{ "cells": [ { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* Running on local URL: http://127.0.0.1:7866\n", "\n", "To create a public link, set `share=True` in `launch()`.\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "Fetching data from: https://clinicaltrials.gov/api/v2/studies?query.titles=glioma SEARCH[Location](AREA[LocationCountry]Japan AND AREA[LocationStatus]Recruiting)&pageSize=100\n" ] } ], "source": [ "import gradio as gr\n", "import pandas as pd\n", "from OpenAITools.FetchTools import fetch_clinical_trials\n", "from langchain_openai import ChatOpenAI\n", "from langchain_groq import ChatGroq\n", "from OpenAITools.CrinicalTrialTools import SimpleClinicalTrialAgent, GraderAgent, LLMTranslator, generate_ex_question_English\n", "\n", "# モデルとエージェントの初期化\n", "groq = ChatGroq(model_name=\"llama3-70b-8192\", temperature=0)\n", "translator = LLMTranslator(groq)\n", "CriteriaCheckAgent = SimpleClinicalTrialAgent(groq)\n", "grader_agent = GraderAgent(groq)\n", "\n", "# データフレームを生成する関数\n", "def generate_dataframe(age, sex, tumor_type, GeneMutation, Meseable, Biopsiable):\n", " # 日本語の腫瘍タイプを英語に翻訳\n", " TumorName = translator.translate(tumor_type)\n", "\n", " # 質問文を生成\n", " ex_question = generate_ex_question_English(age, sex, TumorName, GeneMutation, Meseable, Biopsiable)\n", " \n", " # 臨床試験データの取得\n", " df = fetch_clinical_trials(TumorName)\n", " df['AgentJudgment'] = None\n", " df['AgentGrade'] = None\n", " \n", " # 臨床試験の適格性の評価\n", " NCTIDs = list(df['NCTID'])\n", " progress = gr.Progress(track_tqdm=True)\n", " for i, nct_id in enumerate(NCTIDs):\n", " target_criteria = df.loc[df['NCTID'] == nct_id, 'Eligibility Criteria'].values[0]\n", " agent_judgment = CriteriaCheckAgent.evaluate_eligibility(target_criteria, ex_question)\n", " agent_grade = grader_agent.evaluate_eligibility(agent_judgment)\n", " \n", " # データフレームの更新\n", " df.loc[df['NCTID'] == nct_id, 'AgentJudgment'] = agent_judgment\n", " df.loc[df['NCTID'] == nct_id, 'AgentGrade'] = agent_grade\n", " progress((i + 1) / len(NCTIDs))\n", " \n", " # 列を指定した順に並び替え\n", " columns_order = ['NCTID', 'AgentGrade', 'Title', 'AgentJudgment', 'Japanes Locations', \n", " 'Primary Completion Date', 'Cancer', 'Summary', 'Eligibility Criteria']\n", " df = df[columns_order]\n", " \n", " return df, df # フィルタ用と表示用にデータフレームを返す\n", "\n", "# 特定のAgentGrade（yes, no, unclear）に基づいて行をフィルタリングする関数\n", "def filter_rows_by_grade(original_df, grade):\n", " df_filtered = original_df[original_df['AgentGrade'] == grade]\n", " return df_filtered, df_filtered\n", "\n", "# CSVとして保存しダウンロードする関数\n", "def download_filtered_csv(df):\n", " file_path = \"filtered_data.csv\"\n", " df.to_csv(file_path, index=False)\n", " return file_path\n", "\n", "# 全体結果をCSVとして保存しダウンロードする関数\n", "def download_full_csv(df):\n", " file_path = \"full_data.csv\"\n", " df.to_csv(file_path, index=False)\n", " return file_path\n", "\n", "# Gradioインターフェースの作成\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\"## 臨床試験適格性評価インターフェース\")\n", "\n", " # 各種入力フィールド\n", " age_input = gr.Textbox(label=\"Age\", placeholder=\"例: 65\")\n", " sex_input = gr.Dropdown(choices=[\"男性\", \"女性\"], label=\"Sex\")\n", " tumor_type_input = gr.Textbox(label=\"Tumor Type\", placeholder=\"例: gastric cancer, 日本でも良いですが英語の方が精度が高いです。\")\n", " gene_mutation_input = gr.Textbox(label=\"Gene Mutation\", placeholder=\"例: HER2\")\n", " measurable_input = gr.Dropdown(choices=[\"有り\", \"無し\", \"不明\"], label=\"Measurable Tumor\")\n", " biopsiable_input = gr.Dropdown(choices=[\"有り\", \"無し\", \"不明\"], label=\"Biopsiable Tumor\")\n", "\n", " # データフレーム表示エリア\n", " dataframe_output = gr.DataFrame()\n", " original_df = gr.State()\n", " filtered_df = gr.State()\n", "\n", " # データフレーム生成ボタン\n", " generate_button = gr.Button(\"Generate Clinical Trials Data\")\n", "\n", " # フィルタリングボタン\n", " yes_button = gr.Button(\"Show Eligible Trials\")\n", " no_button = gr.Button(\"Show Ineligible Trials\")\n", " unclear_button = gr.Button(\"Show Unclear Trials\")\n", " \n", " # ダウンロードボタン\n", " download_filtered_button = gr.Button(\"Download Filtered Data\")\n", " download_filtered_output = gr.File(label=\"Download Filtered Data\")\n", "\n", " download_full_button = gr.Button(\"Download Full Data\")\n", " download_full_output = gr.File(label=\"Download Full Data\")\n", "\n", "\n", " # ボタン動作の設定\n", " generate_button.click(fn=generate_dataframe, inputs=[age_input, sex_input, tumor_type_input, gene_mutation_input, measurable_input, biopsiable_input], outputs=[dataframe_output, original_df])\n", " yes_button.click(fn=filter_rows_by_grade, inputs=[original_df, gr.State(\"yes\")], outputs=[dataframe_output, filtered_df])\n", " no_button.click(fn=filter_rows_by_grade, inputs=[original_df, gr.State(\"no\")], outputs=[dataframe_output, filtered_df])\n", " unclear_button.click(fn=filter_rows_by_grade, inputs=[original_df, gr.State(\"unclear\")], outputs=[dataframe_output, filtered_df])\n", " download_filtered_button.click(fn=download_filtered_csv, inputs=filtered_df, outputs=download_filtered_output)\n", " download_full_button.click(fn=download_full_csv, inputs=original_df, outputs=download_full_output)\n", "\n", "\n", "# インターフェースの起動\n", "demo.launch()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "gradio", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 2 }