Spaces:
Running
Running
File size: 2,782 Bytes
1112df1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
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)
|