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import re |
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import json |
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import requests |
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import os |
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import gradio as gr |
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HF_TOKEN = os.getenv("HF_TOKEN") |
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task_options = ["重點整理", "問答", "翻譯"] |
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MODEL_URLS = { |
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"重點整理": { |
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"翻譯": "https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-en-zh", |
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"摘要": "https://api-inference.huggingface.co/models/csebuetnlp/mT5_multilingual_XLSum" |
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}, |
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"問答": "https://api-inference.huggingface.co/models/luhua/chinese_pretrain_mrc_roberta_wwm_ext_large", |
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"翻譯": "https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-en-zh" |
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} |
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def clean_rtf(text): |
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text = re.sub(r"\'.", "", text) |
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text = re.sub(r"\[a-z]+[0-9]* ?", "", text) |
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text = re.sub(r"[{}]", "", text) |
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return text.strip() |
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def run_model(task, text, question=None): |
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headers = { |
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"Authorization": f"Bearer {HF_TOKEN}", |
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"Content-Type": "application/json" |
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} |
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if task == "重點整理": |
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translation_payload = {"inputs": text} |
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translation_url = MODEL_URLS["重點整理"]["翻譯"] |
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translation_response = requests.post(translation_url, headers=headers, json=translation_payload) |
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if translation_response.status_code != 200: |
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return "❌ 翻譯失敗:" + translation_response.text |
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try: |
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translated_text = translation_response.json()[0]['translation_text'] |
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except Exception as e: |
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return f"❌ 翻譯結果解析錯誤:{str(e)}" |
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summarization_payload = {"inputs": f"summarize: {translated_text}"} |
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summarization_url = MODEL_URLS["重點整理"]["摘要"] |
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summarization_response = requests.post(summarization_url, headers=headers, json=summarization_payload) |
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if summarization_response.status_code != 200: |
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return "❌ 摘要失敗:" + summarization_response.text |
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try: |
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summary_text = summarization_response.json()[0]['summary_text'] |
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except Exception as e: |
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return f"❌ 摘要結果解析錯誤:{str(e)}" |
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return summary_text |
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elif task == "問答": |
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if not question: |
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return "❌ 請輸入問題" |
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qa_payload = { |
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"inputs": { |
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"question": question, |
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"context": text |
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} |
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} |
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qa_url = MODEL_URLS["問答"] |
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qa_response = requests.post(qa_url, headers=headers, json=qa_payload) |
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if qa_response.status_code != 200: |
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return "❌ 問答失敗:" + qa_response.text |
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try: |
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answer = qa_response.json().get("answer", "⚠️ 找不到答案") |
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trans_payload = {"inputs": answer} |
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trans_url = MODEL_URLS["翻譯"] |
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trans_response = requests.post(trans_url, headers=headers, json=trans_payload) |
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if trans_response.status_code == 200: |
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return trans_response.json()[0]['translation_text'] |
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else: |
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return answer |
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except Exception as e: |
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return f"❌ 回答解析錯誤:{str(e)}" |
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elif task == "翻譯": |
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translation_payload = {"inputs": text} |
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translation_url = MODEL_URLS["翻譯"] |
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translation_response = requests.post(translation_url, headers=headers, json=translation_payload) |
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if translation_response.status_code != 200: |
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return "❌ 翻譯失敗:" + translation_response.text |
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try: |
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return translation_response.json()[0]['translation_text'] |
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except Exception as e: |
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return f"❌ 翻譯結果解析錯誤:{str(e)}" |
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else: |
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return "❌ 不支援的任務" |
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with gr.Blocks() as demo: |
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gr.Markdown("# 🌐 多功能語言處理器(繁體中文)\n支援:重點整理(英文→中)、問答、翻譯(英翻中)") |
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with gr.Row(): |
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task = gr.Dropdown(choices=task_options, label="請選擇任務") |
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with gr.Row(): |
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text_input = gr.Textbox(lines=10, label="輸入文章 / 內容") |
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with gr.Row(): |
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question_input = gr.Textbox(label="問題(問答任務用)", placeholder="選擇問答任務時必填") |
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with gr.Row(): |
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output = gr.Textbox(lines=5, label="輸出結果") |
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with gr.Row(): |
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run_button = gr.Button("執行") |
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run_button.click(fn=run_model, inputs=[task, text_input, question_input], outputs=output) |
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if __name__ == "__main__": |
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demo.launch() |
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