llm_cpu.ipynb

{
 "nbformat": 4,
 "nbformat_minor": 0,
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "name": "python3",
   "language": "python",
   "display_name": "Python 3 (ipykernel)"
  },
  "language_info": {
   "name": "python"
  }
 },
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version\n",
      "3.9.18 (main, Sep 11 2023, 08:38:23) \n",
      "[Clang 14.0.6 ]\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "# Print the Python version\n",
    "print(\"Python version\")\n",
    "print(sys.version)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-01T10:36:43.394264Z",
     "start_time": "2023-12-01T10:36:43.380017Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ctransformers in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (0.2.27)\r\n",
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      "Requirement already satisfied: filelock in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from huggingface-hub->ctransformers) (3.13.1)\r\n",
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      "Requirement already satisfied: requests in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from huggingface-hub->ctransformers) (2.31.0)\r\n",
      "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from huggingface-hub->ctransformers) (4.66.1)\r\n",
      "Requirement already satisfied: pyyaml>=5.1 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from huggingface-hub->ctransformers) (6.0.1)\r\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from huggingface-hub->ctransformers) (4.8.0)\r\n",
      "Requirement already satisfied: packaging>=20.9 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from huggingface-hub->ctransformers) (23.2)\r\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from requests->huggingface-hub->ctransformers) (3.3.1)\r\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from requests->huggingface-hub->ctransformers) (3.4)\r\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from requests->huggingface-hub->ctransformers) (2.0.7)\r\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from requests->huggingface-hub->ctransformers) (2023.7.22)\r\n",
      "Requirement already satisfied: langchain in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (0.0.327)\r\n",
      "Requirement already satisfied: PyYAML>=5.3 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from langchain) (6.0.1)\r\n",
      "Requirement already satisfied: SQLAlchemy<3,>=1.4 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from langchain) (2.0.22)\r\n",
      "Requirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from langchain) (3.8.6)\r\n",
      "Requirement already satisfied: anyio<4.0 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from langchain) (3.7.1)\r\n",
      "Requirement already satisfied: async-timeout<5.0.0,>=4.0.0 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from langchain) (4.0.3)\r\n",
      "Requirement already satisfied: dataclasses-json<0.7,>=0.5.7 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from langchain) (0.6.1)\r\n",
      "Requirement already satisfied: jsonpatch<2.0,>=1.33 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from langchain) (1.33)\r\n",
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      "Requirement already satisfied: numpy<2,>=1 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from langchain) (1.26.1)\r\n",
      "Requirement already satisfied: pydantic<3,>=1 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from langchain) (2.4.2)\r\n",
      "Requirement already satisfied: requests<3,>=2 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from langchain) (2.31.0)\r\n",
      "Requirement already satisfied: tenacity<9.0.0,>=8.1.0 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from langchain) (8.2.3)\r\n",
      "Requirement already satisfied: attrs>=17.3.0 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (23.1.0)\r\n",
      "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (3.3.1)\r\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (6.0.4)\r\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.9.2)\r\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.4.0)\r\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.3.1)\r\n",
      "Requirement already satisfied: idna>=2.8 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from anyio<4.0->langchain) (3.4)\r\n",
      "Requirement already satisfied: sniffio>=1.1 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from anyio<4.0->langchain) (1.3.0)\r\n",
      "Requirement already satisfied: exceptiongroup in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from anyio<4.0->langchain) (1.1.3)\r\n",
      "Requirement already satisfied: marshmallow<4.0.0,>=3.18.0 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from dataclasses-json<0.7,>=0.5.7->langchain) (3.20.1)\r\n",
      "Requirement already satisfied: typing-inspect<1,>=0.4.0 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from dataclasses-json<0.7,>=0.5.7->langchain) (0.9.0)\r\n",
      "Requirement already satisfied: jsonpointer>=1.9 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from jsonpatch<2.0,>=1.33->langchain) (2.4)\r\n",
      "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from pydantic<3,>=1->langchain) (0.6.0)\r\n",
      "Requirement already satisfied: pydantic-core==2.10.1 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from pydantic<3,>=1->langchain) (2.10.1)\r\n",
      "Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from pydantic<3,>=1->langchain) (4.8.0)\r\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from requests<3,>=2->langchain) (2.0.7)\r\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from requests<3,>=2->langchain) (2023.7.22)\r\n",
      "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from SQLAlchemy<3,>=1.4->langchain) (3.0.1)\r\n",
      "Requirement already satisfied: packaging>=17.0 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from marshmallow<4.0.0,>=3.18.0->dataclasses-json<0.7,>=0.5.7->langchain) (23.2)\r\n",
      "Requirement already satisfied: mypy-extensions>=0.3.0 in /usr/local/anaconda3/envs/bert_topic/lib/python3.9/site-packages (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain) (1.0.0)\r\n",
      "zsh:1: 0.17.1 not found\r\n"
     ]
    }
   ],
   "source": [
    "! pip install ctransformers\n",
    "! pip install langchain\n",
    "! pip install huggingface-hub>=0.17.1"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-12-01T10:20:48.937006Z"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import huggingface_hub\n",
    "huggingface_hub.login(token='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')"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b8Cp6XZDcVlQ",
    "outputId": "c54400bd-7cac-46e6-c075-52e1cc0ca535",
    "ExecuteTime": {
     "end_time": "2023-12-01T10:21:03.368467Z",
     "start_time": "2023-12-01T10:21:02.688892Z"
    }
   },
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Token will not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n",
      "Token is valid (permission: write).\n",
      "Your token has been saved to /Users/kzaho/.cache/huggingface/token\n",
      "Login successful\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.1-GGUF mistral-7b-instruct-v0.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qWjnGI-WcVoG",
    "outputId": "48f6b7bd-11f1-4478-a9ae-a9a1c37fc6cc"
   },
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Consider using `hf_transfer` for faster downloads. This solution comes with some limitations. See https://huggingface.co/docs/huggingface_hub/hf_transfer for more details.\n",
      "downloading https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/mistral-7b-instruct-v0.1.Q4_K_M.gguf to /root/.cache/huggingface/hub/tmpfk8wc_t4\n",
      "mistral-7b-instruct-v0.1.Q4_K_M.gguf: 100% 4.37G/4.37G [00:45<00:00, 96.3MB/s]\n",
      "./mistral-7b-instruct-v0.1.Q4_K_M.gguf\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import os\n",
    "n_threads = os.cpu_count()\n",
    "n_threads"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-AWcavjsm768",
    "outputId": "cdf4d3ce-5c2e-4a8b-b708-7edfd67fb6ec",
    "ExecuteTime": {
     "end_time": "2023-12-01T10:21:28.741864Z",
     "start_time": "2023-12-01T10:21:28.736164Z"
    }
   },
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "12"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from ctransformers import AutoModelForCausalLM\n",
    "from transformers import pipeline\n",
    "\n",
    "# More info gere: https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF\n",
    "# Quant method: Q4_K_M, Bits: 4, Size: 4.37 GB, Max RAM required: 6.87 GB, Use case: medium, balanced quality - recommended\n",
    "model_location = './mistral-7b-instruct-v0.1.Q4_K_M.gguf'\n",
    "\n",
    "# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.\n",
    "llm = AutoModelForCausalLM.from_pretrained(\n",
    "    model_location,\n",
    "    model_type=\"mistral\",\n",
    "    gpu_layers=0,\n",
    "    threads=n_threads\n",
    ")"
   ],
   "metadata": {
    "id": "QuD8djuinUi7",
    "ExecuteTime": {
     "end_time": "2023-12-01T10:21:40.903172Z",
     "start_time": "2023-12-01T10:21:32.355184Z"
    }
   },
   "execution_count": 6,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "print(llm(\"AI is going to\"))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rLHAC4YWcVqJ",
    "outputId": "af36ae2d-1d18-4f7d-ee82-b508d85d65ad",
    "ExecuteTime": {
     "end_time": "2023-12-01T10:23:16.055633Z",
     "start_time": "2023-12-01T10:21:40.904851Z"
    }
   },
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " revolutionize the way we live and work. We've already seen its impact on industries like healthcare, finance, and retail, but there are many more areas where it can make a huge difference.\n",
      "One such area is transportation. Self-driving cars have already started to hit the road, and they have the potential to transform the way we move goods and people around our cities. They could help reduce traffic congestion, improve safety, and increase productivity.\n",
      "But there are also concerns about job loss, privacy, and the ethics of autonomous vehicles. As AI continues to advance, it's important that we address these issues and ensure that it benefits everyone in a responsible way.\n",
      "Overall, AI has the potential to bring about significant change and improvement in many areas of our lives. It's up to us to harness its power for good and make sure that it benefits society as a whole.\n",
      "CPU times: user 14min 33s, sys: 16.9 s, total: 14min 50s\n",
      "Wall time: 1min 35s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# More about data: https://www.kaggle.com/datasets/tboyle10/medicaltranscriptions\n",
    "\n",
    "# Specify the file path (this path should point to where your file is stored)\n",
    "BASE_PATH = './'\n",
    "file_path = BASE_PATH + 'mtsamples.csv'\n",
    "\n",
    "# Read data as pandas DataFrame\n",
    "data = pd.read_csv(file_path, index_col=0)"
   ],
   "metadata": {
    "id": "sP4d3ah5cVuU",
    "ExecuteTime": {
     "end_time": "2023-12-01T10:23:52.434837Z",
     "start_time": "2023-12-01T10:23:50.189130Z"
    }
   },
   "execution_count": 8,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "data.head()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 293
    },
    "id": "TU8KVTIzcpXa",
    "outputId": "a5cba241-2bfc-4ac0-84cb-a1856e1ff80e",
    "ExecuteTime": {
     "end_time": "2023-12-01T10:23:52.540544Z",
     "start_time": "2023-12-01T10:23:52.440204Z"
    }
   },
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "                                         description  \\\n0   A 23-year-old white female presents with comp...   \n1           Consult for laparoscopic gastric bypass.   \n2           Consult for laparoscopic gastric bypass.   \n3                             2-D M-Mode. Doppler.     \n4                                 2-D Echocardiogram   \n\n             medical_specialty                                sample_name  \\\n0         Allergy / Immunology                         Allergic Rhinitis    \n1                   Bariatrics   Laparoscopic Gastric Bypass Consult - 2    \n2                   Bariatrics   Laparoscopic Gastric Bypass Consult - 1    \n3   Cardiovascular / Pulmonary                    2-D Echocardiogram - 1    \n4   Cardiovascular / Pulmonary                    2-D Echocardiogram - 2    \n\n                                       transcription  \\\n0  SUBJECTIVE:,  This 23-year-old white female pr...   \n1  PAST MEDICAL HISTORY:, He has difficulty climb...   \n2  HISTORY OF PRESENT ILLNESS: , I have seen ABC ...   \n3  2-D M-MODE: , ,1.  Left atrial enlargement wit...   \n4  1.  The left ventricular cavity size and wall ...   \n\n                                            keywords  \n0  allergy / immunology, allergic rhinitis, aller...  \n1  bariatrics, laparoscopic gastric bypass, weigh...  \n2  bariatrics, laparoscopic gastric bypass, heart...  \n3  cardiovascular / pulmonary, 2-d m-mode, dopple...  \n4  cardiovascular / pulmonary, 2-d, doppler, echo...  ",
      "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>description</th>\n      <th>medical_specialty</th>\n      <th>sample_name</th>\n      <th>transcription</th>\n      <th>keywords</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A 23-year-old white female presents with comp...</td>\n      <td>Allergy / Immunology</td>\n      <td>Allergic Rhinitis</td>\n      <td>SUBJECTIVE:,  This 23-year-old white female pr...</td>\n      <td>allergy / immunology, allergic rhinitis, aller...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Consult for laparoscopic gastric bypass.</td>\n      <td>Bariatrics</td>\n      <td>Laparoscopic Gastric Bypass Consult - 2</td>\n      <td>PAST MEDICAL HISTORY:, He has difficulty climb...</td>\n      <td>bariatrics, laparoscopic gastric bypass, weigh...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Consult for laparoscopic gastric bypass.</td>\n      <td>Bariatrics</td>\n      <td>Laparoscopic Gastric Bypass Consult - 1</td>\n      <td>HISTORY OF PRESENT ILLNESS: , I have seen ABC ...</td>\n      <td>bariatrics, laparoscopic gastric bypass, heart...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2-D M-Mode. Doppler.</td>\n      <td>Cardiovascular / Pulmonary</td>\n      <td>2-D Echocardiogram - 1</td>\n      <td>2-D M-MODE: , ,1.  Left atrial enlargement wit...</td>\n      <td>cardiovascular / pulmonary, 2-d m-mode, dopple...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2-D Echocardiogram</td>\n      <td>Cardiovascular / Pulmonary</td>\n      <td>2-D Echocardiogram - 2</td>\n      <td>1.  The left ventricular cavity size and wall ...</td>\n      <td>cardiovascular / pulmonary, 2-d, doppler, echo...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Case: Medical specialty classification"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": [
    "data['medical_specialty'].value_counts(normalize=True).head()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GCetCvuOdxpi",
    "outputId": "73781203-e393-4ae4-9b41-c37fcca37679",
    "ExecuteTime": {
     "end_time": "2023-12-01T10:23:55.624057Z",
     "start_time": "2023-12-01T10:23:55.614843Z"
    }
   },
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "medical_specialty\n Surgery                       0.220644\n Consult - History and Phy.    0.103221\n Cardiovascular / Pulmonary    0.074415\n Orthopedic                    0.071014\n Radiology                     0.054611\nName: proportion, dtype: float64"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "specialties = data['medical_specialty'].unique()"
   ],
   "metadata": {
    "id": "aH3C5IflcpaE",
    "ExecuteTime": {
     "end_time": "2023-12-01T10:23:57.635443Z",
     "start_time": "2023-12-01T10:23:57.619767Z"
    }
   },
   "execution_count": 11,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "specialties_text = '\\n - '+'\\n - '.join(specialties)\n",
    "print(specialties_text[:200],'\\n...')"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-rkOK6MUfPBT",
    "outputId": "2ac90234-c581-4fef-97bd-8d315a3801c9",
    "ExecuteTime": {
     "end_time": "2023-12-01T10:23:59.249485Z",
     "start_time": "2023-12-01T10:23:59.230372Z"
    }
   },
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " -  Allergy / Immunology\n",
      " -  Bariatrics\n",
      " -  Cardiovascular / Pulmonary\n",
      " -  Neurology\n",
      " -  Dentistry\n",
      " -  Urology\n",
      " -  General Medicine\n",
      " -  Surgery\n",
      " -  Speech - Language\n",
      " -  SOAP / Chart / Progress Notes \n",
      "...\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# Define prompt\n",
    "prompt_template = \"\"\"\n",
    "You are a nurse reading treatment notes and trying to figure out what topic is discussed and will return the result in JSON output:\n",
    "\n",
    "You are looking for below topics:\"\"\"\n",
    "prompt_template += specialties_text\n",
    "\n",
    "prompt_template += \"\"\"\n",
    "\n",
    "Requirements:\n",
    "• Read the notes carefully and see if the note is talking about the topics you know.\n",
    "• If the note is not talking about the topics you know, figure out it shows other topics.\n",
    "• Present your analysis in a JSON format. The format should be {{\"topic\": \"identified topic or new topic\"}}.\n",
    "\n",
    "Treatment notes to analyze:\n",
    "{text}\n",
    "\"\"\"\n",
    "print(prompt_template)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hXFe6MQ3fBre",
    "outputId": "5ce6e1f1-e4b7-4329-9328-b119a06f7830",
    "ExecuteTime": {
     "end_time": "2023-12-01T10:24:01.850435Z",
     "start_time": "2023-12-01T10:24:01.844006Z"
    }
   },
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "You are a nurse reading treatment notes and trying to figure out what topic is discussed and will return the result in JSON output:\n",
      "\n",
      "You are looking for below topics:\n",
      " -  Allergy / Immunology\n",
      " -  Bariatrics\n",
      " -  Cardiovascular / Pulmonary\n",
      " -  Neurology\n",
      " -  Dentistry\n",
      " -  Urology\n",
      " -  General Medicine\n",
      " -  Surgery\n",
      " -  Speech - Language\n",
      " -  SOAP / Chart / Progress Notes\n",
      " -  Sleep Medicine\n",
      " -  Rheumatology\n",
      " -  Radiology\n",
      " -  Psychiatry / Psychology\n",
      " -  Podiatry\n",
      " -  Physical Medicine - Rehab\n",
      " -  Pediatrics - Neonatal\n",
      " -  Pain Management\n",
      " -  Orthopedic\n",
      " -  Ophthalmology\n",
      " -  Office Notes\n",
      " -  Obstetrics / Gynecology\n",
      " -  Neurosurgery\n",
      " -  Nephrology\n",
      " -  Letters\n",
      " -  Lab Medicine - Pathology\n",
      " -  IME-QME-Work Comp etc.\n",
      " -  Hospice - Palliative Care\n",
      " -  Hematology - Oncology\n",
      " -  Gastroenterology\n",
      " -  ENT - Otolaryngology\n",
      " -  Endocrinology\n",
      " -  Emergency Room Reports\n",
      " -  Discharge Summary\n",
      " -  Diets and Nutritions\n",
      " -  Dermatology\n",
      " -  Cosmetic / Plastic Surgery\n",
      " -  Consult - History and Phy.\n",
      " -  Chiropractic\n",
      " -  Autopsy\n",
      "\n",
      "Requirements:\n",
      "• Read the notes carefully and see if the note is talking about the topics you know.\n",
      "• If the note is not talking about the topics you know, figure out it shows other topics.\n",
      "• Present your analysis in a JSON format. The format should be {{\"topic\": \"identified topic or new topic\"}}.\n",
      "\n",
      "Treatment notes to analyze:\n",
      "{text}\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "patient_notes = data['transcription'].loc[0]\n",
    "patient_notes"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "ouCTV0MxeIJq",
    "outputId": "79585d34-70ca-4baa-d4ab-e6472c20f830",
    "ExecuteTime": {
     "end_time": "2023-12-01T10:24:32.512731Z",
     "start_time": "2023-12-01T10:24:32.503587Z"
    }
   },
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "'SUBJECTIVE:,  This 23-year-old white female presents with complaint of allergies.  She used to have allergies when she lived in Seattle but she thinks they are worse here.  In the past, she has tried Claritin, and Zyrtec.  Both worked for short time but then seemed to lose effectiveness.  She has used Allegra also.  She used that last summer and she began using it again two weeks ago.  It does not appear to be working very well.  She has used over-the-counter sprays but no prescription nasal sprays.  She does have asthma but doest not require daily medication for this and does not think it is flaring up.,MEDICATIONS: , Her only medication currently is Ortho Tri-Cyclen and the Allegra.,ALLERGIES: , She has no known medicine allergies.,OBJECTIVE:,Vitals:  Weight was 130 pounds and blood pressure 124/78.,HEENT:  Her throat was mildly erythematous without exudate.  Nasal mucosa was erythematous and swollen.  Only clear drainage was seen.  TMs were clear.,Neck:  Supple without adenopathy.,Lungs:  Clear.,ASSESSMENT:,  Allergic rhinitis.,PLAN:,1.  She will try Zyrtec instead of Allegra again.  Another option will be to use loratadine.  She does not think she has prescription coverage so that might be cheaper.,2.  Samples of Nasonex two sprays in each nostril given for three weeks.  A prescription was written as well.'"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8min 8s, sys: 5.07 s, total: 8min 13s\n",
      "Wall time: 1min 25s\n"
     ]
    },
    {
     "data": {
      "text/plain": "'{\\n\"topic\": \"Allergy / Immunology\"\\n}'"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "from langchain.llms import CTransformers\n",
    "\n",
    "# Define LLM chain\n",
    "config = {\n",
    "    'context_length': 1024,\n",
    "    'max_new_tokens': 64, # You don`t need to many tokes for topic only\n",
    "    'top_p': 0.95,\n",
    "    'top_k': 50,\n",
    "    'temperature': 0.01,\n",
    "    'stream': False\n",
    "    }\n",
    "llm = CTransformers(model=model_location, model_type='mistral', config=config)\n",
    "\n",
    "response = llm(f'''<|prompter|>:{prompt_template.format(text=patient_notes)}\\n<|assistant|>:JSON:\\n''')\n",
    "response"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-01T10:29:53.276688Z",
     "start_time": "2023-12-01T10:28:27.304517Z"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "eval(response)"
   ],
   "metadata": {
    "id": "-4SJ4z3ZeIPf",
    "ExecuteTime": {
     "end_time": "2023-12-01T10:30:30.994636Z",
     "start_time": "2023-12-01T10:30:30.972961Z"
    }
   },
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "{'topic': 'Allergy / Immunology'}"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "' Allergy / Immunology'"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['medical_specialty'].loc[0]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-01T10:30:48.721415Z",
     "start_time": "2023-12-01T10:30:48.713344Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Case: Medical specialty classification & keywords"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "You are a nurse reading treatment notes and trying to figure out what topic is discussed and will return the result in JSON output:\n",
      "\n",
      "You are looking for below topics:\n",
      " -  Allergy / Immunology\n",
      " -  Bariatrics\n",
      " -  Cardiovascular / Pulmonary\n",
      " -  Neurology\n",
      " -  Dentistry\n",
      " -  Urology\n",
      " -  General Medicine\n",
      " -  Surgery\n",
      " -  Speech - Language\n",
      " -  SOAP / Chart / Progress Notes\n",
      " -  Sleep Medicine\n",
      " -  Rheumatology\n",
      " -  Radiology\n",
      " -  Psychiatry / Psychology\n",
      " -  Podiatry\n",
      " -  Physical Medicine - Rehab\n",
      " -  Pediatrics - Neonatal\n",
      " -  Pain Management\n",
      " -  Orthopedic\n",
      " -  Ophthalmology\n",
      " -  Office Notes\n",
      " -  Obstetrics / Gynecology\n",
      " -  Neurosurgery\n",
      " -  Nephrology\n",
      " -  Letters\n",
      " -  Lab Medicine - Pathology\n",
      " -  IME-QME-Work Comp etc.\n",
      " -  Hospice - Palliative Care\n",
      " -  Hematology - Oncology\n",
      " -  Gastroenterology\n",
      " -  ENT - Otolaryngology\n",
      " -  Endocrinology\n",
      " -  Emergency Room Reports\n",
      " -  Discharge Summary\n",
      " -  Diets and Nutritions\n",
      " -  Dermatology\n",
      " -  Cosmetic / Plastic Surgery\n",
      " -  Consult - History and Phy.\n",
      " -  Chiropractic\n",
      " -  Autopsy\n",
      "\n",
      "Requirements:\n",
      "• Read the notes carefully and see if the note is talking about the topics you know.\n",
      "• If the note is not talking about the topics you know, figure out it shows other topics.\n",
      "• Extract keywords from the notes that are general and indicative of the identified topic.\n",
      "• Make sure that these keywords are written at treatment notes, don't include numeric numbers.\n",
      "• Please, fill the confidence level about the chosen topic within the next options: very confident, need more info, unconfident, not enough data.\n",
      "• Present your analysis in a JSON format. The format should be {{\"topic\": \"identified topic or new topic\", \"keywords\": [\"keyword1\", \"keyword2\", ...], \"confidence level\": \"very confident|need more info|unconfident|not enough data\", \"explanation\": \"briefly describe your analysis\"}}.\n",
      "\n",
      "\n",
      "Treatment notes to analyze:\n",
      "{text}\n"
     ]
    }
   ],
   "source": [
    "# Define prompt\n",
    "prompt_template = \"\"\"\n",
    "You are a nurse reading treatment notes and trying to figure out what topic is discussed and will return the result in JSON output:\n",
    "\n",
    "You are looking for below topics:\"\"\"\n",
    "prompt_template += specialties_text\n",
    "\n",
    "prompt_template += \"\"\"\n",
    "\n",
    "Requirements:\n",
    "• Read the notes carefully and see if the note is talking about the topics you know.\n",
    "• If the note is not talking about the topics you know, figure out it shows other topics.\n",
    "• Extract keywords from the notes that are general and indicative of the identified topic.\n",
    "• Make sure that these keywords are written at treatment notes, don't include numeric numbers.\n",
    "• Please, fill the confidence level about the chosen topic within the next options: very confident, need more info, unconfident, not enough data.\n",
    "• Present your analysis in a JSON format. The format should be {{\"topic\": \"identified topic or new topic\", \"keywords\": [\"keyword1\", \"keyword2\", ...], \"confidence level\": \"very confident|need more info|unconfident|not enough data\", \"explanation\": \"briefly describe your analysis\"}}.\n",
    "\n",
    "\n",
    "Treatment notes to analyze:\n",
    "{text}\n",
    "\"\"\"\n",
    "print(prompt_template)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-01T10:33:47.328138Z",
     "start_time": "2023-12-01T10:33:47.319800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9min 39s, sys: 8.51 s, total: 9min 48s\n",
      "Wall time: 1min 44s\n"
     ]
    },
    {
     "data": {
      "text/plain": "'{\\n\"topic\": \"Allergy / Immunology\",\\n\"keywords\": [\"allergies\", \"rhinitis\", \"nasal sprays\"],\\n\"confidence level\": \"very confident\",\\n\"explanation\": \"The patient presents with a complaint of allergies and has tried different medications for it. The physician diagnoses her with allergic rhinitis and plans to give her nasal sprays as treatment.\"\\n}'"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "from langchain.llms import CTransformers\n",
    "\n",
    "# Define LLM chain\n",
    "config = {\n",
    "    'context_length': 2048,\n",
    "    'max_new_tokens': 256, # Here we added tokens number, because the expected output becomes bigger\n",
    "    'top_p': 0.95,\n",
    "    'top_k': 50,\n",
    "    'temperature': 0.01,\n",
    "    'stream': False\n",
    "    }\n",
    "llm = CTransformers(model=model_location, model_type='mistral', config=config)\n",
    "\n",
    "response = llm(f'''<|prompter|>:{prompt_template.format(text=patient_notes)}\\n<|assistant|>:JSON:\\n''')\n",
    "response"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-01T10:49:12.848753Z",
     "start_time": "2023-12-01T10:47:28.100938Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "{'topic': 'Allergy / Immunology',\n 'keywords': ['allergies', 'rhinitis', 'nasal sprays'],\n 'confidence level': 'very confident',\n 'explanation': 'The patient presents with a complaint of allergies and has tried different medications for it. The physician diagnoses her with allergic rhinitis and plans to give her nasal sprays as treatment.'}"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval(response)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-01T10:49:47.951023Z",
     "start_time": "2023-12-01T10:49:47.938101Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "{'description': ' A 23-year-old white female presents with complaint of allergies.',\n 'medical_specialty': ' Allergy / Immunology',\n 'keywords': 'allergy / immunology, allergic rhinitis, allergies, asthma, nasal sprays, rhinitis, nasal, erythematous, allegra, sprays, allergic,'}"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[0][['description', 'medical_specialty', 'keywords']].to_dict()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-01T10:50:36.386839Z",
     "start_time": "2023-12-01T10:50:36.373106Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ]
}