app.py

import gradio as gr
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch
import os


# 指定模型路徑或 Hugging Face Model Hub 上的模型 ID
model_name_or_path = "DeepLearning101/Corrector101zhTWT5"
auth_token = os.getenv("HF_HOME") 

# 嘗試加載模型和分詞器
try:
    tokenizer = T5Tokenizer.from_pretrained(model_name_or_path, use_auth_token=auth_token)
    model = T5ForConditionalGeneration.from_pretrained(model_name_or_path, use_auth_token=auth_token)
    model.eval()
except Exception as e:
    print(f"加載模型或分詞器失敗，錯誤信息：{e}")
    exit(1)

if torch.cuda.is_available():
    model.cuda()  # 如果可用，將模型移至 GPU

def correct_text(text):
    """將輸入的文本通過 T5 模型進行修正"""
    inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True, padding=True)
    if torch.cuda.is_available():
        inputs = {k: v.cuda() for k, v in inputs.items()}  # 將輸入移至 GPU
    with torch.no_grad():
        outputs = model.generate(**inputs)
    corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return corrected_text

def main():
    interface = gr.Interface(
        fn=correct_text,
        inputs=gr.Textbox(lines=5, placeholder="請輸入需要修正的中文文本..."),
        outputs=gr.Textbox(label="修正後的文本"),
        title="<h1>客服ASR文本AI糾錯系統</h1>",
        description="""<h2><a href='https://www.twman.org' target='_blank'>TonTon Huang Ph.D. @ 2024/04 </a></h2><br>
                    輸入ASR文本，糾正同音字/詞錯誤<br>
                    <a href='https://github.com/Deep-Learning-101' target='_blank'>Deep Learning 101 Github</a> | <a href='http://deeplearning101.twman.org' target='_blank'>Deep Learning 101</a> | <a href='https://www.facebook.com/groups/525579498272187/' target='_blank'>台灣人工智慧社團 FB</a> | <a href='https://www.youtube.com/c/DeepLearning101' target='_blank'>YouTube</a><br>
                    <a href='https://reurl.cc/g6GlZX' target='_blank'>手把手帶你一起踩AI坑</a> | <a href='https://blog.twman.org/2024/11/diffusion.html' target='_blank'>ComfyUI + Stable Diffuision</a><br>
                    <a href='https://blog.twman.org/2024/08/LLM.html' target='_blank'>白話文手把手帶你科普 GenAI</a> | <a href='https://blog.twman.org/2024/09/LLM.html' target='_blank'>大型語言模型直接就打完收工？</a><br>
                    <a href='https://blog.twman.org/2023/04/GPT.html' target='_blank'>什麼是大語言模型，它是什麼？想要嗎？</a> | <a href='https://blog.twman.org/2024/07/RAG.html' target='_blank'>那些檢索增強生成要踩的坑 </a><br>
                    <a href='https://blog.twman.org/2021/04/ASR.html' target='_blank'>那些語音處理 (Speech Processing) 踩的坑</a> | <a href='https://blog.twman.org/2021/04/NLP.html' target='_blank'>那些自然語言處理 (Natural Language Processing, NLP) 踩的坑</a><br>
                    <a href='https://blog.twman.org/2024/02/asr-tts.html' target='_blank'>那些ASR和TTS可能會踩的坑</a> | <a href='https://blog.twman.org/2024/02/LLM.html' target='_blank'>那些大模型開發會踩的坑</a><br>
                    <a href='https://blog.twman.org/2023/07/wsl.html' target='_blank'>用PPOCRLabel來幫PaddleOCR做OCR的微調和標註</a> | <a href='https://blog.twman.org/2023/07/HugIE.html' target='_blank'>基於機器閱讀理解和指令微調的統一信息抽取框架之診斷書醫囑資訊擷取分析</a><br>
                    <a href='https://github.com/shibing624/pycorrector' target='_blank'>Masked Language Model (MLM) as correction BERT</a>
                    基於transformers的T5ForConditionalGeneration""",
        theme="default",
        examples=[
            ["你究輸入利的手機門號跟生分證就可以了。"],
            ["這裡是客服中新，很高性為您服物，請問金天有什麼須要幫忙您得"],
            ["因為我們這邊是按天術比例計蒜給您的，其實不會有態大的穎響。也就是您用前面的資非的廢率來做計算"],
            ["我來看以下，他的時價是多少？起實您就可以直皆就不用到門事"],
            ["因為你現在月富是六九九嘛，我幫擬減衣百塊，兒且也不會江速"]
        ]
    )
    interface.launch(share=True)

if __name__ == "__main__":
    main()