Update vocab.py

vocab.py

@@ -1,63 +1,67 @@
 import sqlite3
 import os
 import json
+import random
+from ai_sentence import generate_sentence
+from tqdm import tqdm
 
 DATA_DIR = "./data"
 DB_PATH = os.path.join(DATA_DIR, "sentences.db")
 
 
-# 初始化資料表
-def init_db():
-    conn = sqlite3.connect(DB_PATH)
-    c = conn.cursor()
-    c.execute('''
-    CREATE TABLE IF NOT EXISTS sentences (
-        word TEXT,
-        phonetic TEXT,
-        sentence TEXT,
-        source TEXT,
-        model TEXT,
-        created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
-        PRIMARY KEY (word, source, model)
-    )
-    ''')
-    conn.commit()
-    conn.close()
-
-
-# 查某個單字的所有例句
-def get_sentences_by_word(word):
-    conn = sqlite3.connect(DB_PATH)
-    c = conn.cursor()
-    c.execute('SELECT word, phonetic, sentence, source, model FROM sentences WHERE word=?', (word,))
-    results = c.fetchall()
-    conn.close()
-    return results
-
-
-# 儲存句子到 SQLite
-def save_sentence(word, phonetic, sentence, source, model):
-    conn = sqlite3.connect(DB_PATH)
-    c = conn.cursor()
-    c.execute('''
-    INSERT INTO sentences (word, phonetic, sentence, source, model)
-    VALUES (?, ?, ?, ?, ?)
-    ON CONFLICT(word, source, model) DO UPDATE SET sentence=excluded.sentence, phonetic=excluded.phonetic
-    ''', (word, phonetic, sentence, source, model))
-    conn.commit()
-    conn.close()
-
-
-# 掃描 /data 內的 JSON 檔，回傳單字庫名稱清單
-def get_sources():
-    files = os.listdir(DATA_DIR)
-    sources = [f.split(".json")[0] for f in files if f.endswith(".json")]
-    return sources
-
-
-# 根據單字庫名稱，回傳該單字庫所有單字
-def get_words_from_source(source):
-    data_path = os.path.join(DATA_DIR, f"{source}.json")
-    with open(data_path, 'r', encoding='utf-8') as f:
-        words = json.load(f)
-    return [w['word'] for w in words]
+# 抽單字 & 查例句或 GPT 生成例句
+def get_words_with_sentences(source, n):
+    try:
+        # 取得單字庫所有單字資料
+        with open(os.path.join(DATA_DIR, f"{source}.json"), 'r', encoding='utf-8') as f:
+            words = json.load(f)
+
+        selected_words = random.sample(words, n)
+
+        result_display = ""
+        for word_data in tqdm(selected_words, desc="處理單字"):
+            word = word_data['word']
+            phonetic = word_data['phonetic']
+
+            # 查詢句庫
+            conn = sqlite3.connect(DB_PATH)
+            c = conn.cursor()
+            c.execute('SELECT sentence, source, model FROM sentences WHERE word=?', (word,))
+            sentence_records = c.fetchall()
+            conn.close()
+
+            if sentence_records:
+                # 優先取 Tatoeba
+                sentence = ""
+                for rec in sentence_records:
+                    if rec[1] == "tatoeba":
+                        sentence = rec[0]
+                        break
+                if not sentence:
+                    sentence = sentence_records[0][0]
+            else:
+                # 如果句庫沒有，生成 GPT 句子
+                sentence = generate_sentence(word, "EleutherAI/pythia-410m")
+                # 存回資料庫
+                conn = sqlite3.connect(DB_PATH)
+                c = conn.cursor()
+                c.execute('''
+                    INSERT INTO sentences (word, phonetic, sentence, source, model)
+                    VALUES (?, ?, ?, ?, ?)
+                    ON CONFLICT(word, source, model) DO UPDATE SET sentence=excluded.sentence, phonetic=excluded.phonetic
+                ''', (word, phonetic, sentence, "ai", "EleutherAI/pythia-410m"))
+                conn.commit()
+                conn.close()
+
+            result_display += f"""
+            <div style="margin-bottom: 10px; padding: 8px; border-left: 4px solid #4CAF50; background-color: #f9f9f9;">
+                <strong>單字：</strong> {word} <br>
+                <strong>音標：</strong> {phonetic or '無'} <br>
+                <strong>句子：</strong> {sentence}
+            </div>
+            """
+
+        return result_display, "✅ 抽單字 & 生成完成"
+
+    except Exception as e:
+        return f"<p style='color:red;'>❌ 發生錯誤：{str(e)}</p>", f"❌ 錯誤：{str(e)}"