run_phenogpt.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6WuAlTCs2b-Y"
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "3xG6yyBJbW4N",
    "outputId": "d06931c6-f62f-4ee9-ceb3-a1884aa07e4c",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nguyenqm/miniconda3/envs/llm_phenogpt/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "/home/nguyenqm/miniconda3/envs/llm_phenogpt/lib/python3.11/site-packages/transformers/utils/generic.py:462: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
      "  _torch_pytree._register_pytree_node(\n",
      "/home/nguyenqm/miniconda3/envs/llm_phenogpt/lib/python3.11/site-packages/transformers/utils/generic.py:319: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
      "  _torch_pytree._register_pytree_node(\n",
      "/home/nguyenqm/miniconda3/envs/llm_phenogpt/lib/python3.11/site-packages/transformers/utils/generic.py:319: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
      "  _torch_pytree._register_pytree_node(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import transformers\n",
    "import textwrap\n",
    "from transformers import LlamaTokenizer, LlamaForCausalLM\n",
    "from transformers import GenerationConfig\n",
    "import sys\n",
    "from peft import PeftModel\n",
    "from typing import List\n",
    "\n",
    "from peft import (\n",
    "    LoraConfig,\n",
    "    get_peft_model,\n",
    "    get_peft_model_state_dict,\n",
    "    prepare_model_for_kbit_training\n",
    ")\n",
    "import torch\n",
    "from datasets import load_dataset\n",
    "import pandas as pd\n",
    "import json, glob\n",
    "\n",
    "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "DEVICE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s0s-WbYYTqI3"
   },
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data is from Biolark GSC+. Please download Biolark GSC on your own\n",
    "#### You can replace your own dataset for your own purpose of fine-tuning in this section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "sYUQX4qibRj_",
    "outputId": "f4a4e3fc-3356-40f2-ef3b-abefc6b5b2c8"
   },
   "outputs": [],
   "source": [
    "path = '/mnt/isilon/wang_lab/jingye/projects/data/biolarkgsc_locs.csv'\n",
    "biolark = pd.read_csv(path, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z2Yr_ol4YAAV",
    "outputId": "a9215f19-d83a-40b2-9606-fc03cdbfe57f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(205, 23)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_df, val_df = train_test_split(biolark, test_size=0.1, random_state=42)\n",
    "len(train_df), len(val_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "x6tbLDlpehkZ",
    "outputId": "10da09f2-eeb9-4961-ddba-9c6a165bfe78"
   },
   "outputs": [],
   "source": [
    "def preprocess(train_df):\n",
    "    rows = []\n",
    "    for i, row in train_df.iterrows():\n",
    "#         bad examples in train_df\n",
    "#         if i in {160,7,23,76}:\n",
    "#             continue\n",
    "        list_hpo = row.labels.split(';')\n",
    "        list_hpo_pair = [[_.split('|')[0], _.split('|')[1].split(':')]  for _ in list_hpo]\n",
    "        # print(list_hpo_pair)\n",
    "        # for hpo, [s,e] in list_hpo_pair:\n",
    "        #     print(row.text[int(s)-50:int(e)+50])\n",
    "        # break\n",
    "        ans = \"\"\n",
    "        hpos = set()\n",
    "        for hpo, [s,e] in list_hpo_pair:\n",
    "            if hpo in hpos:\n",
    "                continue\n",
    "            ans += ' ' + row.text[int(s):int(e)] + ' | ' + hpo + '\\n'\n",
    "            hpos.add(hpo)\n",
    "        ans += ' END'\n",
    "        text = {'input': row.text, 'output': ans}\n",
    "        rows.append(text)\n",
    "#         rows.append({\"prompt\":f\"{row.text}\\n\\n###\\n\\n\", \"completion\":f\" {ans}\"})\n",
    "    return rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 548
    },
    "id": "goHFjtiSY3GW",
    "outputId": "2d670098-904a-42e7-91aa-968b6c6845dc"
   },
   "outputs": [],
   "source": [
    "biolark_ft = preprocess(train_df)\n",
    "biolark_val = preprocess(val_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An example how the input data is processed for model training\n",
    "#### {'input':'TEXT', 'output': 'Phenotype1 | HP_identifier1\\n Phenotype2 | HP_identifier2\\n Phenotype 3 | HP_identifer3\\n ... \\n END'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "ZfSHiESLiUGq"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input': 'Hereditary isolated brachydactyly type C (OMIM 113100) mostly follows an autosomal dominant pattern of inheritance with a marked variability in expression. This phenotype has been mapped to two different loci on chromosomes 12q24 and 20q11.2. The latter locus contains the cartilage-derived morphogenetic protein (CDMP)1 gene, in which a null mutation has been found in patients with malformations restricted to the upper limbs. A more complex brachydactyly type C phenotype has been mapped to chromosome 12q24. Differences in complexity of these phenotypes have been attributed to locus heterogeneity. Clinical subclassification based on the degree of complexity of the phenotype has therefore been suggested. We present patients with a complex brachydactyly type C phenotype in whom there is considerable intra- and interfamilial variability in expression. We show that clinical subclassification based on the complexity of the brachydactyly type C phenotype related to the genetic defect is not feasible. We present evidence that differences in complexity are not only due to locus heterogeneity, but that genetic modifiers and/or environmental factors must also play a role.',\n",
       " 'output': ' brachydactyly | HP_0001156\\n brachydactyly type C | HP_0009373\\n autosomal dominant | HP_0000006\\n marked variability in expression | HP_0003828\\n variability | HP_0003812\\n malformations restricted to the upper limbs | HP_0002817\\n locus heterogeneity | HP_0001425\\n considerable intra- and interfamilial variability | HP_0003822\\n END'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training Example\n",
    "biolark_ft[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input': 'Nevoid basal cell carcinoma syndrome (NBCCS) is rare in black persons. We describe an 11-year-old black boy with NBCCS who presented with exotropia and a painful, expanding, cystic mass in the left posterior alveolar ridge. Further examination revealed odontogenic keratocysts with palmar and plantar pitting. Less than 5% of reported patients with NBCCS are black. To our knowledge, this is the first report of a black patient with NBCCS presenting with exotropia and an impacted molar displaced into the orbit by an odontogenic keratocyst.',\n",
       " 'output': ' basal cell carcinoma | HP_0002671\\n exotropia | HP_0000577\\n cystic mass in the left posterior alveolar ridge | HP_0006477\\n odontogenic keratocysts | HP_0010603\\n palmar and plantar pitting | HP_0010612\\n END'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluation example\n",
    "biolark_val[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"biolark_train.json\", \"w\") as f:\n",
    "    json.dump(biolark_ft, f)\n",
    "with open(\"biolark_val.json\", \"w\") as f:\n",
    "    json.dump(biolark_val, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace your training data directory here\n",
    "train_data = load_dataset(\"json\", data_files=\"biolark_train.json\", split = 'train')\n",
    "val_data = load_dataset(\"json\", data_files=\"biolark_test.json\", split = 'train')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HzSlXm1_T3V5"
   },
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
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     ]
    },
    "id": "SvgJvcO9d12M",
    "outputId": "af74ec42-9057-4908-e678-a660345e6412"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n",
      "Loading checkpoint shards: 100%|██████████| 3/3 [00:06<00:00,  2.08s/it]\n"
     ]
    }
   ],
   "source": [
    "# BASE_MODEL = \"/home/nguyenqm/projects/github/Llama-2-7b-hf\"\n",
    "BASE_MODEL = '/home/nguyenqm/projects/github/llama/hf_models/llama-2-7b-chat'\n",
    "model = LlamaForCausalLM.from_pretrained(\n",
    "    BASE_MODEL,\n",
    "    load_in_8bit=True,\n",
    "    torch_dtype=torch.float16,\n",
    "    device_map='auto',\n",
    ")\n",
    "\n",
    "tokenizer = LlamaTokenizer.from_pretrained(BASE_MODEL)\n",
    "\n",
    "tokenizer.pad_token_id = (\n",
    "    0  # unk. we want this to be different from the eos token\n",
    ")\n",
    "tokenizer.padding_side = \"left\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "t5UWTUmgfevy"
   },
   "outputs": [],
   "source": [
    "CUTOFF_LEN = 512"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "vz01H6ThsDQE"
   },
   "outputs": [],
   "source": [
    "def generate_prompt(data_point):\n",
    "    return f\"\"\"Input:\n",
    "{data_point[\"input\"]}\n",
    "### Response:\n",
    "{data_point[\"out\"]}\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "D7kaRDNqg3aB"
   },
   "outputs": [],
   "source": [
    "def tokenize(prompt, add_eos_token=True):\n",
    "    # there's probably a way to do this with the tokenizer settings\n",
    "    # but again, gotta move fast\n",
    "    result = tokenizer(\n",
    "        prompt,\n",
    "        truncation=True,\n",
    "        max_length=CUTOFF_LEN,\n",
    "        padding=False,\n",
    "        return_tensors=None,\n",
    "    )\n",
    "    if (\n",
    "        result[\"input_ids\"][-1] != tokenizer.eos_token_id\n",
    "        and len(result[\"input_ids\"]) < CUTOFF_LEN\n",
    "        and add_eos_token\n",
    "    ):\n",
    "        result[\"input_ids\"].append(tokenizer.eos_token_id)\n",
    "        result[\"attention_mask\"].append(1)\n",
    "\n",
    "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
    "\n",
    "    return result\n",
    "\n",
    "def generate_and_tokenize_prompt(data_point):\n",
    "    full_prompt = generate_prompt(data_point)\n",
    "    tokenized_full_prompt = tokenize(full_prompt)\n",
    "    return tokenized_full_prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 55,
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      "673632b679a7456f90d32f8ccf5c7f46",
      "bfcf5cea6f8744409f7d4328905f0e58",
      "5ad514b85f234de1aa2bb9d410524619",
      "897c8d8d95504c788594eded27ae67c8",
      "87ef37927f11460099884c0e945c906c",
      "1247b571b96f4fa49ec6d5a909226cd6",
      "e97b7ea4995a47f19bfb9f1fcab845ec",
      "674909b6649a4ed2a41ba33394bc8108",
      "484ab27b1d0a4598a9279b88e7b658e5",
      "293f70aeb6434a2dbb1d00d61ebb71ed",
      "5f18cb392ecb4175b3b1446d8ebd5e81",
      "bf95fdae480e48efa593634d670388ca",
      "00a12232ce314299a5bd230b2e6afd9e",
      "ee8e8a033c834392bd80824d15500343"
     ]
    },
    "id": "OPmfOpQQtDMN",
    "outputId": "dcc1db33-d19d-49f1-84c5-1439988ce9b2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Map: 100%|██████████| 205/205 [00:00<00:00, 637.40 examples/s]\n",
      "Map: 100%|██████████| 23/23 [00:00<00:00, 459.46 examples/s]\n"
     ]
    }
   ],
   "source": [
    "# train_val = data[\"train\"].train_test_split(\n",
    "#     test_size=200, shuffle=True, seed=42\n",
    "# )\n",
    "train_data = (\n",
    "    train_data.map(generate_and_tokenize_prompt)\n",
    ")\n",
    "val_data = (\n",
    "    val_data.map(generate_and_tokenize_prompt)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3AWsbZI0ZL4N"
   },
   "source": [
    "## Alpaca LoRa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "5SVIxGhUorW3"
   },
   "outputs": [],
   "source": [
    "LORA_R = 8\n",
    "LORA_ALPHA = 16\n",
    "LORA_DROPOUT= 0.05\n",
    "LORA_TARGET_MODULES = [\n",
    "    \"q_proj\",\n",
    "    \"v_proj\",\n",
    "]\n",
    "\n",
    "BATCH_SIZE = 128\n",
    "MICRO_BATCH_SIZE = 128\n",
    "GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE\n",
    "LEARNING_RATE = 3e-4\n",
    "TRAIN_STEPS = 40\n",
    "OUTPUT_DIR = \"./model/llama2/fine_tuning_model2/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6-cTLirahjfg",
    "outputId": "7272ccdf-6896-41e6-a2af-54a1fb26a3cd",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model = prepare_model_for_kbit_training(model)\n",
    "config = LoraConfig(\n",
    "    r=LORA_R,\n",
    "    lora_alpha=LORA_ALPHA,\n",
    "    target_modules=LORA_TARGET_MODULES,\n",
    "    lora_dropout=LORA_DROPOUT,\n",
    "    bias=\"none\",\n",
    "    task_type=\"CAUSAL_LM\",\n",
    ")\n",
    "model = get_peft_model(model, config)\n",
    "# model.print_trainable_parameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_trainable_parameters(model):\n",
    "    trainable_params = 0\n",
    "    all_param = 0\n",
    "    for _, param in model.named_parameters():\n",
    "        all_param += param.numel()\n",
    "        if param.requires_grad:\n",
    "            trainable_params += param.numel()\n",
    "    print(\n",
    "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 4194304 || all params: 6742609920 || trainable%: 0.06220594176090199\n"
     ]
    }
   ],
   "source": [
    "print_trainable_parameters(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.float32 266604544 0.03954025921167333\n",
      "torch.int8 6476005376 0.9604597407883266\n"
     ]
    }
   ],
   "source": [
    "# Verifying the datatypes.\n",
    "dtypes = {}\n",
    "for _, p in model.named_parameters():\n",
    "    dtype = p.dtype\n",
    "    if dtype not in dtypes:\n",
    "        dtypes[dtype] = 0\n",
    "    dtypes[dtype] += p.numel()\n",
    "total = 0\n",
    "for k, v in dtypes.items():\n",
    "    total += v\n",
    "for k, v in dtypes.items():\n",
    "    print(k, v, v / total)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YBmdF1XJ7ZOu"
   },
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "-urSep2JDVH0"
   },
   "outputs": [],
   "source": [
    "training_arguments = transformers.TrainingArguments(\n",
    "    per_device_train_batch_size=64, #MICRO_BATCH_SIZE,\n",
    "    gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,\n",
    "    warmup_steps=10,\n",
    "    max_steps=50, #TRAIN_STEPS,\n",
    "    learning_rate=LEARNING_RATE,\n",
    "    fp16=True,\n",
    "    logging_steps=10,\n",
    "    optim=\"adamw_torch\",\n",
    "    evaluation_strategy=\"steps\",\n",
    "    save_strategy=\"steps\",\n",
    "    eval_steps=10,\n",
    "    save_steps=10,\n",
    "    output_dir=OUTPUT_DIR,\n",
    "    save_total_limit=3,\n",
    "    load_best_model_at_end=False, #True,\n",
    "#     report_to=\"tensorboard\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "pRWNx5vlDmRb"
   },
   "outputs": [],
   "source": [
    "data_collator = transformers.DataCollatorForSeq2Seq(\n",
    "    tokenizer, pad_to_multiple_of=8, return_tensors=\"pt\", padding=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "id": "-4eVtddRvSx4",
    "outputId": "ed518024-fcfc-4d36-b3a5-f1df4a30cea6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nguyenqm/miniconda3/envs/llm_phenogpt/lib/python3.11/site-packages/accelerate/accelerator.py:432: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n",
      "dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "trainer = transformers.Trainer(\n",
    "    model=model,\n",
    "    train_dataset=train_data,\n",
    "    eval_dataset=val_data,\n",
    "    args=training_arguments,\n",
    "    data_collator=data_collator\n",
    ")\n",
    "model.config.use_cache = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nguyenqm/miniconda3/envs/llm_phenogpt/lib/python3.11/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
      "  warnings.warn(\n",
      "/home/nguyenqm/miniconda3/envs/llm_phenogpt/lib/python3.11/site-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='50' max='50' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [50/50 24:14, Epoch 12/13]\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",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2.013900</td>\n",
       "      <td>1.908902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.711200</td>\n",
       "      <td>1.707137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.557800</td>\n",
       "      <td>1.633775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.484200</td>\n",
       "      <td>1.603521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1.468700</td>\n",
       "      <td>1.594528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nguyenqm/miniconda3/envs/llm_phenogpt/lib/python3.11/site-packages/peft/utils/save_and_load.py:154: UserWarning: Could not find a config file in /home/nguyenqm/projects/github/llama/hf_models/llama-2-7b-chat - will assume that the vocabulary was not modified.\n",
      "  warnings.warn(\n",
      "/home/nguyenqm/miniconda3/envs/llm_phenogpt/lib/python3.11/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
      "  warnings.warn(\n",
      "/home/nguyenqm/miniconda3/envs/llm_phenogpt/lib/python3.11/site-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
      "/home/nguyenqm/miniconda3/envs/llm_phenogpt/lib/python3.11/site-packages/peft/utils/save_and_load.py:154: UserWarning: Could not find a config file in /home/nguyenqm/projects/github/llama/hf_models/llama-2-7b-chat - will assume that the vocabulary was not modified.\n",
      "  warnings.warn(\n",
      "/home/nguyenqm/miniconda3/envs/llm_phenogpt/lib/python3.11/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
      "  warnings.warn(\n",
      "/home/nguyenqm/miniconda3/envs/llm_phenogpt/lib/python3.11/site-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
      "Checkpoint destination directory ./model/llama2/fine_tuning_model2/checkpoint-30 already exists and is non-empty. Saving will proceed but saved results may be invalid.\n",
      "/home/nguyenqm/miniconda3/envs/llm_phenogpt/lib/python3.11/site-packages/peft/utils/save_and_load.py:154: UserWarning: Could not find a config file in /home/nguyenqm/projects/github/llama/hf_models/llama-2-7b-chat - will assume that the vocabulary was not modified.\n",
      "  warnings.warn(\n",
      "/home/nguyenqm/miniconda3/envs/llm_phenogpt/lib/python3.11/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
      "  warnings.warn(\n",
      "/home/nguyenqm/miniconda3/envs/llm_phenogpt/lib/python3.11/site-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
      "Checkpoint destination directory ./model/llama2/fine_tuning_model2/checkpoint-40 already exists and is non-empty. Saving will proceed but saved results may be invalid.\n",
      "/home/nguyenqm/miniconda3/envs/llm_phenogpt/lib/python3.11/site-packages/peft/utils/save_and_load.py:154: UserWarning: Could not find a config file in /home/nguyenqm/projects/github/llama/hf_models/llama-2-7b-chat - will assume that the vocabulary was not modified.\n",
      "  warnings.warn(\n",
      "/home/nguyenqm/miniconda3/envs/llm_phenogpt/lib/python3.11/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
      "  warnings.warn(\n",
      "/home/nguyenqm/miniconda3/envs/llm_phenogpt/lib/python3.11/site-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
      "Checkpoint destination directory ./model/llama2/fine_tuning_model2/checkpoint-50 already exists and is non-empty. Saving will proceed but saved results may be invalid.\n",
      "/home/nguyenqm/miniconda3/envs/llm_phenogpt/lib/python3.11/site-packages/peft/utils/save_and_load.py:154: UserWarning: Could not find a config file in /home/nguyenqm/projects/github/llama/hf_models/llama-2-7b-chat - will assume that the vocabulary was not modified.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=50, training_loss=1.6471654129028321, metrics={'train_runtime': 1493.3072, 'train_samples_per_second': 2.143, 'train_steps_per_second': 0.033, 'total_flos': 5.252269194412032e+16, 'train_loss': 1.6471654129028321, 'epoch': 12.5})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thu May 16 03:24:49 2024       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  NVIDIA A100 80G...  On   | 00000000:17:00.0 Off |                    0 |\n",
      "| N/A   40C    P0    73W / 300W |  31815MiB / 81920MiB |      0%      Default |\n",
      "|                               |                      |             Disabled |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   1  NVIDIA A100 80G...  On   | 00000000:65:00.0 Off |                    0 |\n",
      "| N/A   40C    P0    78W / 300W |  31677MiB / 81920MiB |      0%      Default |\n",
      "|                               |                      |             Disabled |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   2  NVIDIA A100 80G...  On   | 00000000:CA:00.0 Off |                    0 |\n",
      "| N/A   41C    P0    82W / 300W |  31677MiB / 81920MiB |      0%      Default |\n",
      "|                               |                      |             Disabled |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   3  NVIDIA A100 80G...  On   | 00000000:E3:00.0 Off |                    0 |\n",
      "| N/A   42C    P0    79W / 300W |  36997MiB / 81920MiB |      0%      Default |\n",
      "|                               |                      |             Disabled |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|    0   N/A  N/A   3800510      C   ...s/llm_phenogpt/bin/python    31812MiB |\n",
      "|    1   N/A  N/A   3800510      C   ...s/llm_phenogpt/bin/python    31674MiB |\n",
      "|    2   N/A  N/A   3800510      C   ...s/llm_phenogpt/bin/python    31674MiB |\n",
      "|    3   N/A  N/A   3800510      C   ...s/llm_phenogpt/bin/python    36994MiB |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nguyenqm/miniconda3/envs/llm_phenogpt/lib/python3.11/site-packages/peft/utils/save_and_load.py:154: UserWarning: Could not find a config file in /home/nguyenqm/projects/github/llama/hf_models/llama-2-7b-chat - will assume that the vocabulary was not modified.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "model.save_pretrained(OUTPUT_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lLIr-ynuTilx"
   },
   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /home/nguyenqm/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sent2vec, joblib\n",
    "import numpy as np\n",
    "from nltk import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from string import punctuation\n",
    "from scipy.spatial import distance\n",
    "import nltk\n",
    "import re\n",
    "from itertools import chain\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## please replace the following lines as your directories to the Llama 2 7B base model & Lora-weight training above\n",
    "BASE_MODEL = os.getcwd() + \"/model/llama2/llama2_base\" #from meta\n",
    "lora_weights = os.getcwd() + '/model/llama2/lora_weights'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading checkpoint shards: 100%|██████████| 3/3 [00:25<00:00,  8.45s/it]\n"
     ]
    }
   ],
   "source": [
    "load_8bit = False\n",
    "model = LlamaForCausalLM.from_pretrained(\n",
    "            BASE_MODEL,\n",
    "            load_in_8bit=load_8bit,\n",
    "#             torch_dtype=torch.float16,\n",
    "        )\n",
    "model = PeftModel.from_pretrained(\n",
    "            model,\n",
    "            lora_weights,\n",
    "            torch_dtype=torch.float16,\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = LlamaTokenizer.from_pretrained(BASE_MODEL)\n",
    "model.config.pad_token_id = tokenizer.pad_token_id = 0  # unk\n",
    "model.config.bos_token_id = 1\n",
    "model.config.eos_token_id = 2\n",
    "if tokenizer.pad_token is None:\n",
    "    tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n",
    "tokenizer.padding_side = \"left\"\n",
    "model.eval()\n",
    "generation_config = GenerationConfig(\n",
    "            temperature=0.1,\n",
    "            top_p=0.5,\n",
    "#             top_k=40,\n",
    "#             num_beams=4,\n",
    "#             **kwargs,\n",
    "#             bad_words_ids = [[2]]\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up Answers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Please run everything below until the next section (PhenoGPT Generation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up BioSent2Vec Model\n",
    "Convert medical terms detected by PhenoGPT to HPO terms using BioSent2Vec and cosine similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model successfully loaded\n"
     ]
    }
   ],
   "source": [
    "#please replace your model path here (should use the full directory)\n",
    "biosent2vec_path = '/home/nguyenqm/projects/github/PhenoGPT/BioSentVec/model/BioSentVec_PubMed_MIMICIII-bigram_d700.bin'\n",
    "biosent2vec = sent2vec.Sent2vecModel()\n",
    "try:\n",
    "    biosent2vec.load_model(biosent2vec_path)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "print('model successfully loaded')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess sentences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There is no one-size-fits-all solution to preprocess sentences. We demonstrate a representative code example as below. This is also consistent with the preprocessing appaorach when we trained BioSentVec models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = set(stopwords.words('english'))\n",
    "def preprocess_sentence(text):\n",
    "    text = text.replace('/', ' / ')\n",
    "    text = text.replace('.-', ' .- ')\n",
    "    text = text.replace('.', ' . ')\n",
    "    text = text.replace('\\'', ' \\' ')\n",
    "    text = text.lower()\n",
    "\n",
    "    tokens = [token for token in word_tokenize(text) if token not in punctuation and token not in stop_words]\n",
    "\n",
    "    return ' '.join(tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate numerical representations for all phenotypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "hpo_database = joblib.load('hpo_database.json')\n",
    "all_terms = list(hpo_database.keys())\n",
    "all_terms_preprocessed = [preprocess_sentence(txt) for txt in all_terms]\n",
    "all_terms_vec = biosent2vec.embed_sentences(all_terms_preprocessed)\n",
    "##{Term : Numerical Vector}\n",
    "termDB2vec = {k:v for k,v in zip(all_terms, all_terms_vec)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess Answers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_hpo(text):\n",
    "    # Define the pattern to match HP:XXXXXXX\n",
    "    if \"HP:XXXXXXX\" in text:\n",
    "        pattern = \"HP:XXXXXXX\"\n",
    "    else:\n",
    "        pattern = r'HP:.+'\n",
    "    # Replace matched patterns with an empty string\n",
    "    cleaned_text = re.sub(pattern, '', text)\n",
    "    if 'note' in text.lower():\n",
    "        text = text.lower()\n",
    "        cleaned_text = re.sub(r'note:.+', '', text)\n",
    "    return cleaned_text\n",
    "def generate_output(text):\n",
    "    instructions = \"You are a medical assistant and reading a clinical note. Identify a list of all medical phenotypic abnormalities from input text. Format your answer as a list of the phenotypes separated by new line character and do not generate random answers. Only output the list.\"\n",
    "    base_prompt = \"\"\"<s>[INST]\\n<<SYS>>\\nInstructions: {system_prompt}\\n<</SYS>>\\nInput: {user_prompt}[/INST]\\n ### Response: \"\"\"\n",
    "    prompt = base_prompt.format(system_prompt = instructions,\n",
    "            user_prompt = text)\n",
    "    inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
    "    input_ids = inputs[\"input_ids\"].to('cuda')\n",
    "    model.to(DEVICE)\n",
    "    with torch.no_grad():\n",
    "        generation_output = model.generate(\n",
    "            input_ids=input_ids,\n",
    "            pad_token_id = tokenizer.eos_token_id,\n",
    "            generation_config=generation_config,\n",
    "            return_dict_in_generate=True,\n",
    "            output_scores=True,\n",
    "            max_new_tokens=300,\n",
    "    )\n",
    "    s = generation_output.sequences[0]\n",
    "    output = tokenizer.decode(s)\n",
    "    if len(input_ids[0]) > 512:\n",
    "        print(\"WARNING: Your text input has more than the predefined maximum 512 tokens. The results may be defective.\")\n",
    "    return(output)\n",
    "def clean_output(output):\n",
    "    if \"### Response:\":\n",
    "        output = output.split(\"### Response:\")[-1].split(\"\\n\")\n",
    "        output = [remove_hpo(text) for text in output]\n",
    "        if len(output) > 0:\n",
    "            output_clean = [t.split(\"|\") for t in output]\n",
    "            output_clean = list(set(chain(*output_clean)))\n",
    "            output_clean = [re.sub(r'^[\\W\\d_]+|[\\s\\W]+$', '', t) for t in output_clean if not t.strip().startswith(\"END\") and not t.strip() == '</s>']\n",
    "            output_clean = [re.sub('</s', '', t) for t in output_clean if t and t != \"Phenotype\"]\n",
    "        else:\n",
    "            print(\"No medical terms were detected\")\n",
    "            output_clean = []\n",
    "    else:\n",
    "        print(\"No medical terms were detected\")\n",
    "        output_clean = []\n",
    "    return(output_clean)\n",
    "def read_text(input_file):\n",
    "    if \".txt\" in input_file:\n",
    "        input_list=[input_file]\n",
    "    else:\n",
    "        input_list = glob.glob(input_file + \"/*.txt\")\n",
    "    input_dict = {}\n",
    "    for f in input_list:\n",
    "        file_name = f.split('/')[-1][:-4]\n",
    "        with open(f, 'r') as r:\n",
    "            data = r.readlines()\n",
    "            if len(data) > 1:\n",
    "                data = \"\\n\".join(data)\n",
    "        input_dict[file_name] = data\n",
    "    return(input_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def phenogpt_output(raw_answer, termDB2vec):\n",
    "    answer_clean = clean_output(raw_output)\n",
    "    all_terms = list(termDB2vec.keys())\n",
    "    all_terms_vec = list(termDB2vec.values())\n",
    "    answers_preprocessed = [preprocess_sentence(txt) for txt in answer_clean]\n",
    "    answer_vec = biosent2vec.embed_sentences(answers_preprocessed)\n",
    "    term2hpo = {}\n",
    "    for i,phenoterm in enumerate(answer_vec):\n",
    "        all_distances = {}\n",
    "        dist = []\n",
    "        for j, ref in enumerate(all_terms_vec):\n",
    "            dis = distance.cosine(phenoterm, ref)\n",
    "            if dis >= 0:\n",
    "                all_distances[all_terms[j]] = 1 - dis\n",
    "                dist.append(1-dis)\n",
    "        matched_pheno = list(all_distances.keys())[np.argmax(dist)]\n",
    "        hpo_id = hpo_database[matched_pheno]\n",
    "        term2hpo[answer_clean[i]] = hpo_id\n",
    "    return term2hpo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Please use the following functions to generate the list of medical terms detected by PhenoGPT.\n",
    "#### Please do not use HPO identifiers generated. We will incorporate the conversions to HPO identifiers\n",
    "#### in the next version of PhenoGPT.\n",
    "##### raw_output = generate_output(prompt)\n",
    "##### output = phenogpt_output(raw_output)\n",
    "##### output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PhenoGPT Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Place your input here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Van der Woude syndrome': 'HP:0012385', 'Popliteal pterygium syndrome': 'HP:0009756'}\n",
      "{'Chromosomal anomaly pending investigation': 'HP:0011724', 'Neonatal abdominal distension': 'HP:0003270', 'Renal hypoplasia pending investigation': 'HP:0000089'}\n",
      "{'Limbal dermoids': 'HP:0001140', \"Bell's palsy\": 'HP:0025331', 'Retained endobronchial foreign body': 'HP:0034806', 'Recurrent buccal space abscesses': 'HP:0002722', 'Intracranial fibromatosis': 'HP:0000169'}\n",
      "{'Hypoxia': 'HP:0012418', 'Deteriorating condition': 'HP:0007272', 'Possibility of death': 'HP:0003819', 'Respiratory distress': 'HP:0002098', 'Genetic testing suggested': 'HP:0032316', 'Potential for complications': 'HP:0001005'}\n",
      "{'Intrauterine distress': 'HP:0025116', 'Premature rupture of membranes': 'HP:0001788', 'Abnormal fetal position': 'HP:0034058', 'Oligohydramnios': 'HP:0001562'}\n"
     ]
    }
   ],
   "source": [
    "input_dict = read_text(\"./testing/input/\")\n",
    "for file_name, text in input_dict.items():\n",
    "    # generate raw response\n",
    "    raw_output = generate_output(text)\n",
    "    # clean up response\n",
    "    output = phenogpt_output(raw_output, termDB2vec)\n",
    "    # save output\n",
    "    with open(\"./testing/output/\"+file_name+\"_phenogpt.txt\", 'w') as f:\n",
    "        for k,v in output.items():\n",
    "            f.write(k+\"\\t\"+v+\"\\n\")\n",
    "    print(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<s><s> [INST]\n",
      "<<SYS>>\n",
      "Instructions: You are a medical assistant and reading a clinical note. Identify a list of all medical phenotypic abnormalities from input text. Format your answer as a list of the phenotypes separated by new line character and do not generate random answers. Only output the list.\n",
      "<</SYS>>\n",
      "Input: Details on fetal position, duration of labor, premature rupture of membranes, and intrauterine distress were unspecified. Oligohydramnios was noted, with amniotic fluid grade III. Details on the placenta and umbilical cord length, including any nuchal cord or knotting, were unspecified. Antenatal antibiotic use was reported, with no sedative or dexamethasone use. Sibling health status was unknown for the first sibling aged over 5 years and healthy for the second sibling aged over 2 years.[/INST]\n",
      " ### Response: \n",
      "* Oligohydramnios\n",
      "* Premature rupture of membranes\n",
      "* Intrauterine distress\n",
      "* Unspecified fetal position\n",
      "\n",
      "Note: The above list of phenotypes is based on the information provided in the clinical note and may not be a comprehensive or definitive list of all possible phenotypes that could be present in this case.</s>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Intrauterine distress': 'HP:0025116',\n",
       " 'Premature rupture of membranes': 'HP:0001788',\n",
       " 'Oligohydramnios': 'HP:0001562',\n",
       " 'Unspecified fetal position': 'HP:0010956'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## replace your text with the prompt below\n",
    "#prompt = \"Three successive generations in two families affected with the popliteal pterygium syndrome are reported. While expression of the syndrome was relatively mild in the first and second generation, the patients in the third generation showed the full-blown syndrome. Differential diagnosis between mildly affected patients with the popliteal pterygium syndrome and those with Van der Woude syndrome is difficult and may even be impossible. The present observations further support the hypothesis that both syndromes may in fact represent variants of the same condition.\"\n",
    "prompt = \"Details on fetal position, duration of labor, premature rupture of membranes, and intrauterine distress were unspecified. Oligohydramnios was noted, with amniotic fluid grade III. Details on the placenta and umbilical cord length, including any nuchal cord or knotting, were unspecified. Antenatal antibiotic use was reported, with no sedative or dexamethasone use. Sibling health status was unknown for the first sibling aged over 5 years and healthy for the second sibling aged over 2 years.\"\n",
    "raw_output = generate_output(prompt)\n",
    "print(raw_output)\n",
    "output = phenogpt_output(raw_output, termDB2vec)\n",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: Your text input has more than the predefined maximum 2048 tokens. The results may be defective.\n",
      "<s><s> [INST]\n",
      "<<SYS>>\n",
      "Instructions: You are a medical assistant and reading a clinical note. Identify a list of all medical phenotypic abnormalities from input text. Format your answer as a list of the phenotypes separated by new line character and do not generate random answers. Only output the list.\n",
      "<</SYS>>\n",
      "Input: Excision of limbal dermoids. We reviewed the clinical files of 10 patients who had undergone excision of unilateral epibulbar limbal dermoids. Preoperatively, all of the affected eyes had worse visual acuity (P less than .02) and more astigmatism (P less than .01) than the contralateral eyes. Postoperatively, every patient was cosmetically improved. Of the eight patients for whom both preoperative and postoperative visual acuity measurements had been obtained, in six it had changed minimally (less than or equal to 1 line), and in two it had improved (less than or equal to 2 lines). Surgical complications included persistent epithelial defects (40%) and peripheral corneal vascularization and opacity (70%). These complications do not outweigh the cosmetic and visual benefits of dermoid excision in selected patients. Bell's palsy. A diagnosis of exclusion. In cases of acute unilateral facial weakness, a careful and systematic evaluation is necessary to identify the cause. Idiopathic facial paralysis (Bell's palsy) is a diagnosis of exclusion. It is also the most common cause of unilateral facial weakness seen by primary care physicians. The most important aspect of initial treatment is eye protection. Administration of systemic oral corticosteroids may lessen severity and duration of symptoms. Retained endobronchial foreign body removal facilitated by steroid therapy of an obstructing, inflammatory polyp. Oral and topical steroids were used to induce regression in an inflammatory, obstructing endobronchial polyp caused by a retained foreign body. The FB (a peanut half), which had been present for over six months, was then able to be easily and bloodlessly retrieved with fiberoptic bronchoscopy. Recurrent buccal space abscesses: a complication of Crohn's disease. A patient is described with generalized gastrointestinal involvement by Crohn's disease. Symptoms of recurrent ulceration and mucosal tags are well-described oral manifestations of Crohn's disease; however, in our patient recurrent facial abscesses, which required extraoral drainage, also developed. This complication has not previously been reported. Intracranial fibromatosis. Fibromatoses are uncommon infiltrative lesions affecting musculoaponeurotic structures, most often of the limbs and trunk. Lesions involving the cranial cavity are rare and require the same aggressive surgical management as elsewhere in the body. This case illustrates their clinical and neuroradiological features and underscores the necessity for aggressive resection to avoid recurrence. The literature is reviewed. The effect of intrathecal morphine on somatosensory evoked potentials in awake humans.[/INST]\n",
      " ### Response: \n",
      "• Limbal dermoids\n",
      "• Bell's palsy\n",
      "• Retained endobronchial foreign body\n",
      "• Recurrent buccal space abscesses\n",
      "• Intracranial fibromatosis</s>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Limbal dermoids': 'HP:0001140',\n",
       " \"Bell's palsy\": 'HP:0025331',\n",
       " 'Retained endobronchial foreign body': 'HP:0034806',\n",
       " 'Recurrent buccal space abscesses': 'HP:0002722',\n",
       " 'Intracranial fibromatosis': 'HP:0000169'}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = \"Excision of limbal dermoids. We reviewed the clinical files of 10 patients who had undergone excision of unilateral epibulbar limbal dermoids. Preoperatively, all of the affected eyes had worse visual acuity (P less than .02) and more astigmatism (P less than .01) than the contralateral eyes. Postoperatively, every patient was cosmetically improved. Of the eight patients for whom both preoperative and postoperative visual acuity measurements had been obtained, in six it had changed minimally (less than or equal to 1 line), and in two it had improved (less than or equal to 2 lines). Surgical complications included persistent epithelial defects (40%) and peripheral corneal vascularization and opacity (70%). These complications do not outweigh the cosmetic and visual benefits of dermoid excision in selected patients. Bell's palsy. A diagnosis of exclusion. In cases of acute unilateral facial weakness, a careful and systematic evaluation is necessary to identify the cause. Idiopathic facial paralysis (Bell's palsy) is a diagnosis of exclusion. It is also the most common cause of unilateral facial weakness seen by primary care physicians. The most important aspect of initial treatment is eye protection. Administration of systemic oral corticosteroids may lessen severity and duration of symptoms. Retained endobronchial foreign body removal facilitated by steroid therapy of an obstructing, inflammatory polyp. Oral and topical steroids were used to induce regression in an inflammatory, obstructing endobronchial polyp caused by a retained foreign body. The FB (a peanut half), which had been present for over six months, was then able to be easily and bloodlessly retrieved with fiberoptic bronchoscopy. Recurrent buccal space abscesses: a complication of Crohn's disease. A patient is described with generalized gastrointestinal involvement by Crohn's disease. Symptoms of recurrent ulceration and mucosal tags are well-described oral manifestations of Crohn's disease; however, in our patient recurrent facial abscesses, which required extraoral drainage, also developed. This complication has not previously been reported. Intracranial fibromatosis. Fibromatoses are uncommon infiltrative lesions affecting musculoaponeurotic structures, most often of the limbs and trunk. Lesions involving the cranial cavity are rare and require the same aggressive surgical management as elsewhere in the body. This case illustrates their clinical and neuroradiological features and underscores the necessity for aggressive resection to avoid recurrence. The literature is reviewed. The effect of intrathecal morphine on somatosensory evoked potentials in awake humans.\"\n",
    "raw_output = generate_output(text)\n",
    "print(raw_output)\n",
    "output = phenogpt_output(raw_output, termDB2vec)\n",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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