n",
    "        preds = preds[0]\n",
    "    \n",
    "    # Decode the predictions back to text\n",
    "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
    "    \n",
    "    # Replace -100 in the labels as we can't decode them.\n",
    "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
    "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
    "\n",
    "    # Some simple post-processing for ROUGE\n",
    "    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)\n",
    "\n",
    "    # Compute ROUGE on the decoded predictions and the decoder labels\n",
    "    result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)\n",
    "    \n",
    "    result = {k: round(v * 100, 4) for k, v in result.items()}\n",
    "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]\n",
    "    result[\"gen_len\"] = np.mean(prediction_lens)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b244846-2ebf-4019-a577-3ef07e350f7c",
   "metadata": {},
   "source": [
    "## Creating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "49c1ac7c-6400-4a67-b32b-5bdc7330d790",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the AutoModelForSeq2SeqLM class and use the model_ckpt variable)\n",
    "model = AutoModelForSeq2SeqLM.from_pretrained(model_ckpt)\n",
    "\n",
    "clear_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e027b290-c04f-4241-b238-41787f32abe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we want to ignore tokenizer pad token in the loss\n",
    "label_pad_token_id = -100\n",
    "\n",
    "# Data Collator, specifying the tokenizer, model, and label_pad_token_id\n",
    "# pad_to_multiple_of=8 to speed up training\n",
    "data_collator = transformers.DataCollatorForSeq2Seq(\n",
    "    tokenizer,\n",
    "    model=model,\n",
    "    label_pad_token_id=label_pad_token_id,\n",
    "    pad_to_multiple_of=8\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0d20ee86-ac8c-4ae7-9e7c-92283e879e00",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "logging.getLogger(\"transformers\").setLevel(logging.WARNING)\n",
    "\n",
    "\n",
    "# Define training hyperparameters in Seq2SeqTrainingArguments\n",
    "training_args = Seq2SeqTrainingArguments(\n",
    "    output_dir=\"./t5_samsum\", # the output directory\n",
    "    logging_strategy=\"epoch\",\n",
    "    save_strategy=\"epoch\",\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    learning_rate=2e-5,\n",
    "    num_train_epochs=5,\n",
    "    predict_with_generate=True,\n",
    "    per_device_train_batch_size=8,\n",
    "    per_device_eval_batch_size=8,\n",
    "    weight_decay=0.01,\n",
    "    load_best_model_at_end=True,\n",
    "    logging_steps=50,\n",
    "    logging_first_step=False,\n",
    "    fp16=False\n",
    ")\n",
    "\n",
    "# index into the tokenized_dataset variable to get the training and validation data\n",
    "training_data = tokenized_dataset['train']\n",
    "eval_data = tokenized_dataset['validation']\n",
    "\n",
    "# Create the Trainer for the model\n",
    "trainer = Seq2SeqTrainer(\n",
    "    model=model,    # the model to be trained\n",
    "    args=training_args, # training arguments\n",
    "    train_dataset=training_data, # the training dataset\n",
    "    eval_dataset=eval_data, # the validation dataset\n",
    "    tokenizer=tokenizer, # the tokenizer we used to tokenize our data\n",
    "    compute_metrics=compute_metrics, # the function we defined above to compute metrics\n",
    "    data_collator=data_collator # the data collator we defined above\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a3b5f21d-b4cb-4f8b-a7fc-cf132ef43c65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TrainOutput(global_step=9210, training_loss=1.9861197174436753, metrics={'train_runtime': 3551.1547, 'train_samples_per_second': 20.743, 'train_steps_per_second': 2.594, 'total_flos': 9969277096427520.0, 'train_loss': 1.9861197174436753, 'epoch': 5.0})\n"
     ]
    }
   ],
   "source": [
    "# Train the model (this will take a while!)\n",
    "results = trainer.train()\n",
    "clear_output()\n",
    "pprint(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddf8c308",
   "metadata": {},
   "source": [
    "## Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "03e94a7f-2d26-48eb-ab17-cb58b14b93f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = trainer.evaluate()\n",
    "clear_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "23675ccb-071c-4a4f-8e42-1a71dc628a5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eval_loss</th>\n",
       "      <th>eval_rouge1</th>\n",
       "      <th>eval_rouge2</th>\n",
       "      <th>eval_rougeL</th>\n",
       "      <th>eval_rougeLsum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t5-small</th>\n",
       "      <td>1.764253</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          eval_loss  eval_rouge1  eval_rouge2  eval_rougeL  eval_rougeLsum\n",
       "t5-small   1.764253        100.0        100.0        100.0           100.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols  = [\"eval_loss\", \"eval_rouge1\", \"eval_rouge2\", \"eval_rougeL\", \"eval_rougeLsum\"]\n",
    "filtered_scores = dict((x , res[x]) for x in cols)\n",
    "pd.DataFrame([filtered_scores], index=[model_ckpt])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7c59a731",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "summarizer_pipeline = pipeline(\"summarization\",\n",
    "                              model=model,\n",
    "                              tokenizer=tokenizer,\n",
    "                              device=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5138f2bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dialogue: Adelina: Hi handsome. Where you you come from?\r\n",
      "Cyprien: What do you mean?\r\n",
      "Adelina: What do you mean, \"what do you mean\"? It's a simple question, where do you come from?\r\n",
      "Cyprien: Well I was born in Jarrow, live in London now, so you could say I came from either of those places\r\n",
      "Cyprien: I was educated in Loughborouogh, so in a sense I came from there.\r\n",
      "Adelina: OK. \r\n",
      "Cyprien: In another sense I come from my mother's vagina, but I dare say everyone can say that.\r\n",
      "Adelina: Are you all right?\r\n",
      "Cyprien: IN another sense I come from the atoms in the air that I breath or the food I eat, which comes to me from many places, so all I can say is \"I come from Planet Earth\".\r\n",
      "Adelina: OK, bye. If you're gonna be a dick...\r\n",
      "Cyprien: Wait, what you got against earthlings?\n",
      "-------------------------\n",
      "True Summary: Cyprien irritates Adelina by giving too many responses.\n",
      "-------------------------\n",
      "Model Summary: Cyprien came from Jarrow, live in London. She came from Loughborouogh, and came from her mother's vagina.\n",
      "-------------------------\n"
     ]
    }
   ],
   "source": [
    "rand_idx = np.random.randint(low=0, high=len(samsum[\"test\"]))\n",
    "sample = samsum[\"test\"][rand_idx]\n",
    "\n",
    "dialog = sample[\"dialogue\"]\n",
    "true_summary = sample[\"summary\"]\n",
    "\n",
    "model_summary = summarizer_pipeline(dialog)\n",
    "clear_output()\n",
    "\n",
    "print(f\"Dialogue: {dialog}\")\n",
    "print(\"-\"*25)\n",
    "print(f\"True Summary: {true_summary}\")\n",
    "print(\"-\"*25)\n",
    "print(f\"Model Summary: {model_summary[0]['summary_text']}\")\n",
    "print(\"-\"*25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f051655f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Your max_length is set to 200, but you input_length is only 94. You might consider decreasing max_length manually, e.g. summarizer('...', max_length=47)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Text:\n",
      "\n",
      "Andy: I need you to come in to work on the weekend.\n",
      "David: Why boss? I have plans to go on a concert I might not be able to come on the weekend.\n",
      "Andy: It's important we need to get our paperwork all sorted out for this year. Corporate needs it.\n",
      "David: But I already made plans and this is news to me on very short notice.\n",
      "Andy: Be there or you'r fired\n",
      "\n",
      "\n",
      " -------------------------------------------------- \n",
      "\n",
      "Generated Summary: \n",
      "[{'summary_text': 'David has plans to go on a concert. Andy needs to get his paperwork all sorted out for this year. David already made plans.'}]\n"
     ]
    }
   ],
   "source": [
    "def create_summary(input_text, model_pipeline=summarizer_pipeline):\n",
    "    summary = model_pipeline(input_text)\n",
    "    return summary\n",
    "\n",
    "text = '''\n",
    "Andy: I need you to come in to work on the weekend.\n",
    "David: Why boss? I have plans to go on a concert I might not be able to come on the weekend.\n",
    "Andy: It's important we need to get our paperwork all sorted out for this year. Corporate needs it.\n",
    "David: But I already made plans and this is news to me on very short notice.\n",
    "Andy: Be there or you'r fired\n",
    "'''\n",
    "\n",
    "print(f\"Original Text:\\n{text}\")\n",
    "print('\\n', '-'*50, '\\n')\n",
    "\n",
    "summary = create_summary(text)\n",
    "\n",
    "print(f\"Generated Summary: \\n{summary}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad5d29a0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}