app.py

import os, re, base64
from langchain_core.documents import Document
from langchain_chroma import Chroma
from langchain_huggingface import HuggingFaceEmbeddings   # ✅ 雲端可直接使用
from langchain_google_genai import ChatGoogleGenerativeAI
import chromadb
import gradio as gr

# === 記憶模組相容多版本 ===
try:
    from langchain_memory import ConversationBufferMemory
except ImportError:
    try:
        from langchain.memory import ConversationBufferMemory
    except ImportError:
        from langchain_community.memory import ConversationBufferMemory


# =============================================
# 1️⃣ 使用 Hugging Face 雲端 embedding 模型
# =============================================
embedding = HuggingFaceEmbeddings(model_name="BAAI/bge-small-zh-v1.5")

# =============================================
# 2️⃣ 載入 QA 檔案並分類
# =============================================
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
qa_path = os.path.join(BASE_DIR, "QA_v2.txt")

if not os.path.exists(qa_path):
    raise FileNotFoundError(f"❌ 找不到 QA 檔案：{qa_path}")

with open(qa_path, "r", encoding="utf-8") as f:
    text = f.read()

pattern = r"(Ｑ[:：].*?)(?=Ｑ[:：]|$)"
qas = re.findall(pattern, text, flags=re.S)
qa_docs = {"證券": [], "期貨": [], "複委託": []}
for qa in qas:
    if "證券" in qa:
        qa_docs["證券"].append(Document(page_content=qa.strip()))
    elif "期貨" in qa:
        qa_docs["期貨"].append(Document(page_content=qa.strip()))
    elif "複委託" in qa:
        qa_docs["複委託"].append(Document(page_content=qa.strip()))

print("✅ 已成功讀取 QA 並完成分類：")
for k, v in qa_docs.items():
    print(f"　{k}：{len(v)} 筆")


# =============================================
# 3️⃣ 建立向量資料庫
# =============================================
client = chromadb.PersistentClient(path="./chroma_db")
collection_names = {"證券": "stocks", "期貨": "futures", "複委託": "overseas"}

vectordbs = {}
for cat, docs in qa_docs.items():
    vectordbs[cat] = Chroma(
        client=client,
        collection_name=collection_names[cat],
        embedding_function=embedding
    )
    if len(vectordbs[cat].get()["documents"]) == 0:
        vectordbs[cat].add_documents(docs)
print("✅ 各類別向量資料庫建立完成")


# =============================================
# 4️⃣ 初始化 Gemini LLM（雲端）
# =============================================
API_KEY = os.getenv("GOOGLE_API_KEY")
if not API_KEY:
    raise ValueError("⚠️ 未設定 GOOGLE_API_KEY，請在 Hugging Face Secrets 中新增。")

llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", google_api_key=API_KEY)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)


# =============================================
# 5️⃣ 對話邏輯
# =============================================
def auto_detect_category(text):
    if any(k in text for k in ["股票", "證券", "開戶", "下單", "交割"]):
        return "證券"
    elif any(k in text for k in ["期貨", "選擇權", "保證金"]):
        return "期貨"
    elif any(k in text for k in ["複委託", "海外", "美股", "港股"]):
        return "複委託"
    else:
        return "證券"


def chat_fn(message, history):
    category = auto_detect_category(message)
    vectordb = vectordbs.get(category)
    docs = vectordb.similarity_search(message, k=2)
    context = "\n\n".join([d.page_content for d in docs]) if docs else "查無資料"

    prompt = f"""
你是一位金融客服人員，根據以下公司QA回答客戶問題：
---
{context}
---
使用者問題：{message}
    """

    try:
        response = llm.invoke(prompt)
        reply = response.content.strip()
    except Exception as e:
        reply = f"⚠️ 生成錯誤：{e}"
    return reply or "請洽營業員"


# =============================================
# 6️⃣ Gradio 介面（LINE風格 + 小輸入按鈕 + 純白footer）
# =============================================
logo_path = os.path.join(BASE_DIR, "mega.png")
logo_base64 = ""
if os.path.exists(logo_path):
    with open(logo_path, "rb") as f:
        logo_base64 = base64.b64encode(f.read()).decode("utf-8")

with gr.Blocks(
    theme="soft",
    css="""
        /* ====== logo ====== */
        #logo-top {
            position: fixed; top: 12px; left: 18px;
            background-color: white; border-radius: 10px;
            padding: 6px 8px; box-shadow: 0 0 8px rgba(0,0,0,0.15);
        }
        #logo-top img { width: 120px; height: auto; display: block; }

        /* ====== 標題 ====== */
        #main-title {
            font-size: 28px; font-weight: bold; text-align: center;
            line-height: 1.4; margin: 0; display: inline-block;
        }
        @media (max-width: 768px) {
            #main-title { font-size: 24px; white-space: pre-line; }
            #main-title::before {
                content: "👨‍💼 我是小智\\A您的金融好幫手 🫰";
                white-space: pre;
            }
            #main-title span { display: none; }
        }

        /* ====== footer（純白背景） ====== */
        #footer {
            position: fixed; bottom: 40px; left: 0; width: 100%;
            text-align: center; font-size: 13px; color: #aaa;
            border-top: 1px solid #ddd; padding-top: 8px;
            background-color: transparent;
        }
        @media (max-width: 768px) {
            #footer { position: relative; margin-top: 40px; }
        }

        /* ====== LINE風格輸入區 ====== */
        #input-row { display: flex; align-items: center; gap: 8px; margin-top: 10px; }
        #user-input {
            flex-grow: 1; border-radius: 20px; border: 1px solid #ccc;
            padding: 6px 12px; font-size: 15px; background-color: #fff;
            box-shadow: inset 0 0 1px rgba(0,0,0,0.05);
        }

        /* 🟢 小巧文字版「輸入」按鈕 */
        #send-btn {
            background-color: #00b800;
            color: white;
            border: none;
            border-radius: 14px;
            height: 26px;
            padding: 0 10px;
            font-size: 13px;
            font-weight: 600;
            cursor: pointer;
            transition: background-color 0.2s ease, transform 0.1s ease;
            box-shadow: 0 1px 2px rgba(0,0,0,0.1);
        }
        #send-btn:hover { background-color: #00a000; }
        #send-btn:active { transform: scale(0.95); }
    """
) as demo:
    if logo_base64:
        gr.HTML(f"<div id='logo-top'><img src='data:image/png;base64,{logo_base64}' alt='logo'></div>")

    gr.HTML("""
        <div id='main-title-wrapper' style='text-align:center; margin-top:20px;'>
            <h1 id='main-title'><span>👨‍💼 我是小智&nbsp;&nbsp;您的金融好幫手 🫰</span></h1>
            <p id='sub-title' style='margin-top:10px; font-size:14px; color:#666;'>Powered by Gemini & LangChain</p>
        </div>
    """)

    with gr.Row():
        with gr.Column(scale=4):
            chatbox = gr.Chatbot(label="💬 對話紀錄", type="messages")
            with gr.Row(elem_id="input-row"):
                user_input = gr.Textbox(elem_id="user-input", show_label=False, placeholder="輸入訊息...", scale=8)
                send_btn = gr.Button("輸入", elem_id="send-btn", scale=1)

            def handle_input(message, history):
                if not message.strip():
                    return history, gr.update(value="")
                reply = chat_fn(message, history)
                history = history + [
                    {"role": "user", "content": message},
                    {"role": "assistant", "content": reply}
                ]
                return history, gr.update(value="")

            user_input.submit(handle_input, [user_input, chatbox], [chatbox, user_input])
            send_btn.click(handle_input, [user_input, chatbox], [chatbox, user_input])

        with gr.Column(scale=1):
            gr.Markdown("### 👇 快速提問")
            btns = [
                ("未成年可以開戶嗎？", "未成年可以開戶嗎?"),
                ("法人開戶要準備什麼？", "法人開戶要準備什麼?"),
                ("期貨交易保證金是什麼？", "期貨交易保證金是什麼?"),
                ("複委託要如何下單？", "複委託要如何下單?"),
                ("美股交易時間？", "美股交易時間?"),
                ("美股可以定期定額嗎？", "美股可以定期定額嗎?")
            ]
            for label, q in btns:
                gr.Button(label).click(lambda h, q=q: handle_input(q, h), [chatbox], [chatbox, user_input])

            def clear_memory():
                memory.clear()
                return [], gr.update(value="", placeholder="輸入訊息...")
            gr.Button("🧹 整理畫面").click(clear_memory, outputs=[chatbox, user_input])

    gr.HTML("<div id='footer'>© Fintech Assistant — 僅業務使用，非官方授權</div>")

    demo.load(None, None, None, js="""
        window.addEventListener('focusin', () => {
            document.querySelector('textarea')?.scrollIntoView({ behavior: 'smooth', block: 'center' });
        });
    """)

demo.launch()