Update vocab.py

vocab.py

@@ -1,20 +1,11 @@
 import sqlite3
-import json
-import random
 import os
-import re
-from transformers import AutoModelForCausalLM, AutoTokenizer
 
-# 初始化 GPT 模型
-model_name = "EleutherAI/pythia-410m"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-
-# 資料夾
 DATA_DIR = "./data"
 DB_PATH = os.path.join(DATA_DIR, "sentences.db")
 
-# 建立資料表
+
+# 建立資料表（若不存在）
 def init_db():
     conn = sqlite3.connect(DB_PATH)
     c = conn.cursor()
@@ -29,13 +20,8 @@ def init_db():
     conn.commit()
     conn.close()
 
-# 自動掃描資料夾生成選單
-def get_sources():
-    files = os.listdir(DATA_DIR)
-    sources = [f.split(".json")[0] for f in files if f.endswith(".json")]
-    return sources
 
-# 查詢句庫
+# 查句庫，傳回 (word, phonetic, sentence) 或 None
 def get_sentence(word):
     conn = sqlite3.connect(DB_PATH)
     c = conn.cursor()
@@ -44,6 +30,7 @@ def get_sentence(word):
     conn.close()
     return result
 
+
 # 保存句子到 SQLite
 def save_sentence(word, phonetic, sentence):
     conn = sqlite3.connect(DB_PATH)
@@ -56,69 +43,6 @@ def save_sentence(word, phonetic, sentence):
     conn.commit()
     conn.close()
 
-# 清理 GPT 生成句子的雜訊
-def clean_sentence(output):
-    output = output.split(":")[-1].strip()
-    output = re.sub(r"^\d+\.\s*", "", output).strip()
-    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
-
-# 核心：抽單字 + 查句庫 or GPT 生成句子
-def get_words_with_sentences(source, n):
-    status = []
-    display_result = ""
-
-    try:
-        # 讀取單字庫
-        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 n 個單字
-        selected_words = random.sample(words, n)
-        results = []
-
-        for i, word_data in enumerate(selected_words):
-            word = word_data['word']
-            phonetic = word_data['phonetic']
-
-            # 查詢句庫，看是否已有例句
-            cached_result = get_sentence(word)
-            if cached_result:
-                sentence = cached_result[2]
-                status.append(f"✅ {word} 已有例句，從句庫讀取")
-            else:
-                # 沒有的話，GPT 生成句子
-                status.append(f"📝 正在生成第 {i + 1}/{n} 個單字 [{word}] 例句...")
-                prompt = f"A simple English sentence with the word '{word}':"
-                inputs = tokenizer(prompt, return_tensors="pt")
-                outputs = model.generate(**inputs, max_new_tokens=30)
-                sentence = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-                # 清理生成句子
-                sentence = clean_sentence(sentence)
-
-                # 存入句庫
-                save_sentence(word, phonetic, sentence)
-
-            # 美化輸出
-            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> {phonetic}</p>
-                <p><strong>✍️ 例句：</strong> {sentence}</p>
-            </div>
-            """
-
-        status.append("✅ 完成！")
-        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)
 
-# 啟動時自動建表
+# 初始化資料表
 init_db()