Create vocab.py

vocab.py

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+import json
+import random
+import os
+import re
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# 初始化模型，只執行一次，避免每次請求都重新載入
+model_name = "EleutherAI/pythia-410m"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+DATA_DIR = "./data"
+
+def get_sources():
+    """掃描資料夾，回傳所有單字庫名稱"""
+    files = os.listdir(DATA_DIR)
+    sources = [f.split(".json")[0] for f in files if f.endswith(".json")]
+    return sources
+
+
+def clean_sentence(output):
+    """清理 GPT 生成的句子，去除雜訊"""
+    output = re.sub(r"Write.*?beginners\.", "", output, flags=re.IGNORECASE).strip()
+    output = re.sub(r"\*\*?\d+\.*\*\*", "", output).strip()
+    if not output.endswith("."):
+        output += "."
+    return output
+
+
+def get_words_with_sentences(source, n):
+    """抽取單字 + 生成例句，回傳結果和狀態"""
+    status = []
+    display_result = ""
+    try:
+        # 讀取單字庫資料
+        data_path = os.path.join(DATA_DIR, f"{source}.json")
+        with open(data_path, 'r', encoding='utf-8') as f:
+            words = json.load(f)
+
+        # 隨機抽取
+        selected_words = random.sample(words, n)
+        results = []
+
+        for i, word_data in enumerate(selected_words):
+            status.append(f"正在生成第 {i + 1}/{n} 個單字 [{word_data['word']}] 例句...")
+            word = word_data['word']
+
+            # GPT 造句 Prompt
+            prompt = f"Use the word '{word}' in a simple English sentence suitable for beginners. Output only the sentence."
+
+            inputs = tokenizer(prompt, return_tensors="pt")
+            outputs = model.generate(**inputs, max_new_tokens=30)
+            sentence = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+            clean_output = clean_sentence(sentence)
+
+            results.append({
+                "word": word,
+                "phonetic": word_data["phonetic"],
+                "sentence": clean_output
+            })
+
+            # 美化輸出文字
+            display_result += f"""
+            <div style="border-bottom: 1px solid #ddd; margin-bottom: 10px; padding-bottom: 5px;">
+                <p><strong>📖 單字：</strong> {word}</p>
+                <p><strong>🔤 音標：</strong> {word_data['phonetic']}</p>
+                <p><strong>✍️ 例句：</strong> {clean_output}</p>
+            </div>
+            """
+
+        status.append("✅ 完成！")
+
+        # 以HTML形式回傳美化後的結果
+        return display_result, "\n".join(status)
+
+    except Exception as e:
+        status.append(f"❌ 發生錯誤: {str(e)}")
+        return f"<p style='color:red;'>發生錯誤：{str(e)}</p>", "\n".join(status)