import openai import gradio as gr import fitz # PyMuPDF api_key = "" selected_model = "" summary_text = "" def set_api_key(user_api_key): global api_key api_key = user_api_key return "✅ API Key 已設定" def set_model(model_name): global selected_model selected_model = model_name return f"✅ 模型已選:{model_name}" def extract_pdf_text(file_path): doc = fitz.open(file_path) # 直接用檔案路徑打開 text = "" for page in doc: text += page.get_text() return text def generate_summary(pdf_file): global summary_text client = openai.OpenAI(api_key=api_key) pdf_text = extract_pdf_text(pdf_file) if not pdf_text.strip(): return "⚠️ 無法解析 PDF 文字,可能為純圖片 PDF。" response = client.chat.completions.create( model=selected_model, messages=[ {"role": "system", "content": "請將以下 PDF 內容整理為條列式摘要重點。"}, {"role": "user", "content": pdf_text[:4000]} ] ) summary_text = response.choices[0].message.content return summary_text def ask_question(user_question): client = openai.OpenAI(api_key=api_key) response = client.chat.completions.create( model=selected_model, messages=[ {"role": "system", "content": f"根據以下 PDF 摘要內容回答問題:\n{summary_text}"}, {"role": "user", "content": user_question} ] ) return response.choices[0].message.content with gr.Blocks() as demo: gr.Markdown("# 📄 PDF 摘要 & 問答助手 (Hugging Face 版)") api_key_input = gr.Textbox(label="輸入 OpenAI API Key", type="password") api_key_status = gr.Textbox(label="狀態", interactive=False) api_key_input.submit(set_api_key, inputs=api_key_input, outputs=api_key_status) model_choice = gr.Radio(["gpt-4", "gpt-4.1-nano", "gpt-4o"], label="選擇模型") model_status = gr.Textbox(label="模型狀態", interactive=False) model_choice.change(set_model, inputs=model_choice, outputs=model_status) pdf_upload = gr.File(label="上傳 PDF") summary_output = gr.Textbox(label="PDF 摘要", lines=10) summary_btn = gr.Button("生成摘要") summary_btn.click(generate_summary, inputs=pdf_upload, outputs=summary_output) question_input = gr.Textbox(label="請輸入您的問題") answer_output = gr.Textbox(label="AI 回答", lines=5) question_btn = gr.Button("送出問題") question_btn.click(ask_question, inputs=question_input, outputs=answer_output) demo.launch()