before major change

.gitignore

@@ -7,7 +7,7 @@ __pycache__/
 #*$py.class
 #*.txt
 #*.tsv
-#*.csv
+*.csv
 *.xlsx
 *.pdf
 *.nii

app copy.py

@@ -0,0 +1,106 @@
+import gradio as gr
+import pandas as pd
+from OpenAITools.FetchTools import fetch_clinical_trials
+from langchain_openai import ChatOpenAI
+from langchain_groq import ChatGroq
+from OpenAITools.CrinicalTrialTools import SimpleClinicalTrialAgent, GraderAgent, LLMTranslator, generate_ex_question_English
+
+# モデルとエージェントの初期化
+groq = ChatGroq(model_name="llama3-70b-8192", temperature=0)
+translator = LLMTranslator(groq)
+CriteriaCheckAgent = SimpleClinicalTrialAgent(groq)
+grader_agent = GraderAgent(groq)
+
+# データフレームを生成する関数
+def generate_dataframe(age, sex, tumor_type, GeneMutation, Meseable, Biopsiable):
+    # 日本語の腫瘍タイプを英語に翻訳
+    TumorName = translator.translate(tumor_type)
+
+    # 質問文を生成
+    ex_question = generate_ex_question_English(age, sex, TumorName, GeneMutation, Meseable, Biopsiable)
+    
+    # 臨床試験データの取得
+    df = fetch_clinical_trials(TumorName)
+    df['AgentJudgment'] = None
+    df['AgentGrade'] = None
+    
+    # 臨床試験の適格性の評価
+    NCTIDs = list(df['NCTID'])
+    progress = gr.Progress(track_tqdm=True)
+    for i, nct_id in enumerate(NCTIDs):
+        target_criteria = df.loc[df['NCTID'] == nct_id, 'Eligibility Criteria'].values[0]
+        agent_judgment = CriteriaCheckAgent.evaluate_eligibility(target_criteria, ex_question)
+        agent_grade = grader_agent.evaluate_eligibility(agent_judgment)
+        
+        # データフレームの更新
+        df.loc[df['NCTID'] == nct_id, 'AgentJudgment'] = agent_judgment
+        df.loc[df['NCTID'] == nct_id, 'AgentGrade'] = agent_grade
+        progress((i + 1) / len(NCTIDs))
+    
+    # 列を指定した順に並び替え
+    columns_order = ['NCTID', 'AgentGrade', 'Title', 'AgentJudgment', 'Japanes Locations', 
+                     'Primary Completion Date', 'Cancer', 'Summary', 'Eligibility Criteria']
+    df = df[columns_order]
+        
+    return df, df  # フィルタ用と表示用にデータフレームを返す
+
+# 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
+def filter_rows_by_grade(original_df, grade):
+    df_filtered = original_df[original_df['AgentGrade'] == grade]
+    return df_filtered, df_filtered
+
+# CSVとして保存しダウンロードする関数
+def download_filtered_csv(df):
+    file_path = "filtered_data.csv"
+    df.to_csv(file_path, index=False)
+    return file_path
+
+# 全体結果をCSVとして保存しダウンロードする関数
+def download_full_csv(df):
+    file_path = "full_data.csv"
+    df.to_csv(file_path, index=False)
+    return file_path
+
+# Gradioインターフェースの作成
+with gr.Blocks() as demo:
+    gr.Markdown("## 臨床試験適格性評価インターフェース")
+
+    # 各種入力フィールド
+    age_input = gr.Textbox(label="Age", placeholder="例: 65")
+    sex_input = gr.Dropdown(choices=["男性", "女性"], label="Sex")
+    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。")
+    gene_mutation_input = gr.Textbox(label="Gene Mutation", placeholder="例: HER2")
+    measurable_input = gr.Dropdown(choices=["有り", "無し", "不明"], label="Measurable Tumor")
+    biopsiable_input = gr.Dropdown(choices=["有り", "無し", "不明"], label="Biopsiable Tumor")
+
+    # データフレーム表示エリア
+    dataframe_output = gr.DataFrame()
+    original_df = gr.State()
+    filtered_df = gr.State()
+
+    # データフレーム生成ボタン
+    generate_button = gr.Button("Generate Clinical Trials Data")
+
+    # フィルタリングボタン
+    yes_button = gr.Button("Show Eligible Trials")
+    no_button = gr.Button("Show Ineligible Trials")
+    unclear_button = gr.Button("Show Unclear Trials")
+    
+    # ダウンロードボタン
+    download_filtered_button = gr.Button("Download Filtered Data")
+    download_filtered_output = gr.File(label="Download Filtered Data")
+
+    download_full_button = gr.Button("Download Full Data")
+    download_full_output = gr.File(label="Download Full Data")
+
+
+    # ボタン動作の設定
+    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])
+    yes_button.click(fn=filter_rows_by_grade, inputs=[original_df, gr.State("yes")], outputs=[dataframe_output, filtered_df])
+    no_button.click(fn=filter_rows_by_grade, inputs=[original_df, gr.State("no")], outputs=[dataframe_output, filtered_df])
+    unclear_button.click(fn=filter_rows_by_grade, inputs=[original_df, gr.State("unclear")], outputs=[dataframe_output, filtered_df])
+    download_filtered_button.click(fn=download_filtered_csv, inputs=filtered_df, outputs=download_filtered_output)
+    download_full_button.click(fn=download_full_csv, inputs=original_df, outputs=download_full_output)
+    
+if __name__ == "__main__":
+    demo.launch()