Update app.py

app.py

@@ -1,46 +1,43 @@
 import gradio as gr
 from openai import OpenAI
-from datasets import Dataset
 from datetime import datetime, timedelta
 import pandas as pd
-import time, os, random, uuid, tempfile, json
+import os, random, tempfile, json
 import re
+import threading
 
 # --- API設定 ---
 API_KEY = os.getenv("API_KEY")
 BASE_URL = "https://openrouter.ai/api/v1"
-HF_TOKEN = os.getenv("HF_TOKEN")
-DATASET_REPO = "Toya0421/lexile_test_logging"
-LOG_FILE = "logs.csv"
+LOG_FILE = os.getenv("LOG_FILE", "logs.csv")
+
+# ✅ 管理者DL用パスワード（環境変数で設定）
+ADMIN_PASSWORD = os.getenv("ADMIN_PASSWORD", "")
 
 client = OpenAI(base_url=BASE_URL, api_key=API_KEY)
 
-# --- 外部CSVとして管理する passsage_id / passage データ ---
-# （例）columns: passage_id, lexile_level, text
+# --- 外部CSVとして管理する passage データ ---
 passages_df = pd.read_csv("passage.csv")
 
 levels = [1, 2, 3, 4, 5]
 
-# --- 状態 ---
-used_passages = set()
-question_count = 0
-current_user_id = None
-action_log = []  # ✅ 選択肢変更ログ
+# ✅ ログの同時書き込みガード（ログは全ユーザーで共有）
+_log_lock = threading.Lock()
 
 # --- AIで問題生成 ---
 def generate_question(text):
     prompt = f"""
-    Read the following passage and create ONE multiple-choice question with 4 options (A–D).
-    Only output the question and options.
-    Format:
-    Q: <question text>
-    A. <option>
-    B. <option>
-    C. <option>
-    D. <option>
-    Passage:
-    {text}
-    """
+Read the following passage and create ONE multiple-choice question with 4 options (A–D).
+Only output the question and options.
+Format:
+Q: <question text>
+A. <option>
+B. <option>
+C. <option>
+D. <option>
+Passage:
+{text}
+"""
     response = client.chat.completions.create(
         model="google/gemma-3-27b-it:free",
         messages=[{"role": "user", "content": prompt}],
@@ -52,14 +49,14 @@ def generate_question(text):
 # --- 正誤判定 ---
 def check_answer_with_ai(text, question, user_answer):
     prompt = f"""
-    Read the passage and question below. Decide if the user's answer is correct.
-    Passage:
-    {text}
-    Question:
-    {question}
-    User Answer: {user_answer}
-    Respond with one word: "Correct" or "Incorrect".
-    """
+Read the passage and question below. Decide if the user's answer is correct.
+Passage:
+{text}
+Question:
+{question}
+User Answer: {user_answer}
+Respond with one word: "Correct" or "Incorrect".
+"""
     response = client.chat.completions.create(
         model="google/gemma-3-27b-it:free",
         messages=[{"role": "user", "content": prompt}],
@@ -68,14 +65,11 @@ def check_answer_with_ai(text, question, user_answer):
     )
     result = response.choices[0].message.content.strip().lower()
 
-    # まず "incorrect" を検出（"incorrect" の中に "correct" が含まれるため先に判定する必要がある）
+    # "incorrect" の中に "correct" が含まれるため順序が重要
     if re.search(r"\bincorrect\b", result):
         return False
-    # 次に "correct" を検出
     if re.search(r"\bcorrect\b", result):
         return True
-
-    # どちらでも判断できない場合は安全側（不正解）
     return False
 
 # --- 自動難易度調整 ---
@@ -87,8 +81,8 @@ def adaptive_test(prev_level, prev_correct):
         return levels[idx - 1]
     return prev_level
 
-# --- passage取得 ---
-def get_passage(level):
+# --- passage取得（✅ used_passages は state から渡す） ---
+def get_passage(level, used_passages):
     subset = passages_df[passages_df["level"] == level]
     available = [pid for pid in subset["passage_id"] if pid not in used_passages]
     if not available:
@@ -97,83 +91,88 @@ def get_passage(level):
     text = subset[subset["passage_id"] == passage_id]["text"].iloc[0]
     return passage_id, text
 
-# --- ログ追記 & Push ---
-def log_to_csv_and_push(entry):
-    df = pd.DataFrame([entry])
-    if os.path.exists(LOG_FILE):
-        df.to_csv(LOG_FILE, mode="a", index=False, header=False)
-    else:
-        df.to_csv(LOG_FILE, index=False)
-
-    all_logs = pd.read_csv(LOG_FILE)
-    tmp_dir = tempfile.mkdtemp()
-    tmp_path = os.path.join(tmp_dir, "data.parquet")
-    all_logs.to_parquet(tmp_path)
-
-    dataset = Dataset.from_parquet(tmp_path)
-    dataset.push_to_hub(DATASET_REPO, token=HF_TOKEN)
+# --- ログ追記（✅ CSVのみ。Dataset保存はしない） ---
+def log_to_csv(entry):
+    with _log_lock:
+        df = pd.DataFrame([entry])
+        if os.path.exists(LOG_FILE):
+            df.to_csv(LOG_FILE, mode="a", index=False, header=False)
+        else:
+            df.to_csv(LOG_FILE, index=False)
+
+# --- セッション state 初期化 ---
+def _new_session_state():
+    return {
+        "used_passages": set(),
+        "question_count": 0,
+        "user_id": None,
+        "action_log": [],
+    }
 
 # --- 開始ボタン動作 ---
-def start_test(student_id):
-    global used_passages, question_count, current_user_id, action_log
-    used_passages = set()
-    question_count = 0
-    action_log = []
+def start_test(student_id, state):
+    # 新規開始時は常に初期化
+    state = _new_session_state()
 
     if not student_id or student_id.strip() == "":
         return (
+            state,
             "", "", 0, "", "",
             "⚠️ 学生番号を入力してからテストを開始してください",
             False, "", "", ""
         )
 
-    current_user_id = student_id.strip()
+    state["user_id"] = student_id.strip()
 
     level = 3
-    passage_id, text = get_passage(level)
-    used_passages.add(passage_id)
+    passage_id, text = get_passage(level, state["used_passages"])
+    state["used_passages"].add(passage_id)
 
     question = generate_question(text)
     displayed_time = datetime.utcnow() + timedelta(hours=9)
 
     return (
+        state,
         text, question, level, passage_id, "",
-        "", True, displayed_time.isoformat(), 1, current_user_id
+        "", True, displayed_time.isoformat(), 1, state["user_id"]
     )
 
-# --- 選択肢変更イベント（ここで操作履歴を保存） ---
-def log_choice_change(choice, question_number, user_id):
-    global action_log
+# --- 選択肢変更イベント（操作履歴を保存） ---
+def log_choice_change(choice, question_number, user_id, state):
+    if not isinstance(state, dict):
+        state = _new_session_state()
     if choice:
-        action_log.append({
+        state["action_log"].append({
             "action": "choice",
             "choice": choice,
             "time": (datetime.utcnow() + timedelta(hours=9)).isoformat()
         })
-    return
+    return state
 
 # --- 回答送信 ---
 def next_step(prev_level, user_answer, question_text, passage_text,
-              displayed_time, question_number, user_id, passage_id):
+              displayed_time, question_number, user_id, passage_id, state):
 
-    global question_count, used_passages, action_log
+    if not isinstance(state, dict):
+        state = _new_session_state()
 
     if not user_answer:
         return (
+            state,
             "⚠️ Please select an answer!", passage_text, question_text, prev_level,
             None, "", True, displayed_time, question_number, user_id, passage_id
         )
 
     submit_time = datetime.utcnow() + timedelta(hours=9)
-    question_count += 1
+    state["question_count"] += 1
 
     correct = check_answer_with_ai(passage_text, question_text, user_answer)
     new_level = adaptive_test(prev_level, correct)
 
-    # --- ✅ ログ保存 (actionsに選択履歴がすべて入る)
+    # ✅ ログ保存（actionsに選択履歴を入れる）
     entry = {
         "user_id": user_id,
-        "question_number": question_count,
+        "question_number": state["question_count"],
         "reading_level": prev_level,
         "passage_id": passage_id,
         "question": question_text,
@@ -181,41 +180,66 @@ def next_step(prev_level, user_answer, question_text, passage_text,
         "correct": correct,
         "displayed_time": displayed_time,
         "submit_time": submit_time.strftime("%Y-%m-%d %H:%M:%S"),
-        "actions": json.dumps(action_log, ensure_ascii=False)
+        "actions": json.dumps(state["action_log"], ensure_ascii=False)
     }
-    log_to_csv_and_push(entry)
+    log_to_csv(entry)
 
-    # ✅ 最終問題なら結果だけ大きく表示
-    if question_count >= 5:
+    # 最終問題なら結果だけ大きく表示
+    if state["question_count"] >= 5:
         final_level = prev_level if correct else new_level
         return (
+            state,
             f"<h1>🎯 Your Reading level: <strong>Level {final_level}</strong></h1>",
             "", "", final_level,
             None, "", False, "", "", user_id, passage_id
         )
 
-    # --- 次の問題へ ---
-    next_passage_id, next_text = get_passage(new_level)
-    used_passages.add(next_passage_id)
+    # 次の問題へ
+    next_passage_id, next_text = get_passage(new_level, state["used_passages"])
+    state["used_passages"].add(next_passage_id)
 
     next_question = generate_question(next_text)
     next_display_time = datetime.utcnow() + timedelta(hours=9)
 
     # ✅ 新しい問題のために選択操作ログをリセット
-    action_log = []
+    state["action_log"] = []
 
     feedback = "✅ Correct!" if correct else "❌ Incorrect."
 
     return (
+        state,
         feedback + "\n➡️ Loading next question…",
         next_text, next_question, new_level,
-        None, "", True, next_display_time.isoformat(), question_count + 1, user_id, next_passage_id
+        None, "", True, next_display_time.isoformat(), state["question_count"] + 1, user_id, next_passage_id
     )
 
+# --- 管理者DL（再起動までの logs.csv を返す） ---
+def download_logs(admin_password):
+    if not ADMIN_PASSWORD:
+        return None, "⚠️ ADMIN_PASSWORD が設定されていません（環境変数で設定してください）"
+
+    if (admin_password or "") != ADMIN_PASSWORD:
+        return None, "❌ Password incorrect."
+
+    with _log_lock:
+        if not os.path.exists(LOG_FILE):
+            return None, "⚠️ logs.csv がまだ存在しません（ログが1件もありません）"
+
+        # DL安定のため一時コピーして返す
+        tmp_dir = tempfile.mkdtemp()
+        out_path = os.path.join(tmp_dir, "logs.csv")
+        with open(LOG_FILE, "rb") as fsrc, open(out_path, "wb") as fdst:
+            fdst.write(fsrc.read())
+
+    return out_path, "✅ logs.csv is ready."
+
 # --- Gradio UI ---
 with gr.Blocks() as demo:
     gr.Markdown("# 📘 Reading Level Test")
 
+    # ✅ セッションごとの状態
+    session_state = gr.State(_new_session_state())
+
     student_id_input = gr.Textbox(label="Student ID", placeholder="例: B123456")
     start_btn = gr.Button("▶️ Start Test")
 
@@ -236,34 +260,36 @@ with gr.Blocks() as demo:
 
     start_btn.click(
         fn=start_test,
-        inputs=[student_id_input],
+        inputs=[student_id_input, session_state],
         outputs=[
+            session_state,
             text_display, question_display, hidden_level, hidden_passage_id, user_answer,
             feedback_display, test_visible, hidden_display_time,
             hidden_question_number, hidden_user_id
         ]
     )
 
-    # ✅ 選択肢変更ログ
+    # 選択肢変更ログ
     user_answer.change(
         fn=log_choice_change,
-        inputs=[user_answer, hidden_question_number, hidden_user_id],
-        outputs=[]
+        inputs=[user_answer, hidden_question_number, hidden_user_id, session_state],
+        outputs=[session_state]
     )
 
-    # ✅ 回答送信
+    # 回答送信
     submit_btn.click(
         fn=next_step,
         inputs=[hidden_level, user_answer, question_display, text_display,
-                hidden_display_time, hidden_question_number, hidden_user_id, hidden_passage_id],
+                hidden_display_time, hidden_question_number, hidden_user_id, hidden_passage_id, session_state],
         outputs=[
+            session_state,
             feedback_display, text_display, question_display, hidden_level,
             user_answer, hidden_passage, test_visible, hidden_display_time,
             hidden_question_number, hidden_user_id, hidden_passage_id
         ]
     )
 
-    # ✅ 表示のON/OFF制御
+    # 表示のON/OFF制御
     def toggle_visibility(show):
         v = bool(show)
         return (
@@ -277,4 +303,18 @@ with gr.Blocks() as demo:
         outputs=[text_display, question_display, user_answer, submit_btn]
     )
 
-demo.launch()
+    gr.Markdown("---")
+    gr.Markdown("## 🔒 Admin: Download logs (since last restart)")
+
+    admin_pw = gr.Textbox(label="Admin Password", type="password", placeholder="Enter admin password")
+    dl_btn = gr.Button("⬇️ Download logs.csv")
+    dl_file = gr.File(label="logs.csv (download)")
+    dl_msg = gr.Markdown()
+
+    dl_btn.click(
+        fn=download_logs,
+        inputs=[admin_pw],
+        outputs=[dl_file, dl_msg]
+    )
+
+demo.launch()