Update app.py

app.py

@@ -1,109 +1,182 @@
-import os
-import random
 import gradio as gr
+from openai import OpenAI
+import random
 
-# OpenRouter APIキーを指定
+# ✅ OpenRouter APIキー（ここに直接書く）
 API_KEY = "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"
-
-# Lexileレベルごとの文章セット（各3問）
-texts_by_level = {
-    400: [
-        {"text": "Tom has a little dog. The dog likes to run in the park.", "question": "What does Tom's dog like to do?", "options": ["Run in the park", "Sleep all day", "Eat candy", "Climb trees"], "answer": "Run in the park"},
-        {"text": "The sun is bright today. Many children are playing outside.", "question": "Where are the children playing?", "options": ["Outside", "In school", "At night", "In the house"], "answer": "Outside"},
-        {"text": "Anna has a red ball. She throws it to her friend in the garden.", "question": "Where does Anna throw the ball?", "options": ["In the garden", "In the kitchen", "In the school", "In the store"], "answer": "In the garden"},
+BASE_URL = "https://openrouter.ai/api/v1"
+
+# --- Lexile難易度別教材（各段階3つずつ） ---
+texts = {
+    300: [
+        "Tom has a red ball. He plays with it in the park. The sun is bright.",
+        "Anna likes cats. She feeds them milk every morning.",
+        "The boy sees a bird in the tree. It sings a happy song."
     ],
     600: [
-        {"text": "Sara goes to the library every Saturday. She likes to read mystery books.", "question": "What kind of books does Sara like?", "options": ["Mystery books", "Science books", "History books", "Cooking books"], "answer": "Mystery books"},
-        {"text": "A frog can jump far. It uses its strong legs to move fast.", "question": "What helps the frog jump far?", "options": ["Its strong legs", "Its arms", "Its eyes", "Its color"], "answer": "Its strong legs"},
-        {"text": "Ben plants some seeds in the garden. After a few days, small plants grow.", "question": "What happens after a few days?", "options": ["Small plants grow", "The seeds disappear", "It starts to snow", "The garden becomes empty"], "answer": "Small plants grow"},
+        "A young boy found a lost puppy near the river. He decided to take care of it.",
+        "Lisa visits her grandmother every weekend. They bake cookies together.",
+        "The farmer plants rice in the spring and harvests it in the autumn."
     ],
-    800: [
-        {"text": "David wanted to learn how machines work. He took apart an old radio to see the parts inside.", "question": "Why did David take apart the radio?", "options": ["To see the parts inside", "To break it", "To sell it", "To clean it"], "answer": "To see the parts inside"},
-        {"text": "In winter, many animals rest or sleep for a long time. This is called hibernation.", "question": "What do animals do during hibernation?", "options": ["Sleep for a long time", "Eat more food", "Play outside", "Travel to other countries"], "answer": "Sleep for a long time"},
-        {"text": "Lisa looked through the telescope and saw bright stars in the night sky.", "question": "What did Lisa use to see the stars?", "options": ["A telescope", "A microscope", "A flashlight", "A magnifier"], "answer": "A telescope"},
+    850: [
+        "Sarah enjoyed reading stories about ancient civilizations and their discoveries.",
+        "Tom built a small robot that could move and pick up light objects.",
+        "Maria was fascinated by how airplanes could stay in the air for hours."
     ],
-    1000: [
-        {"text": "Plants use sunlight to make food. This process is called photosynthesis.", "question": "What do plants use to make food?", "options": ["Sunlight", "Wind", "Soil", "Rain"], "answer": "Sunlight"},
-        {"text": "Electric cars are better for the environment because they produce less pollution.", "question": "Why are electric cars better for the environment?", "options": ["They produce less pollution", "They are faster", "They use gasoline", "They make noise"], "answer": "They produce less pollution"},
-        {"text": "The computer stores data in its memory. This allows users to save their work.", "question": "What does the computer store in its memory?", "options": ["Data", "Food", "Games", "Pictures only"], "answer": "Data"},
+    1050: [
+        "The scientist developed a new hypothesis about the evolution of animal behavior.",
+        "A group of engineers designed a bridge that could resist strong winds.",
+        "Students discussed how technology changes communication in modern society."
     ],
-    1200: [
-        {"text": "The scientist observed how the new medicine affected the cells under a microscope.", "question": "What did the scientist use to see the cells?", "options": ["A microscope", "A telescope", "A camera", "A ruler"], "answer": "A microscope"},
-        {"text": "During the experiment, the temperature was carefully controlled to ensure accurate results.", "question": "Why was the temperature controlled?", "options": ["To ensure accurate results", "To make it hotter", "To test faster", "To make it colder"], "answer": "To ensure accurate results"},
-        {"text": "Researchers developed a new algorithm that can predict weather patterns more accurately.", "question": "What can the new algorithm do?", "options": ["Predict weather patterns", "Make maps", "Control the wind", "Measure temperature"], "answer": "Predict weather patterns"},
+    1250: [
+        "Philosophers have long debated the intricate relationship between free will and determinism.",
+        "Economists analyzed the impact of automation on global labor markets.",
+        "Researchers investigated how cultural identity influences moral decision-making."
     ]
 }
 
-# 状態管理
-state = {
-    "current_level": 600,
-    "question_count": 0,
-    "used_texts": set(),
-    "correct_count": 0,
-    "finished": False
-}
+levels = [300, 600, 850, 1050, 1250]
+
+# --- OpenAIクライアント設定 ---
+client = OpenAI(
+    base_url=BASE_URL,
+    api_key=API_KEY
+)
+
+# --- AIに問題生成を依頼 ---
+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 — DO NOT include which one is correct.
+    Format strictly as:
+    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}],
+        max_tokens=300,
+        temperature=0.7,
+    )
+    return response.choices[0].message.content.strip()
+
+# --- AIで正解を判定（別プロンプト） ---
+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}
 
-def get_next_question():
-    level_texts = texts_by_level[state["current_level"]]
-    available = [t for t in level_texts if t["text"] not in state["used_texts"]]
-    if not available:
-        return None
-    question = random.choice(available)
-    state["used_texts"].add(question["text"])
-    return question
-
-def check_answer(user_answer):
-    correct = state["current_question"]["answer"]
-    return user_answer == correct
-
-def quiz_flow(user_answer=None):
-    if state["finished"]:
-        return "テストはすでに終了しています。再開するにはページをリロードしてください。", None, None, None
-
-    # 回答処理
-    if user_answer is not None:
-        if check_answer(user_answer):
-            state["correct_count"] += 1
-            state["current_level"] = min(state["current_level"] + 200, 1200)
-        else:
-            state["current_level"] = max(state["current_level"] - 200, 400)
-        state["question_count"] += 1
-
-    # 5問で終了
-    if state["question_count"] >= 5:
-        state["finished"] = True
-        return f"✅ テスト終了！あなたの推定読解レベルは {state['current_level']}L です。", None, None, None
-
-    # 次の問題取得
-    question = get_next_question()
-    if not question:
-        state["finished"] = True
-        return f"📘 これ以上の問題がありません。あなたの推定レベルは {state['current_level']}L です。", None, None, None
-
-    state["current_question"] = question
-    return question["text"], question["question"], question["options"], None
-
-# Gradio UI
+    User Answer: {user_answer}
+
+    Respond with only one word: "Correct" or "Incorrect".
+    """
+
+    response = client.chat.completions.create(
+        model="google/gemma-3-27b-it:free",
+        messages=[{"role": "user", "content": prompt}],
+        max_tokens=10,
+        temperature=0,
+    )
+    return response.choices[0].message.content.strip().lower() == "correct"
+
+# --- 適応型テストの進行 ---
+def adaptive_test(prev_level, prev_correct):
+    idx = levels.index(prev_level)
+    if prev_correct and idx < len(levels) - 1:
+        new_level = levels[idx + 1]
+    elif not prev_correct and idx > 0:
+        new_level = levels[idx - 1]
+    else:
+        new_level = prev_level
+    return new_level
+
+# --- 状態管理 ---
+used_texts = set()
+question_count = 0
+MAX_QUESTIONS = 5
+
+# --- テスト開始 ---
+def start_test():
+    global used_texts, question_count
+    used_texts = set()
+    question_count = 0
+
+    level = 850  # 中間レベルから開始
+    text = random.choice(texts[level])
+    used_texts.add(text)
+    question = generate_question(text)
+    return text, question, level, None, "", ""
+
+# --- 回答処理して次へ ---
+def next_step(prev_level, user_answer, question_text, passage_text):
+    global question_count, used_texts
+    question_count += 1
+
+    # 正解判定（AIに依頼）
+    correct = check_answer_with_ai(passage_text, question_text, user_answer)
+
+    # レベルを更新
+    new_level = adaptive_test(prev_level, correct)
+
+    feedback = "✅ Correct!" if correct else "❌ Incorrect."
+
+    # --- 終了条件 ---
+    if question_count >= MAX_QUESTIONS:
+        feedback += f"\n🎯 Test finished! Your estimated reading level is **{new_level}L**."
+        return feedback, passage_text, question_text, new_level, None, ""
+
+    # --- 次の文章を選択（同じものを避ける） ---
+    available = [t for t in texts[new_level] if t not in used_texts]
+    if not available:  # 全て使い切ったらランダムに戻す
+        available = texts[new_level]
+    next_text = random.choice(available)
+    used_texts.add(next_text)
+
+    next_question = generate_question(next_text)
+    feedback += f"\n➡️ Next question is at a different level."
+
+    return feedback, next_text, next_question, new_level, None, ""
+
+# --- Gradio UI ---
 with gr.Blocks() as demo:
-    gr.Markdown("## 📖 英文読解テスト（全5問）")
+    gr.Markdown("# 📘 Adaptive Reading Level Test (Lexile-based)")
 
-    passage = gr.Textbox(label="文章", interactive=False, lines=4)
-    question_box = gr.Textbox(label="問題", interactive=False)
-    options = gr.Radio(label="選択肢", choices=[])
-    result = gr.Textbox(label="結果表示", interactive=False)
+    start_btn = gr.Button("▶️ Start Test")
 
-    next_btn = gr.Button("次へ")
+    text_display = gr.Textbox(label="Reading Passage", lines=6, interactive=False)
+    question_display = gr.Textbox(label="Generated Question", lines=8, interactive=False)
 
-    def next_step(user_choice):
-        return quiz_flow(user_choice)
+    user_answer = gr.Radio(choices=["A", "B", "C", "D"], label="Your Answer")
+    submit_btn = gr.Button("Submit Answer")
 
-    next_btn.click(
-        fn=next_step,
-        inputs=[options],
-        outputs=[passage, question_box, options, result],
+    feedback_display = gr.Markdown()
+    hidden_level = gr.Number(visible=False)
+    hidden_passage = gr.Textbox(visible=False)
+
+    # --- Start Test ---
+    start_btn.click(
+        fn=start_test,
+        inputs=[],
+        outputs=[text_display, question_display, hidden_level, user_answer, feedback_display, hidden_passage]
     )
 
-    # 初期化時に最初の問題表示
-    demo.load(fn=quiz_flow, inputs=None, outputs=[passage, question_box, options, result])
+    # --- Submit & Move to Next ---
+    submit_btn.click(
+        fn=next_step,
+        inputs=[hidden_level, user_answer, question_display, text_display],
+        outputs=[feedback_display, text_display, question_display, hidden_level, user_answer, hidden_passage]
+    )
 
 demo.launch()