Update quiz.py

quiz.py

@@ -1,27 +1,58 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
+import random
+from vocab import get_words_from_source
+from sentences import get_sentence
 
-from ai_sentence import load_model
+# 生成單字選擇題
 
-# 生成選擇題
-def generate_mcq(word, model_name):
-    tokenizer, model = load_model(model_name)
+def generate_fill_in_blank_exam(source, num):
+    words_data = get_words_from_source(source)
+    words = random.sample(words_data, num)
 
-    prompt = f"Write a simple multiple-choice English question for beginners using the word '{word}'. Provide 4 options labeled A, B, C, D, and mark the correct answer."
-    inputs = tokenizer(prompt, return_tensors="pt")
-    outputs = model.generate(
-        **inputs,
-        max_new_tokens=50,
-        temperature=0.7,
-        top_p=0.9
-    )
-    question = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    questions = []
+    for word_data in words:
+        word = word_data['word']
+        phonetic = word_data['phonetic']
 
-    return question
+        # 取得例句
+        sentence_data = get_sentence(word)
+        if not sentence_data:
+            continue  # 沒例句的跳過
 
-# 對答案（未來補）
-def check_answer(user_answer, correct_answer):
-    return user_answer == correct_answer
+        sentence = sentence_data[2]
+        blank_sentence = sentence.replace(word, '______')
 
-# 計算分數（未來補）
-def calculate_score(total, correct):
-    return f"{correct}/{total} 分"
+        # 生成干擾選項 (亂數抽 3 個其他單字)
+        other_words = [w['word'] for w in words_data if w['word'] != word]
+        distractors = random.sample(other_words, 3)
+        options = [word] + distractors
+        random.shuffle(options)
+
+        questions.append({
+            "sentence": blank_sentence,
+            "options": options,
+            "answer": word,
+            "phonetic": phonetic
+        })
+
+    return questions
+
+
+# 自動對答案並計分
+def check_exam(user_answers, questions):
+    correct_count = 0
+    results = []
+
+    for i, user_answer in enumerate(user_answers):
+        correct_answer = questions[i]['answer']
+        is_correct = (user_answer == correct_answer)
+        results.append({
+            "question": questions[i]['sentence'],
+            "user_answer": user_answer,
+            "correct_answer": correct_answer,
+            "is_correct": is_correct
+        })
+        if is_correct:
+            correct_count += 1
+
+    score = f"{correct_count}/{len(questions)} 分"
+    return score, results