{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f986b468-c428-4ca3-9101-1cbabe6ad73f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from vllm import LLM, SamplingParams\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torch.nn.functional as F\n",
    "import torch\n",
    "from transformers import AutoTokenizer\n",
    "from transformers import AutoModelForCausalLM\n",
    "import re\n",
    "import os\n",
    "#os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e0537c8-85cc-4bae-97de-6dd6f70ea5a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "llama = LLM(model='hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4', tensor_parallel_size = 2, download_dir = \"../meta_ai/\", gpu_memory_utilization=0.80, max_model_len=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0394142-a749-4995-abc8-bac884fea671",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_synthetic_path_reports(num_reports, llama_model):\n",
    "\n",
    "    tokenizer = llama_model.get_tokenizer()\n",
    "    prompts = []\n",
    "    cancer_types = np.random.choice(['breast', 'non-small cell lung', 'small cell lung', 'colorectal', 'pancreatic', 'urothelial', 'prostate', 'gastric', 'esophageal', 'thymoma', 'thymic carcinoma', 'adrenal', 'ovarian', 'endometrial', 'melanoma', 'renal cell', 'sarcoma', 'head and neck', 'Hodgkin lymphoma', 'Non-Hodgkin lymphoma', 'myeloma', 'acute myeloid leukemia', 'chronic myeloid leukemia', 'acute lymphoblastic leukemia', 'chronic lymphocytic leukemia/lymphoma', 'primary brain tumor'], size=num_reports) \n",
    "\n",
    "    for i in range(num_reports):\n",
    "        messages = [\n",
    "            {'role':'system', 'content': \"\"\"Your job is to generate synthetic pathology reports for hypothetical patients with cancer.\n",
    "            You know all there is to know about cancer and its treatments, so be detailed.           \n",
    "        \"\"\"},      \n",
    "              \n",
    "            {'role':'user', 'content': \"\"\"Imagine a patient with cancer. \n",
    "            The cancer type is\"\"\" + cancer_types[i] + \".\" + \"\"\"\n",
    "            Then, generate a very detailed pathology report that might have been written about a specimen collected from the patient. The patient might have any stage of disease. Use everything you know about cancer, including biomarkers, epidemiology, and heterogeneity in disease presentations.\n",
    "            The report might be from a cytology specimen, anatomic pathology specimen, genomic sequencing analysis, bone marrow biopsy, flow cytometry, SPEP, etc.\n",
    "            The report should not include any treatment recommendations.\n",
    "            The pathology report should be approximately a full page long.\"\"\"}\n",
    "        ]\n",
    "    \n",
    "        prompts.append(tokenizer.apply_chat_template(conversation=messages, add_generation_prompt=True, tokenize=False))\n",
    "    \n",
    "\n",
    "    \n",
    "    responses = llama_model.generate(\n",
    "        prompts,   \n",
    "        SamplingParams(\n",
    "        temperature=1.0,\n",
    "        top_p=0.9,\n",
    "        max_tokens=4000,\n",
    "        stop_token_ids=[tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids(\"<|eot_id|>\")],  # KEYPOINT HERE\n",
    "    ))\n",
    "\n",
    "    response_texts = [x.outputs[0].text for x in responses]\n",
    "\n",
    "\n",
    "    return pd.DataFrame({'cancer_type':cancer_types, 'synthetic_path_report':response_texts})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "109f2208-de29-43cf-a831-4609bdab225e",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = generate_synthetic_path_reports(10000, llama)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b40b06f0-69e9-48cc-9766-61d8d26178d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "results.to_csv('synthetic_path_reports.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6119a0b4-5a63-4f91-a637-484b5e9dc29c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0470b150-dda1-4900-adb6-a90e5cf74e39",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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