Update app.py

app.py

@@ -1,6 +1,6 @@
 import openai
 import gradio as gr
-import fitz  # PyMuPDF
+import fitz
 from openai import OpenAI
 import traceback
 
@@ -12,7 +12,6 @@ client = None
 pdf_text = ""
 
 def set_api_key(user_api_key):
-    """設定 OpenAI API Key 並初始化客戶端"""
     global api_key, client
     try:
         api_key = user_api_key.strip()
@@ -20,26 +19,21 @@ def set_api_key(user_api_key):
             return "❌ API Key 不能為空"
 
         client = OpenAI(api_key=api_key)
-
-        # 測試 API Key 是否有效
         client.chat.completions.create(
             model="gpt-4",
             messages=[{"role": "user", "content": "你好"}],
             max_tokens=5
         )
-
         return "✅ API Key 已設定並驗證成功"
     except Exception as e:
         return f"❌ API Key 設定失敗: {str(e)}"
 
 def set_model(model_name):
-    """設定選擇的模型"""
     global selected_model
     selected_model = model_name
     return f"✅ 模型已選擇：{model_name}"
 
 def extract_pdf_text(file_path):
-    """從 PDF 文件中提取文字"""
     try:
         doc = fitz.open(file_path)
         text = ""
@@ -53,99 +47,85 @@ def extract_pdf_text(file_path):
         return f"❌ PDF 解析錯誤: {str(e)}"
 
 def generate_summary(pdf_file):
-    """從 PDF 內容生成摘要"""
     global summary_text, pdf_text
-
     if not client:
-        return "❌ 請先設定 OpenAI API Key"
-
+        return "❌ 請先設定 API Key"
     if not pdf_file:
         return "❌ 請先上傳 PDF 文件"
-
     try:
         pdf_text = extract_pdf_text(pdf_file.name)
         if not pdf_text.strip():
-            return "⚠️ 無法解析 PDF 文字，可能為純圖片 PDF 或空白文件。"
-
-        pdf_text_truncated = pdf_text[:8000]  # 簡單限制長度
+            return "⚠️ 無法解析 PDF 文字"
+        pdf_text_truncated = pdf_text[:8000]
         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，並用繁體中文回答："},
+                {"role": "system", "content": "請用繁體中文整理以下 PDF 內容摘要。"},
                 {"role": "user", "content": pdf_text_truncated}
             ],
             temperature=0.3
         )
-
         summary_text = response.choices[0].message.content
         return summary_text
-
     except Exception as e:
-        print(f"錯誤詳情: {traceback.format_exc()}")
+        print(traceback.format_exc())
         return f"❌ 摘要生成失敗: {str(e)}"
 
 def ask_question(user_question):
-    """基於 PDF 內容回答問題"""
     if not client:
-        return "❌ 請先設定 OpenAI API Key"
-
+        return "❌ 請先設定 API Key"
     if not summary_text and not pdf_text:
         return "❌ 請先生成 PDF 摘要"
-
     if not user_question.strip():
         return "❌ 請輸入問題"
-
     try:
         context = f"PDF 摘要:\n{summary_text}\n\n原始內容（部分）:\n{pdf_text[:2000]}"
         response = client.chat.completions.create(
             model=selected_model,
             messages=[
-                {"role": "system", "content": f"根據以下 PDF 內容回答用戶的問題，請用繁體中文回答並客觀精準：\n{context}"},
+                {"role": "system", "content": f"根據以下 PDF 內容回答問題，用繁體中文：\n{context}"},
                 {"role": "user", "content": user_question}
             ],
             temperature=0.2
         )
         return response.choices[0].message.content
-
     except Exception as e:
-        print(f"錯誤詳情: {traceback.format_exc()}")
+        print(traceback.format_exc())
         return f"❌ 問答生成失敗: {str(e)}"
 
 def clear_all():
-    """清除所有資料"""
     global summary_text, pdf_text
     summary_text = ""
     pdf_text = ""
     return "", "", ""
 
-# Gradio 介面
 with gr.Blocks(title="PDF 摘要助手") as demo:
-    gr.Markdown("# 📄 PDF 摘要 & 問答助手 (修正版)")
+    gr.Markdown("## 📄 PDF 摘要 & 問答助手")
 
     with gr.Tab("🔧 設定"):
-        api_key_input = gr.Textbox(label="🔑 輸入 OpenAI API Key", type="password")
-        api_key_status = gr.Textbox(label="狀態", interactive=False, value="等待設定 API Key...")
-        api_key_btn = gr.Button("確認設定 API Key")
+        api_key_input = gr.Textbox(label="輸入 OpenAI API Key", type="password")
+        api_key_status = gr.Textbox(label="API 狀態", interactive=False)
+        api_key_btn = gr.Button("確認 API Key")
         api_key_btn.click(set_api_key, inputs=api_key_input, outputs=api_key_status)
 
         model_choice = gr.Radio(["gpt-4", "gpt-4.1", "gpt-4.5"], label="選擇 AI 模型", value="gpt-4")
         model_status = gr.Textbox(label="模型狀態", interactive=False, value="✅ 已選擇：gpt-4")
         model_choice.change(set_model, inputs=model_choice, outputs=model_status)
 
-    with gr.Tab("📄 PDF 摘要"):
-        pdf_upload = gr.File(label="選擇 PDF 文件", file_types=[".pdf"])
+    with gr.Tab("📄 摘要"):
+        pdf_upload = gr.File(label="上傳 PDF", file_types=[".pdf"])
         summary_btn = gr.Button("生成摘要")
         summary_output = gr.Textbox(label="PDF 摘要", lines=10)
         summary_btn.click(generate_summary, inputs=pdf_upload, outputs=summary_output)
 
     with gr.Tab("❓ 問答"):
-        question_input = gr.Textbox(label="請輸入您的問題", lines=2)
+        question_input = gr.Textbox(label="請輸入問題", lines=2)
         question_btn = gr.Button("送出問題")
-        answer_output = gr.Textbox(label="AI 回答", lines=10)
+        answer_output = gr.Textbox(label="AI 回答", lines=8)
         question_btn.click(ask_question, inputs=question_input, outputs=answer_output)
         question_input.submit(ask_question, inputs=question_input, outputs=answer_output)
 
-    clear_btn = gr.Button("🗑️ 清除所有資料")
+    clear_btn = gr.Button("清除所有資料")
     clear_btn.click(clear_all, outputs=[summary_output, question_input, answer_output])
 
 if __name__ == "__main__":