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ClinicalTrialV2
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/satoc/miniforge3/envs/gradio/lib/python3.12/site-packages/IPython/core/interactiveshell.py:3577: LangChainDeprecationWarning: As of langchain-core 0.3.0, LangChain uses pydantic v2 internally. The langchain_core.pydantic_v1 module was a compatibility shim for pydantic v1, and should no longer be used. Please update the code to import from Pydantic directly.\n",
      "\n",
      "For example, replace imports like: `from langchain_core.pydantic_v1 import BaseModel`\n",
      "with: `from pydantic import BaseModel`\n",
      "or the v1 compatibility namespace if you are working in a code base that has not been fully upgraded to pydantic 2 yet. \tfrom pydantic.v1 import BaseModel\n",
      "\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    }
   ],
   "source": [
    "from langchain_community.agent_toolkits import create_sql_agent\n",
    "from langchain_openai import ChatOpenAI\n",
    "from langchain_groq import ChatGroq\n",
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
    "import pandas as pd\n",
    "from pydantic import BaseModel, Field\n",
    "\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain_community.vectorstores import Chroma\n",
    "from langchain.embeddings import HuggingFaceEmbeddings\n",
    "from langchain_core.runnables import RunnablePassthrough\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "\n",
    "\n",
    "gpt = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
    "#agent_gpt_executor = create_sql_agent(gpt, db=db, agent_type=\"tool-calling\", verbose=True)\n",
    "\n",
    "## make database\n",
    "from langchain_community.utilities import SQLDatabase\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "from langchain.prompts import ChatPromptTemplate\n",
    "from langchain.schema import SystemMessage\n",
    "from langchain_core.prompts import MessagesPlaceholder\n",
    "#agent_groq_executor = create_sql_agent(llm, db=db, agent_type=\"tool-calling\", verbose=True)\n",
    "\n",
    "from OpenAITools.FetchTools import fetch_clinical_trials, fetch_clinical_trials_jp\n",
    "from OpenAITools.CrinicalTrialTools import QuestionModifierEnglish, TumorNameExtractor, SimpleClinicalTrialAgent,GraderAgent,LLMTranslator\n",
    "\n",
    "groq = ChatGroq(model_name=\"llama3-70b-8192\", temperature=0)\n",
    "#agent_groq_executor = create_sql_agent(groq, db=db, agent_type=\"tool-calling\", verbose=True)\n",
    "\n",
    "age = \"65\"\n",
    "sex =\"男性\"\n",
    "tumor_type =\"θƒƒη™Œ\"\n",
    "#tumor_type = \"gastric cancer\"\n",
    "GeneMutation =\"HER2\"\n",
    "Meseable = \"ζœ‰γ‚Š\"\n",
    "Biopsiable = \"ζœ‰γ‚Š\"\n",
    "NCTID = 'NCT06441994'\n",
    "\n",
    "#Define extractor\n",
    "Translator = LLMTranslator(groq)\n",
    "TumorName = Translator.translate(tumor_type)\n",
    "\n",
    "#Make db\n",
    "df = fetch_clinical_trials(TumorName)\n",
    "# ζ–°γ—γ„εˆ—γ‚’θΏ½εŠ \n",
    "df['AgentJudgment'] = None\n",
    "df['AgentGrade'] = None\n",
    "#df = df.iloc[:5, :]\n",
    "NCTIDs = list(df['NCTID' ])\n",
    "\n",
    "#Define agents\n",
    "#modifier = QuestionModifierEnglish(groq)\n",
    "CriteriaCheckAgent = SimpleClinicalTrialAgent(groq)\n",
    "grader_agent = GraderAgent(groq)\n",
    "\n",
    "\n",
    "for nct_id in NCTIDs:\n",
    "    TargetCriteria = df.loc[df['NCTID'] == nct_id, 'Eligibility Criteria'].values[0]\n",
    "    AgentJudgment = CriteriaCheckAgent.evaluate_eligibility(TargetCriteria, ex_question)\n",
    "    print(AgentJudgment)\n",
    "\n",
    "    AgentGrade = grader_agent.evaluate_eligibility(AgentJudgment)\n",
    "    print(AgentGrade)\n",
    "    # NCTIDγ«δΈ€θ‡΄γ™γ‚‹θ‘Œγ‚’θ¦‹γ€γ‘γ¦ζ›΄ζ–°\n",
    "    df.loc[df['NCTID'] == nct_id, 'AgentJudgment'] = AgentJudgment\n",
    "    df.loc[df['NCTID'] == nct_id, 'AgentGrade'] = AgentGrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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