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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)
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()
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