Update app.py

app.py

@@ -1,70 +1,224 @@
+import os, re, requests, base64
+from langchain_core.documents import Document
+from langchain_chroma import Chroma
+from openai import OpenAI
+from langchain.embeddings.base import Embeddings
+from langchain_google_genai import ChatGoogleGenerativeAI
+import chromadb
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import LLMChain
+from langchain.prompts import ChatPromptTemplate
+
+# =============================================
+# 1️⃣ 自訂 LM Studio Embedding 類別
+# =============================================
+class LmStudioEmbeddings(Embeddings):
+    def __init__(self, model_name, url):
+        self.model_name = model_name
+        self.client = OpenAI(base_url=url, api_key="lm-studio")
+
+    def embed_query(self, text: str):
+        res = self.client.embeddings.create(input=text, model=self.model_name)
+        return res.data[0].embedding
+
+    def embed_documents(self, texts: list[str]):
+        res = self.client.embeddings.create(input=texts, model=self.model_name)
+        return [x.embedding for x in res.data]
+
+# =============================================
+# 2️⃣ 載入 QA 檔案並分類
+# =============================================
+path = "/Users/adamlin/Library/CloudStorage/OneDrive-個人/QA/QA_v2.txt"
+with open(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(), metadata={"source": path}))
+    elif "期貨" in qa:
+        qa_docs["期貨"].append(Document(page_content=qa.strip(), metadata={"source": path}))
+    elif "複委託" in qa:
+        qa_docs["複委託"].append(Document(page_content=qa.strip(), metadata={"source": path}))
+
+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")
 
-    messages = [{"role": "system", "content": system_message}]
+# =============================================
+# 3️⃣ 建立三個獨立向量資料庫
+# =============================================
+embedding = LmStudioEmbeddings(
+    model_name="text-embedding-bge-large-zh-v1.5",
+    url="http://127.0.0.1:1234/v1"
+)
+
+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():
+    eng_name = collection_names[cat]
+    vectordbs[cat] = Chroma(
+        client=client,
+        collection_name=eng_name,
+        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 + 記憶模組
+# =============================================
+API_KEY = "AIzaSyAxoIHYjStZ5xPe2EoNrOapHhvVmx9QzWs"
+llm = ChatGoogleGenerativeAI(model='gemini-2.5-flash', google_api_key=API_KEY)
+
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+
+# ✅ 只保留一個變數 input，context 會手動插入文字中
+prompt = ChatPromptTemplate.from_messages([
+    ("system", "你是一位金融客服人員，請根據下列公司規章內容回答使用者問題。若內容不足，也請根據既有資訊給出合理說明，並建議洽營業員了解詳情。"),
+    ("human", "{input}")
+])
+
+chain = LLMChain(
+    llm=llm,
+    prompt=prompt,
+    memory=memory
+)
 
-    messages.extend(history)
+# =============================================
+# 5️⃣ 自動分類 + 對話主邏輯
+# =============================================
+def auto_detect_category(text):
+    if any(k in text for k in ["股票", "證券", "開戶", "下單", "交割", "現股"]):
+        return "證券"
+    elif any(k in text for k in ["期貨", "選擇權", "結算", "保證金", "契約"]):
+        return "期貨"
+    elif any(k in text for k in ["複委託", "海外", "美股", "港股", "國外"]):
+        return "複委託"
+    else:
+        return "證券"
 
-    messages.append({"role": "user", "content": message})
+def chat_fn(message, history):
+    print(f"[DEBUG] 問題：{message}")
 
-    response = ""
+    if "午餐吃什麼" in message:
+        return "還在盤中交易無法離開，還是我們約下午茶如何？"
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
+    category = auto_detect_category(message)
+    vectordb = vectordbs.get(category)
+    if not vectordb:
+        return "目前尚無此類別的知識庫，請洽營業員。"
 
-        response += token
-        yield response
+    # 向量檢索
+    docs = vectordb.similarity_search(message, k=2)
+    context = "\n\n".join([d.page_content for d in docs]) if docs else "目前查無相關內容。"
 
+    # ✅ 將 context 手動整合進輸入文字中（新版 LangChain 安全寫法）
+    full_input = f"公司規章內容如下：\n{context}\n\n使用者問題：{message}"
+
+    try:
+        response = chain.invoke({"input": full_input})
+        reply = response["text"].strip()
+    except Exception as e:
+        reply = f"⚠️ 生成錯誤：{e}"
+
+    return reply or "請洽營業員"
+
+# =============================================
+# 6️⃣ Gradio 介面 + 左上角 logo
+# =============================================
 
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+＃要在ＨＦ上部署的話需要改ㄧ下api，把它藏起來
+
+import os
+from langchain_google_genai import ChatGoogleGenerativeAI
+
+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)
 """
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+# =============================================
+
+
+
+logo_path = r"/Users/adamlin/Library/CloudStorage/OneDrive-個人/QA/mega.png"  # ← 改成你的實際路徑
+with open(logo_path, "rb") as f:
+    logo_base64 = base64.b64encode(f.read()).decode("utf-8")
+
+with gr.Blocks(
+    theme="Taithrah/Minimal",
+    css="""
+        /* 固定 logo 在左上角 */
+        #logo-top {
+            position: fixed;
+            top: 12px;
+            left: 18px;
+            z-index: 1000;
+            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;
+        }
+    """
+) as demo:
+
+    # 插入 logo
+    gr.HTML(f"""
+        <div id="logo-top">
+            <img src="data:image/png;base64,{logo_base64}" alt="logo">
+        </div>
+    """)
+
+    gr.Markdown("<h1 style='text-align:center'>👨‍💼 我是小智，您的金融好幫手🫰</h1>")
+
+    with gr.Row():
+        with gr.Column(scale=4):
+            chatbox = gr.Chatbot(label="💬 對話紀錄", type="messages")
+            user_input = gr.Textbox(label="輸入訊息", placeholder="請輸入問題...")
+
+            def handle_input(message, history):
+                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])
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+        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])
 
+            # ✅ 清除記憶按鈕
+            def clear_memory():
+                memory.clear()
+                return [], gr.update(value="", placeholder="請輸入問題...")
+            gr.Button("🧹 整理畫面").click(clear_memory, outputs=[chatbox, user_input])
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()