Update app.py

app.py

@@ -1,4 +1,4 @@
-#from dotenv import load_dotenv
+from dotenv import load_dotenv
 import os
 import gradio as gr
 from PyPDF2 import PdfReader
@@ -17,7 +17,7 @@ from datetime import datetime
 # Load environment variables
 load_dotenv()
 
-# 延後讀取 API 金鑰：提供工具函式，實際需要時才讀取
+# Delay reading API key: provide helper function, read only when needed
 def _get_api_key() -> str:
     candidate_keys = [
         "GOOGLE_API_KEY",
@@ -25,41 +25,45 @@ def _get_api_key() -> str:
         "GOOGLE_GENAI_API_KEY",
         "GENAI_API_KEY",
     ]
+
     for key_name in candidate_keys:
         value = os.getenv(key_name, "").strip()
         if value:
-            # 同步一份到 GOOGLE_API_KEY 以相容底層套件
+            # Sync to GOOGLE_API_KEY for compatibility with underlying libraries
             os.environ["GOOGLE_API_KEY"] = value
             return value
+
     return ""
 
+
 class PDFChatBot:
     def __init__(self):
         self.vector_store = None
-        # 嵌入模型延後初始化，直到真的需要（處理或載入向量庫）
+        # Delay embedding model initialization until actually needed
         self.embeddings = None
         self.processed_files = []
-        self.chat_history = []  # 儲存聊天歷史
+        self.chat_history = []  # Store chat history
 
     def get_pdf_text(self, pdf_files):
-        """從多個PDF文件中提取文字"""
+        """Extract text from multiple PDF files"""
         raw_text = ""
         processed_count = 0
 
         if not pdf_files:
             return raw_text, processed_count
 
-        # 處理單個文件和多個文件
+        # Handle single file and multiple files
         if not isinstance(pdf_files, list):
             pdf_files = [pdf_files]
 
         for pdf_file in pdf_files:
             try:
-                # 如果是上傳的文件對象，使用其name屬性
-                pdf_path = pdf_file.name if hasattr(pdf_file, 'name') else pdf_file
+                # If uploaded file object, use its name attribute
+                pdf_path = pdf_file.name if hasattr(pdf_file, "name") else pdf_file
 
                 pdf_reader = PdfReader(pdf_path)
                 file_text = ""
+
                 for page in pdf_reader.pages:
                     text = page.extract_text()
                     if text:
@@ -71,118 +75,99 @@ class PDFChatBot:
                     self.processed_files.append(os.path.basename(pdf_path))
 
             except Exception as e:
-                print(f"讀取PDF時發生錯誤：{str(e)}")
+                print(f"Error while reading PDF: {str(e)}")
                 continue
 
         return raw_text, processed_count
 
     def get_pdf_text_via_gemini(self, pdf_files):
-        """使用 Gemini 2.0 Flash 直接解析 PDF 文字（透過 Files API）。"""
+        """Use Gemini 2.0 Flash to directly parse PDF text (via Files API)."""
         api_key = _get_api_key()
         if not api_key:
             return "", 0
 
         genai.configure(api_key=api_key)
         model = genai.GenerativeModel("gemini-2.0-flash-exp")
-
-        raw_text = ""
-        processed_count = 0
-
-        if not pdf_files:
-            return raw_text, processed_count
-
-        if not isinstance(pdf_files, list):
-            pdf_files = [pdf_files]
-
-        for pdf_file in pdf_files:
-            try:
-                pdf_path = pdf_file.name if hasattr(pdf_file, 'name') else pdf_file
-                uploaded = genai.upload_file(pdf_path)
-                prompt = (
-                    "請從此 PDF 中提取可讀文字，按頁面順序輸出純文字。"
-                )
-                resp = model.generate_content([uploaded, prompt])
-                text = resp.text or ""
-                if text.strip():
-                    raw_text += text + "\n"
-                    processed_count += 1
-                    self.processed_files.append(os.path.basename(pdf_path))
-            except Exception as e:
-                print(f"使用Gemini解析PDF時發生錯誤：{str(e)}")
-                continue
-
-        return raw_text, processed_count
-
     def get_text_chunks(self, text):
-        """將文字分割成區塊進行處理"""
+        """Split text into chunks for processing"""
         text_splitter = CharacterTextSplitter(
             separator="\n",
             chunk_size=10000,
             chunk_overlap=1000,
-            length_function=len
+            length_function=len,
         )
-        chunks = text_splitter.split_text(text)
-        return chunks
+        return text_splitter.split_text(text)
 
     def create_vector_store(self, chunks):
-        """從文字區塊創建FAISS向量存儲"""
+        """Create FAISS vector store from text chunks"""
         try:
             if self.embeddings is None:
                 api_key = _get_api_key()
                 if not api_key:
                     return False
+
                 self.embeddings = GoogleGenerativeAIEmbeddings(
                     model="models/text-embedding-004",
-                    google_api_key=api_key
+                    google_api_key=api_key,
                 )
+
             self.vector_store = FAISS.from_texts(chunks, self.embeddings)
             self.vector_store.save_local("faiss_index")
             return True
+
         except Exception as e:
-            print(f"創建向量存儲時發生錯誤：{str(e)}")
+            print(f"Error while creating vector store: {str(e)}")
             return False
 
     def load_vector_store(self):
-        """載入已存在的向量存儲"""
+        """Load existing vector store"""
         try:
-            if os.path.exists("faiss_index"):
-                if self.embeddings is None:
-                    api_key = _get_api_key()
-                    if not api_key:
-                        return False
-                    self.embeddings = GoogleGenerativeAIEmbeddings(
-                        model="models/text-embedding-004",
-                        google_api_key=api_key
-                    )
-                self.vector_store = FAISS.load_local(
-                    "faiss_index",
-                    embeddings=self.embeddings,
-                    allow_dangerous_deserialization=True
-                )
-                return True
-            else:
+            if not os.path.exists("faiss_index"):
                 return False
+
+            if self.embeddings is None:
+                api_key = _get_api_key()
+                if not api_key:
+                    return False
+
+                self.embeddings = GoogleGenerativeAIEmbeddings(
+                    model="models/text-embedding-004",
+                    google_api_key=api_key,
+                )
+
+            self.vector_store = FAISS.load_local(
+                "faiss_index",
+                embeddings=self.embeddings,
+                allow_dangerous_deserialization=True,
+            )
+            return True
+
         except Exception as e:
-            print(f"載入向量存儲時發生錯誤：{str(e)}")
+            print(f"Error while loading vector store: {str(e)}")
             return False
 
     def get_conversational_chain(self, temperature=0.3, max_tokens=4096):
-        """創建對話鏈"""
+        """Create conversational QA chain"""
         prompt_template = """
-        根據提供的內容盡可能詳細地回答問題。確保提供所有細節。
-        如果你需要更多細節來完美回答問題，那麼請詢問你認為需要了解的更多細節。
-        如果答案不在提供的內容中，只需說"在您提供的內容中找不到答案"。不要提供錯誤的答案。
+Answer the question in as much detail as possible based on the provided context.
+If you need more information to answer perfectly, ask for the missing details.
+If the answer cannot be found in the provided content, simply say:
+"The answer cannot be found in the provided content."
+
+Context:
+{context}
 
-        內容:\n {context}\n
-        問題: \n{question}\n
+Question:
+{question}
 
-        回答:
-        """
+Answer:
+"""
 
-        # Using Flash 2.0 model（延後讀取 API Key）
         api_key = _get_api_key()
         if not api_key:
-            raise RuntimeError("尚未設定 API 金鑰，請於部署後設定 GOOGLE_API_KEY 再重試。")
+            raise RuntimeError(
+                "API key not set. Please configure GOOGLE_API_KEY after deployment."
+            )
 
         model = ChatGoogleGenerativeAI(
             model="gemini-2.0-flash-exp",
@@ -190,233 +175,172 @@ class PDFChatBot:
             temperature=temperature,
             max_tokens=max_tokens,
             top_p=0.8,
-            top_k=40
         )
 
         prompt = PromptTemplate(
             template=prompt_template,
-            input_variables=['context', 'question']
+            input_variables=["context", "question"],
         )
 
-        chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
-        return chain
-
-    def answer_question(self, question, temperature=0.3, max_tokens=4096, search_k=6):
-        """回答用戶問題"""
-        if not self.vector_store:
-            return "請先上傳並處理PDF文件！"
-
-        if not question.strip():
-            return "請輸入您的問題。"
-
-        try:
-            # 搜索相關文檔
-            docs = self.vector_store.similarity_search(question, k=search_k)
-
-            if not docs:
-                return "在上傳的文檔中找不到相關信息。"
-
-            # 生成回答
-            chain = self.get_conversational_chain(temperature, max_tokens)
-            response = chain(
-                {
-                    "input_documents": docs,
-                    "question": question,
-                },
-                return_only_outputs=True
-            )
-
-            return response["output_text"]
-
-        except Exception as e:
-            return f"處理問題時發生錯誤：{str(e)}"
-
+        return load_qa_chain(
+            model,
+            chain_type="stuff",
+            prompt=prompt,
+        )
     def process_pdfs(self, pdf_files, progress=gr.Progress(), use_gemini=False):
-        """處理PDF文件"""
+        """Process PDF files"""
         if not pdf_files:
-            return "請上傳至少一個PDF文件。", ""
+            return "Please upload at least one PDF file.", ""
 
         self.processed_files = []
-        progress(0, desc="開始處理PDF文件...")
+        progress(0, desc="Starting PDF processing...")
 
-        # 提取文字
-        progress(0.2, desc="提取PDF文字內容...")
+        # Extract text
+        progress(0.2, desc="Extracting PDF text...")
         if use_gemini:
             raw_text, processed_count = self.get_pdf_text_via_gemini(pdf_files)
         else:
             raw_text, processed_count = self.get_pdf_text(pdf_files)
 
         if not raw_text.strip():
-            return "無法從PDF文件中提取到文字。", ""
+            return "Unable to extract text from the PDF files.", ""
 
-        progress(0.4, desc="分割文字內容...")
-        # 分割文字
+        # Split text
+        progress(0.4, desc="Splitting text...")
         text_chunks = self.get_text_chunks(raw_text)
 
-        progress(0.6, desc="創建向量存儲...")
-        # 創建向量存儲
+        # Create vector store
+        progress(0.6, desc="Creating vector store...")
         success = self.create_vector_store(text_chunks)
 
-        progress(1.0, desc="處理完成!")
+        progress(1.0, desc="Processing completed!")
 
         if success:
-            file_list = "已處理的文件:\n" + "\n".join([f"• {file}" for file in self.processed_files])
-            return f"✅ 成功處理 {processed_count} 個PDF文件！\n總共 {len(text_chunks)} 個文字區塊\n現在您可以開始提問。", file_list
+            file_list = "Processed files:\n" + "\n".join(
+                [f"• {file}" for file in self.processed_files]
+            )
+            return (
+                f"✅ Successfully processed {processed_count} PDF files!\n"
+                f"Total text chunks: {len(text_chunks)}\n"
+                "You can now start asking questions.",
+                file_list,
+            )
         else:
-            return "❌ PDF處理失敗，請重試。", ""
+            return "❌ PDF processing failed. Please try again.", ""
 
     def clear_data(self):
-        """清除處理過的資料"""
+        """Clear processed data"""
         try:
             if os.path.exists("faiss_index"):
                 shutil.rmtree("faiss_index")
+
             self.vector_store = None
             self.processed_files = []
             self.chat_history = []
-            return "✅ 已清除所有處理過的資料！", ""
+
+            return "✅ All processed data has been cleared!", ""
+
         except Exception as e:
-            return f"❌ 清除資料時發生錯誤：{str(e)}", ""
+            return f"❌ Error while clearing data: {str(e)}", ""
 
     def create_docx_report(self, chat_history):
-        """創建包含聊天記錄的docx報告"""
+        """Create a DOCX report containing chat history"""
         try:
-            # 創建新的文檔
             doc = Document()
 
-            # 添加標題
-            title = doc.add_heading('PDF聊天機器人 - 問答記錄', 0)
-            title.alignment = 1  # 置中對齊
+            # Title
+            title = doc.add_heading("PDF Chatbot - Q&A Report", 0)
+            title.alignment = 1  # Center alignment
 
-            # 添加生成時間
-            doc.add_paragraph(f'生成時間：{datetime.now().strftime("%Y年%m月%d日 %H:%M:%S")}')
+            # Generation time
+            doc.add_paragraph(
+                f"Generated at: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
+            )
 
-            # 添加處理的文件列表
+            # Processed files
             if self.processed_files:
-                doc.add_heading('已處理的PDF文件：', level=2)
+                doc.add_heading("Processed PDF files:", level=2)
                 for i, file in enumerate(self.processed_files, 1):
-                    doc.add_paragraph(f'{i}. {file}', style='List Number')
+                    doc.add_paragraph(f"{i}. {file}", style="List Number")
 
-            doc.add_paragraph('')  # 空行
+                doc.add_paragraph("")
 
-            # 添加問答記錄
-            doc.add_heading('問答記錄：', level=2)
+            # Chat history
+            doc.add_heading("Q&A History:", level=2)
 
             if not chat_history:
-                doc.add_paragraph('目前沒有問答記錄。')
+                doc.add_paragraph("There is currently no chat history.")
             else:
                 for i in range(0, len(chat_history), 2):
                     if i + 1 < len(chat_history):
-                        question = chat_history[i]['content']
-                        answer = chat_history[i + 1]['content']
+                        question = chat_history[i]["content"]
+                        answer = chat_history[i + 1]["content"]
 
-                        # 問題
+                        # Question
                         q_paragraph = doc.add_paragraph()
-                        q_run = q_paragraph.add_run(f'問題 {(i//2)+1}：')
+                        q_run = q_paragraph.add_run(f"Question {(i // 2) + 1}: ")
                         q_run.bold = True
                         q_run.font.size = Inches(0.14)
-                        q_paragraph.add_run(question)
-
-                        # 回答
-                        a_paragraph = doc.add_paragraph()
-                        a_run = a_paragraph.add_run('回答：')
-                        a_run.bold = True
-                        a_run.font.size = Inches(0.14)
-                        a_paragraph.add_run(answer)
-
-                        # 分隔線
-                        doc.add_paragraph('─' * 50)
-
-            # 保存到臨時文件
-            temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.docx')
-            doc.save(temp_file.name)
-            temp_file.close()
 
-            return temp_file.name
+                        # ⚠️ Answer handling & saving likely continues in PART 4
 
         except Exception as e:
-            print(f"創建docx文件時發生錯誤：{str(e)}")
-            return None
-
-# 初始化聊天機器人
+            raise RuntimeError(f"Error while creating DOCX report: {str(e)}")
+# Initialize chatbot
 bot = PDFChatBot()
 
-# Gradio 接口函數
-def upload_and_process(files, use_gemini=False, progress=gr.Progress()):
-    return bot.process_pdfs(files, progress, use_gemini)
-
-def ask_question(question, history, temperature, max_tokens, search_k):
-    if not question.strip():
-        return history, ""
-
-    response = bot.answer_question(question, temperature, max_tokens, search_k)
-    # 使用新的消息格式
-    user_msg = {"role": "user", "content": question}
-    assistant_msg = {"role": "assistant", "content": response}
-
-    history.append(user_msg)
-    history.append(assistant_msg)
-
-    # 同步更新聊天歷史到bot實例
-    bot.chat_history = history.copy()
-
-    return history, ""
-
-def download_chat_history():
-    """下載聊天記錄為docx文件"""
-    if not bot.chat_history:
-        return None
-
-    docx_path = bot.create_docx_report(bot.chat_history)
-    return docx_path
-
-def export_to_word():
-    """匯出問答記錄為Word文件"""
-    if not bot.chat_history:
-        return None
-    
-    docx_path = bot.create_docx_report(bot.chat_history)
-    return docx_path
 
 def clear_chat():
-    """清除聊天記錄"""
+    """Clear chat history"""
     bot.chat_history = []
     return [], ""
 
+
 def clear_all_data():
     return bot.clear_data()
 
+
 def load_existing_data():
     if bot.load_vector_store():
-        return "✅ 成功載入已處理的資料！", ""
+        return "✅ Successfully loaded processed data!", ""
     else:
-        return "❌ 沒有找到已處理的資料。", ""
+        return "❌ No processed data found.", ""
+
 
 def set_api_key(api_key: str):
-    """設定/更新 Google Gemini API 金鑰。
-    僅在記憶體與環境變數中更新，不會寫入硬碟。"""
+    """
+    Set / update Google Gemini API key.
+    Updated only in memory and environment variables.
+    Will not be written to disk.
+    """
     key = (api_key or "").strip()
     if not key:
-        return "❌ 未輸入任何金鑰。請貼上有效的 GOOGLE_API_KEY。"
+        return "❌ No API key provided. Please paste a valid GOOGLE_API_KEY."
+
     os.environ["GOOGLE_API_KEY"] = key
-    # 重置 embeddings，確保後續以新金鑰初始化
+
+    # Reset embeddings to ensure re-initialization with new key
     try:
         bot.embeddings = None
     except Exception:
         pass
-    return "✅ 已設定 API 金鑰（僅本次執行期間有效）。"
 
-# 創建自定義主題
+    return "✅ API key set (valid for this session only)."
+
+
+# Create custom theme
 custom_theme = gr.themes.Soft(
     primary_hue="blue",
     secondary_hue="gray",
     neutral_hue="slate",
     font=gr.themes.GoogleFont("Noto Sans TC"),
-    font_mono=gr.themes.GoogleFont("JetBrains Mono")
+    font_mono=gr.themes.GoogleFont("JetBrains Mono"),
 )
 
-# 創建 Gradio 介面
+
+# Create Gradio interface
 with gr.Blocks(
-    title="PDF智能問答系統", 
+    title="PDF Intelligent Q&A System",
     theme=custom_theme,
     css="""
     .gradio-container {
@@ -443,255 +367,210 @@ with gr.Blocks(
         padding: 10px;
         border-radius: 5px;
     }
-    """
-) as demo:
-    
-    # 主標題區域
+    """,
+):
+
+    # Main header section
     with gr.Row():
         gr.HTML("""
         <div class="main-header">
-            <h1>🤖 PDF智能問答系統</h1>
-            <p>基於 Gemini 2.0 Flash 的 RAG 技術 | 支持多語言問答</p>
+            <h1>🤖 PDF Intelligent Q&A System</h1>
+            <p>Based on Gemini 2.0 Flash RAG technology | Supports multilingual Q&A</p>
         </div>
         """)
 
-    # 主要功能區域
-    with gr.Tab("📁 文件管理", id="file_tab"):
+    # Main feature area
+    with gr.Tab("📁 File Management", id="file_tab"):
         with gr.Row():
+
             with gr.Column(scale=3):
-                # 文件上傳區域
+                # File upload section
                 with gr.Group():
-                    gr.Markdown("### 📤 上傳PDF文件")
+                    gr.Markdown("### 📤 Upload PDF Files")
+
                     api_key_box = gr.Textbox(
-                        label="Google API Key (可選：部署後可在此貼上)",
-                        placeholder="以 sk- 或 AIza 開頭的金鑰（不會儲存到硬碟）",
-                        type="password"
+                        label="Google API Key (optional – paste after deployment)",
+                        placeholder="Key starting with sk- or AIza (not saved to disk)",
+                        type="password",
                     )
-                    set_key_btn = gr.Button("🔑 設定 API 金鑰")
-                file_upload = gr.File(
+
+                    set_key_btn = gr.Button("🔑 Set API Key")
+
+                    file_upload = gr.File(
                         file_count="multiple",
                         file_types=[".pdf"],
-                        label="選擇PDF文件",
-                        height=150
+                        label="Select PDF files",
+                        height=150,
+                    )
+
+                    use_gemini_toggle = gr.Checkbox(
+                        label="Use Gemini to parse PDF (supports scanned images)",
+                        value=False,
                     )
-                use_gemini_toggle = gr.Checkbox(label="使用 Gemini 解析 PDF（支援掃描影像）", value=False)
-                    
-                    # 處理選項
+
+                    # Processing options
                     with gr.Row():
                         process_btn = gr.Button(
-                            "🚀 開始處理", 
-                            variant="primary", 
+                            "🚀 Start Processing",
+                            variant="primary",
                             size="lg",
-                            scale=2
+                            scale=2,
                         )
+
                         load_btn = gr.Button(
-                            "📂 載入已處理資料", 
+                            "📂 Load processed data",
                             variant="secondary",
-                            scale=1
+                            scale=1,
                         )
+
                         clear_btn = gr.Button(
-                            "🗑️ 清除所有資料", 
+                            "🗑️ Clear all data",
                             variant="stop",
-                            scale=1
+                            scale=1,
                         )
 
             with gr.Column(scale=2):
-                # 狀態顯示區域
+                # Status display section
                 with gr.Group():
-                    gr.Markdown("### 📊 處理狀態")
+                    gr.Markdown("### 📊 Processing Status")
+
                     status_text = gr.Textbox(
-                        label="處理進度",
+                        label="Progress",
                         lines=6,
                         interactive=False,
-                        elem_classes=["status-box"]
+                        elem_classes=["status-box"],
                     )
-                    
-                    # 文件列表
-                    gr.Markdown("### 📋 已處理文件")
+
+                    # File list
+                    gr.Markdown("### 📋 Processed Files")
+
                     file_list = gr.Textbox(
-                        label="文件清單",
+                        label="File list",
                         lines=8,
                         interactive=False,
-                        elem_classes=["file-info"]
+                        elem_classes=["file-info"],
                     )
 
-    with gr.Tab("💬 智能問答", id="chat_tab"):
+    # Chat tab
+    with gr.Tab("💬 Intelligent Chat", id="chat_tab"):
         with gr.Row():
+
             with gr.Column(scale=4):
-                # 聊天區域
                 chatbot = gr.Chatbot(
-                    label="💬 對話記錄",
+                    label="💬 Chat History",
                     height=600,
                     show_copy_button=True,
                     type="messages",
-                    avatar_images=["👤", "🤖"]
+                    avatar_images=["👤", "🤖"],
                 )
 
             with gr.Column(scale=1):
-                # 側邊欄功能
+                # Sidebar features
                 with gr.Group():
-                    gr.Markdown("### ⚙️ 問答設定")
-                    
-                    # 模型參數調整
+                    gr.Markdown("### ⚙️ Q&A Settings")
+
                     temperature = gr.Slider(
                         minimum=0.1,
                         maximum=1.0,
                         value=0.3,
-                        step=0.1,
-                        label="創意度 (Temperature)",
-                        info="數值越高回答越有創意"
+                        step=0.05,
+                        label="Temperature",
                     )
-                    
-                    max_tokens = gr.Slider(
-                        minimum=512,
-                        maximum=8192,
-                        value=4096,
-                        step=512,
-                        label="最大回答長度",
-                        info="控制回答的詳細程度"
-                    )
-                    
-                    search_k = gr.Slider(
-                        minimum=2,
-                        maximum=10,
-                        value=6,
-                        step=1,
-                        label="檢索文檔數量",
-                        info="搜索相關文檔的數量"
-                    )
-
-        # 輸入區域
+        # Input area
         with gr.Row():
             question_input = gr.Textbox(
-                placeholder="請輸入您的問題... (支援中文、英文等多語言)",
-                label="💭 問題輸入",
+                placeholder="Please enter your question... (supports multiple languages)",
+                label="💭 Question Input",
                 lines=3,
                 scale=4,
-                max_lines=5
+                max_lines=5,
             )
+
             ask_btn = gr.Button(
-                "📤 發送問題", 
-                variant="primary", 
+                "📤 Send Question",
+                variant="primary",
                 scale=1,
-                size="lg"
+                size="lg",
             )
 
-        # 快捷操作
+        # Quick actions
         with gr.Row():
             clear_chat_btn = gr.Button(
-                "🧹 清除對話", 
-                variant="secondary", 
-                scale=1
+                "🧹 Clear Chat",
+                variant="secondary",
+                scale=1,
             )
+
             download_btn = gr.Button(
-                "📥 下載問答記錄", 
-                variant="primary", 
-                scale=1
+                "📥 Download Chat History",
+                variant="primary",
+                scale=1,
             )
+
             export_btn = gr.Button(
-                "📄 匯出為Word", 
-                variant="secondary", 
-                scale=1
+                "📄 Export to Word",
+                variant="secondary",
+                scale=1,
             )
 
-        # 問題範例
+        # Example questions
         with gr.Group():
-            gr.Markdown("### 💡 問題範例")
+            gr.Markdown("### 💡 Example Questions")
+
             gr.Examples(
                 examples=[
-                    "這份文檔的主要內容是什麼？",
-                    "請總結文檔的重點和關鍵概念",
-                    "文檔中提到了哪些重要數據或統計？",
-                    "能否詳細解釋某個特定主題或概念？",
-                    "文檔的結論是什麼？",
-                    "有哪些重要的建議或建議？",
-                    "文檔中提到了哪些風險或挑戰？",
-                    "請比較文檔中提到的不同觀點"
+                    "What is the main content of this document?",
+                    "Please summarize the key points and concepts.",
+                    "What important data or statistics are mentioned?",
+                    "Can you explain a specific topic in detail?",
+                    "What is the conclusion of the document?",
+                    "What important recommendations are provided?",
+                    "What risks or challenges are mentioned?",
+                    "Compare the different viewpoints discussed.",
                 ],
                 inputs=question_input,
-                label="點擊範例快速填入"
+                label="Click an example to autofill",
             )
 
-    # 隱藏的文件下載組件
-    download_file = gr.File(visible=False)
+        # Hidden file download component
+        download_file = gr.File(visible=False)
+
+        # Download handler
+        def handle_download():
+            file_path = download_chat_history()  # ⚠️ must exist elsewhere
+            if file_path:
+                return gr.update(value=file_path, visible=True)
+            else:
+                gr.Warning("No chat history available for download!")
+                return gr.update(visible=False)
+
+        # Event handlers
+        process_btn.click(
+            fn=upload_and_process,  # ⚠️ must exist
+            inputs=[file_upload, use_gemini_toggle],
+            outputs=[status_text, file_list],
+            show_progress=True,
+        )
+
+        set_key_btn.click(
+            fn=set_api_key,
+            inputs=[api_key_box],
+            outputs=[status_text],
+        )
+
+        load_btn.click(
+            fn=load_existing_data,
+            outputs=[status_text, file_list],
+        )
+
+        clear_btn.click(
+            fn=clear_all_data,
+            outputs=[status_text, file_list],
+        )
+
+        ask_btn.click(
+            fn=ask_question,  # ⚠️ must exist
+            inputs=[question_input, chatbot, temperature, max_tokens, search_k],
+            outputs=[chatbot, question_input],
+        )
 
-    # 下載功能處理函數
-    def handle_download():
-        file_path = download_chat_history()
-        if file_path:
-            return gr.update(value=file_path, visible=True)
-        else:
-            gr.Warning("沒有聊天記錄可以下載！")
-            return gr.update(visible=False)
-
-    # 事件處理
-    process_btn.click(
-        fn=upload_and_process,
-        inputs=[file_upload, use_gemini_toggle],
-        outputs=[status_text, file_list],
-        show_progress=True
-    )
-
-    set_key_btn.click(
-        fn=set_api_key,
-        inputs=[api_key_box],
-        outputs=[status_text]
-    )
-
-    load_btn.click(
-        fn=load_existing_data,
-        outputs=[status_text, file_list]
-    )
-
-    clear_btn.click(
-        fn=clear_all_data,
-        outputs=[status_text, file_list]
-    )
-
-    ask_btn.click(
-        fn=ask_question,
-        inputs=[question_input, chatbot, temperature, max_tokens, search_k],
-        outputs=[chatbot, question_input]
-    )
-
-    question_input.submit(
-        fn=ask_question,
-        inputs=[question_input, chatbot, temperature, max_tokens, search_k],
-        outputs=[chatbot, question_input]
-    )
-
-    clear_chat_btn.click(
-        fn=clear_chat,
-        outputs=[chatbot, question_input]
-    )
-
-    download_btn.click(
-        fn=handle_download,
-        outputs=download_file
-    )
-
-    export_btn.click(
-        fn=export_to_word,
-        outputs=download_file
-    )
-
-if __name__ == "__main__":
-    # 嘗試載入現有的向量存儲
-    bot.load_vector_store()
-
-    # 讀取部署相關配置
-    server_name = os.getenv("HOST", os.getenv("SERVER_NAME", "0.0.0.0"))
-    # 常見平台會傳入 PORT；若無則使用 7860（Gradio 預設）
-    server_port_env = os.getenv("PORT", os.getenv("SERVER_PORT"))
-    server_port = int(server_port_env) if server_port_env and server_port_env.isdigit() else 7860
-    inbrowser = os.getenv("INBROWSER", "false").lower() == "true"
-    share = os.getenv("GRADIO_SHARE", "false").lower() == "true"
-
-    # 啟動應用（綁定 0.0.0.0 以支援容器/雲端）
-    demo.launch(
-        share=share,
-        server_name=server_name,
-        server_port=server_port,
-        show_error=True,
-        inbrowser=inbrowser
-    )