Delete gradio_show.ipynb

gradio_show.ipynb

@@ -1,1248 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "bbac0476",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The gradio extension is already loaded. To reload it, use:\n",
-      "  %reload_ext gradio\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext gradio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a83f8fbf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%blocks\n",
-    "import gradio as gr\n",
-    "from llama_cpp import Llama\n",
-    "import llama_cpp\n",
-    "from langchain.callbacks.manager import CallbackManager\n",
-    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
-    "\n",
-    "\n",
-    "llm = Llama(\n",
-    "    model_path=r\"C:\\Users\\user\\breeze-7b-instruct-v1_0-q4_k_m.gguf\",\n",
-    "    n_gpu_layers=100,\n",
-    "    n_batch=512,\n",
-    "    n_ctx=3000,\n",
-    "    f16_kv=True,\n",
-    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
-    "    verbose=False,\n",
-    ")\n",
-    "\n",
-    "with gr.Blocks() as demo:\n",
-    "    name=\"cora\"\n",
-    "    gr.Markdown(f\"# Greetings {name}!\")\n",
-    "    inp = gr.Textbox()\n",
-    "    out = gr.Textbox()\n",
-    "\n",
-    "    inp.change(fn=lambda x: x, inputs=inp, outputs=out)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c51b8778",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "\n",
-    "# 定義處理函數\n",
-    "def process_user_input(message):\n",
-    "    return message\n",
-    "\n",
-    "# 定義主函數\n",
-    "def main_pipeline(message, history):\n",
-    "    # 呼叫處理函數\n",
-    "    response = process_user_input(message)\n",
-    "    # 將輸出加入歷史訊息\n",
-    "    return response\n",
-    "\n",
-    "# 創建 Gradio 介面\n",
-    "chat_interface = gr.Interface(fn=main_pipeline,inputs=\"text\",outputs=\"text\",live=True)\n",
-    "\n",
-    "# 啟動應用程式\n",
-    "if __name__ == \"__main__\":\n",
-    "    \n",
-    "    chat_interface.launch()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3ff36b6c-1b1d-4703-96f7-65642fae5722",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "\n",
-    "def process_user_input(message):\n",
-    "    return message\n",
-    "\n",
-    "def main_pipeline(message, history):\n",
-    "    response = process_user_input(message)\n",
-    "    return response\n",
-    "\n",
-    "chat_interface = gr.ChatInterface(main_pipeline, type=\"messages\")\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "    chat_interface.launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ee5766db-3500-4082-a634-2b1cdad5859b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4d1d8fe7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "from llama_cpp import Llama\n",
-    "from langchain_community.llms import LlamaCpp\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "import llama_cpp\n",
-    "from langchain.callbacks.manager import CallbackManager\n",
-    "from sentence_transformers import SentenceTransformer\n",
-    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import re\n",
-    "import os\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "\n",
-    "# 定義輔助函式\n",
-    "def process_user_input(message):\n",
-    "    return message\n",
-    "\n",
-    "#     # 假設 PromptTemplate 和 invoke_with_temperature 已正確定義\n",
-    "#     user_mental_state4 = PromptTemplate(\n",
-    "#         input_variables=[\"input\"],\n",
-    "#         template=\"\"\"...\"\"\"\n",
-    "#     )\n",
-    "    \n",
-    "#     df_user = pd.DataFrame(columns=[\"輸入內容\", \"形容詞1\", \"形容詞2\", \"形容詞3\", \"角色1\", \"角色2\", \"角色3\"])\n",
-    "#     prompt_value1 = user_mental_state4.invoke({\"input\": message})\n",
-    "#     string = invoke_with_temperature(prompt_value1)\n",
-    "#     adjectives = [adj.strip() for adj in re.split('[,、，]', string)]\n",
-    "#     index = len(df_user)\n",
-    "#     df_user.loc[index, '輸入內容'] = message\n",
-    "#     if len(adjectives) == 3:\n",
-    "#         df_user.loc[index, '形容詞1'] = adjectives[0]\n",
-    "#         df_user.loc[index, '形容詞2'] = adjectives[1]\n",
-    "#         df_user.loc[index, '形容詞3'] = adjectives[2]\n",
-    "#     df_user.to_excel(\"user_gradio系統.xlsx\")\n",
-    "#    return message\n",
-    "\n",
-    "# 主邏輯\n",
-    "def main_pipeline(message, history):\n",
-    "    df_user = process_user_input(message)\n",
-    "    return df_user\n",
-    "\n",
-    "demo=gr.ChatInterface(main_pipeline)\n",
-    "\n",
-    "# 主程式進入點\n",
-    "if __name__ == \"__main__\":\n",
-    "    \n",
-    "    demo.launch()\n",
-    "    \n",
-    "# import gradio as gr\n",
-    "\n",
-    "# # 定義處理函數\n",
-    "# def process_user_input(message):\n",
-    "#     return message\n",
-    "\n",
-    "# # 定義主函數\n",
-    "# def main_pipeline(message, history):\n",
-    "#     # 呼叫處理函數\n",
-    "#     response = process_user_input(message)\n",
-    "#     # 將輸出加入歷史訊息\n",
-    "#     return response\n",
-    "\n",
-    "# # 創建 Gradio 介面\n",
-    "# chat_interface = gr.ChatInterface(main_pipeline)\n",
-    "\n",
-    "# # 啟動應用程式\n",
-    "# if __name__ == \"__main__\":\n",
-    "#     chat_interface.launch()\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "4df2a74d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tf_keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
-      "\n",
-      "Running on local URL:  http://127.0.0.1:7864\n",
-      "\n",
-      "To create a public link, set `share=True` in `launch()`.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><iframe src=\"http://127.0.0.1:7864/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\gradio\\analytics.py:106: UserWarning: IMPORTANT: You are using gradio version 4.44.0, however version 4.44.1 is available, please upgrade. \n",
-      "--------\n",
-      "  warnings.warn(\n",
-      "Traceback (most recent call last):\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\gradio\\queueing.py\", line 536, in process_events\n",
-      "    response = await route_utils.call_process_api(\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\gradio\\route_utils.py\", line 322, in call_process_api\n",
-      "    output = await app.get_blocks().process_api(\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\gradio\\blocks.py\", line 1935, in process_api\n",
-      "    result = await self.call_function(\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\gradio\\blocks.py\", line 1518, in call_function\n",
-      "    prediction = await fn(*processed_input)\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\gradio\\utils.py\", line 793, in async_wrapper\n",
-      "    response = await f(*args, **kwargs)\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\gradio\\chat_interface.py\", line 623, in _submit_fn\n",
-      "    response = await anyio.to_thread.run_sync(\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\anyio\\to_thread.py\", line 56, in run_sync\n",
-      "    return await get_async_backend().run_sync_in_worker_thread(\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 2441, in run_sync_in_worker_thread\n",
-      "    return await future\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 943, in run\n",
-      "    result = context.run(func, *args)\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_17468\\2638884024.py\", line 229, in main_pipeline\n",
-      "    df_filter=filter(sorted_df)\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_17468\\2638884024.py\", line 162, in filter\n",
-      "    p=len(df_user)-1\n",
-      "NameError: name 'df_user' is not defined\n"
-     ]
-    }
-   ],
-   "source": [
-    "import gradio as gr\n",
-    "from llama_cpp import Llama\n",
-    "from langchain_community.llms import LlamaCpp\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "import llama_cpp\n",
-    "from langchain.callbacks.manager import CallbackManager\n",
-    "from sentence_transformers import SentenceTransformer\n",
-    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import re\n",
-    "import os\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "\n",
-    "model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-mpnet-base-v2',device='cpu')\n",
-    "\n",
-    "# llm = LlamaCpp(\n",
-    "#     model_path=r\"C:\\Users\\Cora\\.cache\\lm-studio\\models\\YC-Chen\\Breeze-7B-Instruct-v1_0-GGUF\\breeze-7b-instruct-v1_0-q4_k_m.gguf\",\n",
-    "#     n_gpu_layers=100,\n",
-    "#     n_batch=512,\n",
-    "#     n_ctx=3000,\n",
-    "#     f16_kv=True,\n",
-    "#     callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
-    "#     verbose=False,\n",
-    "# )\n",
-    "\n",
-    "llm = LlamaCpp(\n",
-    "    model_path=r\"C:\\Users\\user\\breeze-7b-instruct-v1_0-q4_k_m.gguf\",\n",
-    "    n_gpu_layers=100,\n",
-    "    n_batch=512,\n",
-    "    n_ctx=3000,\n",
-    "    f16_kv=True,\n",
-    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
-    "    verbose=False,\n",
-    ")\n",
-    "\n",
-    "embedd_bk=pd.read_pickle(r\"C:\\Users\\user\\推薦系統實作\\bk_description1_角色形容詞_677.pkl\")\n",
-    "df_bk=pd.read_excel(r\"C:\\Users\\user\\推薦系統實作\\bk_description1_角色形容詞.xlsx\")\n",
-    "\n",
-    "def invoke_with_temperature(prompt, temperature=0.4):\n",
-    "    return llm.invoke(prompt, temperature=temperature)\n",
-    "\n",
-    "def process_user_input(message):\n",
-    "    user_mental_state4= PromptTemplate(\n",
-    "        input_variables=[\"input\"],\n",
-    "        template=\"\"\"[INST]<<SYS>>你是一位具有同理心的專業心理諮商師，沒有性別歧視，你可以客觀的根據談話內容的描述，判斷說話的人的心理困擾<</SYS>> \n",
-    "        請根據{input}描述三個最有可能心理困擾，輸出只包含三個心理困擾，回答格式只採用CSV格式，分隔符號使用逗號，參考以下範例：名詞1,名詞2,名詞3。[/INST]\"\"\"\n",
-    "    )\n",
-    "    \n",
-    "    user_character= PromptTemplate(\n",
-    "        input_variables=[\"input\"],\n",
-    "        template=\"\"\"[INST]<<SYS>>你是一位具有同理心的專業心理諮商師，沒有性別歧視，你可以客觀的根據談話內容的描述，判斷說話的大學生，在生活中的多重角色身分<</SYS>> \n",
-    "        請你根據談話內容{input}，客觀的判斷說話的大學生，在談話內容中的角色，以及他生活中其他角色的身分，提供三個最有可能的角色身分名詞，\n",
-    "        輸出只包含三個身分名詞，回答格式只採用CSV格式，分隔符號使用逗號，參考以下範例：名詞1,名詞2,名詞3。[/INST]\"\"\"\n",
-    "    )\n",
-    "    \n",
-    "\n",
-    "    df_user=pd.DataFrame(columns=[\"輸入內容\",\"形容詞1\", \"形容詞2\", \"形容詞3\", \"角色1\", \"角色2\", \"角色3\"])\n",
-    "    #df_user_record=pd.read_excel(r\"C:\\Users\\Cora\\推薦系統實作\\gradio系統歷史紀錄.xlsx\")\n",
-    "    \n",
-    "\n",
-    "    prompt_value1=user_mental_state4.invoke({\"input\":message})\n",
-    "    string=invoke_with_temperature(prompt_value1)\n",
-    "    #print(\"\\n\")\n",
-    "\n",
-    "    # 將字符串分割為名詞\n",
-    "    adjectives = [adj.strip() for adj in re.split('[,、，]', string)]\n",
-    "    \n",
-    "    index=len(df_user)\n",
-    "    df_user.loc[index, '輸入內容'] = message\n",
-    "\n",
-    "    # 確保形容詞數量符合欄位數量\n",
-    "    if len(adjectives) == 3:\n",
-    "        df_user.loc[index, '形容詞1'] = adjectives[0]\n",
-    "        df_user.loc[index, '形容詞2'] = adjectives[1]\n",
-    "        df_user.loc[index, '形容詞3'] = adjectives[2]\n",
-    "    df_user.to_excel(\"user_gradio系統.xlsx\")\n",
-    "    return df_user\n",
-    "\n",
-    "def embedd_df_user(df_user):\n",
-    "    \n",
-    "    columns_to_encode=df_user.loc[:,[\"形容詞1\", \"形容詞2\", \"形容詞3\"]]\n",
-    "\n",
-    "    # 初始化一個空的 DataFrame，用來存儲向量化結果\n",
-    "    embedd_user=df_user[[\"輸入內容\"]]\n",
-    "    #user_em= user_em.assign(形容詞1=None, 形容詞2=None, 形容詞3=None,角色1=None,角色2=None,角色3=None)\n",
-    "    embedd_user= embedd_user.assign(形容詞1=None, 形容詞2=None, 形容詞3=None)\n",
-    "    \n",
-    "\n",
-    "    # 遍歷每一個單元格，將結果存入新的 DataFrame 中\n",
-    "    i=len(df_user)-1\n",
-    "    for col in columns_to_encode:\n",
-    "        #print(i,col)\n",
-    "        # 將每個單元格的內容進行向量化\n",
-    "        embedd_user.at[i, col] = model.encode(df_user.at[i, col])           \n",
-    "    \n",
-    "    embedd_user.to_pickle(r\"C:\\Users\\user\\推薦系統實作\\user_gradio系統.pkl\")\n",
-    "    \n",
-    "    return embedd_user\n",
-    "\n",
-    "def top_n_books_by_average(df, n=3):\n",
-    "    \n",
-    "    # 根据 `average` 列降序排序\n",
-    "    sorted_df = df.sort_values(by='average', ascending=False)\n",
-    "    \n",
-    "    # 选择前 N 行\n",
-    "    top_n_df = sorted_df.head(n)\n",
-    "    \n",
-    "    # 提取书名列\n",
-    "    top_books = top_n_df['書名'].tolist()\n",
-    "    \n",
-    "    return top_books,sorted_df\n",
-    "\n",
-    "def similarity(embedd_user,embedd_bk,df_bk):\n",
-    "    df_similarity= pd.DataFrame(df_bk[['書名',\"內容簡介\",\"URL\",\"形容詞1\", \"形容詞2\", \"形容詞3\", '角色1', '角色2', '角色3']])\n",
-    "    df_similarity['average'] = np.nan\n",
-    "    #for p in range(len(embedd_user)): \n",
-    "    index=len(embedd_user)-1               \n",
-    "    for k in range(len(embedd_bk)):\n",
-    "            list=[]\n",
-    "            for i in range(1,4):\n",
-    "                for j in range(3,6):\n",
-    "                    vec1=embedd_user.iloc[index,i]#i是第i個形容詞，數字是第幾個是使用者輸入\n",
-    "                    vec2=embedd_bk.iloc[k,j]\n",
-    "                    similarity = cosine_similarity([vec1], [vec2])\n",
-    "                    list.append(similarity[0][0])\n",
-    "            # 计算总和\n",
-    "            total_sum = sum(list)\n",
-    "            # 计算数量\n",
-    "            count = len(list)\n",
-    "            # 计算平均值\n",
-    "            average = total_sum / count\n",
-    "            df_similarity.loc[k,'average']=average\n",
-    "\n",
-    "    top_books,sorted_df = top_n_books_by_average(df_similarity)\n",
-    "    return sorted_df     \n",
-    "\n",
-    "def filter(sorted_df):\n",
-    "    filter_prompt4 = PromptTemplate(\n",
-    "        input_variables=[\"mental_issue\", \"user_identity\",\" book\",\"book_reader\", \"book_description\"],\n",
-    "        template=\"\"\"[INST]<<SYS>>你是專業的心理諮商師和書籍推薦專家，擅長根據使用者的心理問題、身份特質，以及書名、書籍針對的主題和適合的讀者，判斷書籍是否適合推薦給使用者。\n",
-    "\n",
-    "        你的目的是幫助讀者找到可以緩解心理問題的書籍。請注意：\n",
-    "        1. 若書籍針對的問題與使用者的心理問題有關聯，即使書籍適合的讀者群與使用者身份沒有直接關聯，應偏向推薦。\n",
-    "        2. 若使用者身份的需求與書籍針對的問題有潛在關聯，應偏向推薦。\n",
-    "        3. 若書籍適合的讀者與使用者身份特質有任何關聯，應傾向推薦。\n",
-    "        4. 若書名跟使用者的心理問題或身分特質有任何關聯，應偏向推薦<</SYS>>\n",
-    "\n",
-    "        使用者提供的資訊如下：\n",
-    "        使用者身份是「{user_identity}」，其心理問題是「{mental_issue}」。書名是{book}，書籍適合的讀者群為「{book_reader}」，書籍針對的問題是「{book_description}」。\n",
-    "\n",
-    "        請根據以上資訊判斷這本書是否適合推薦給該使用者。\n",
-    "        僅輸出「是」或「否」，輸出後即停止。[/INST]\"\"\"\n",
-    "     )\n",
-    "    df_filter=sorted_df.iloc[:20,:]\n",
-    "    df_filter = df_filter.reset_index(drop=True)\n",
-    "    df_filter=df_filter.assign(推薦=None)\n",
-    "    #df_similarity= pd.DataFrame(df_bk[['書名',\"內容簡介\",\"URL\",\"形容詞1\", \"形容詞2\", \"形容詞3\", '角色1', '角色2', '角色3']])\n",
-    "    #df_similarity['average'] = np.nan\n",
-    "\n",
-    "    \n",
-    "    p=len(df_user)-1\n",
-    "    for k in range(len(df_filter)):    \n",
-    "        word=df_user[\"輸入內容\"].iloc[p]\n",
-    "        #book_reader = df_filter[\"角色1\"].iloc[p] + \"or\" + df_filter[\"角色2\"].iloc[p] + \"or\" + df_filter[\"角色3\"].iloc[p]\n",
-    "        book=df_filter[\"書名\"].iloc[k] \n",
-    "        book_reader = df_filter[\"角色1\"].iloc[k] \n",
-    "        user_identity = df_user[\"角色1\"].iloc[p]\n",
-    "        mental_issue=df_user[\"形容詞1\"].iloc[p]+\"、\"+df_user[\"形容詞2\"].iloc[p]+\"、\"+df_user[\"形容詞3\"].iloc[p]\n",
-    "        book_description=df_filter[\"形容詞1\"].iloc[k]+\"、\"+df_filter[\"形容詞2\"].iloc[k]+\"、\"+df_filter[\"形容詞3\"].iloc[k]\n",
-    "        print(book_reader)\n",
-    "        print(user_identity)\n",
-    "        #output = filter_prompt1.invoke({\"user_identity\": user_identity, \"book_reader\": book_reader})\n",
-    "        output = filter_prompt4.invoke({\"mental_issue\":mental_issue,\"user_identity\": user_identity, \"book\":book,\"book_description\":book_description,\"book_reader\": book_reader})\n",
-    "        string2=invoke_with_temperature(output)\n",
-    "        df_filter.loc[k, '推薦'] =string2\n",
-    "        df_recommend=df_filter[df_filter[\"推薦\"].str.strip() == \"是\"]\n",
-    "        \n",
-    "    return df_recommend\n",
-    "def output_content(df_recommend):\n",
-    "    content_prompt = PromptTemplate(\n",
-    "        input_variables=[\"content\"],\n",
-    "        template=\"\"\"[INST]<<SYS>>你是一個有同理心的心理師，負責推薦相關書籍給使用者<</SYS>>你是一個有同理心的心理師，\n",
-    "        請根據{content}，用平易近人且不官方的語氣，先介紹這本書的內容，總共約50-70字[/INST]\"\"\"\n",
-    "    )\n",
-    "\n",
-    "    a=0\n",
-    "    title=df_recommend.loc[a,\"書名\"]\n",
-    "    #URL=sorted_df.iloc[a,1]\n",
-    "    #content=sorted_df.iloc[a,2]\n",
-    "    \n",
-    "#     prompt_value2=content_prompt.invoke({\"content\":content})\n",
-    "#     summary=invoke_with_temperature(prompt_value2)\n",
-    "#     recommend_prompt = PromptTemplate(\n",
-    "#         input_variables=[\"title\",\"URL\",\"summary\"],\n",
-    "#         template=\"\"\"<<SYS>>\n",
-    "#         你是一個有同理心的心理師，負責推薦相關書籍給使用者<</SYS>>\n",
-    "#         [INST] \n",
-    "#         請根據{title}{URL}{summary}產出訊息，開頭不要有空格，並在最後面加入一句或兩句鼓勵的話\n",
-    "#         格式為：根據您的狀態，這裡提供一本書供您參考\\n\n",
-    "#         書名：{title}\\n\n",
-    "#         本書介紹:{summary}\\n\n",
-    "#         購書網址：{URL}\\n\n",
-    "#         希望對您有所幫助\n",
-    "#         [/INST]\"\"\"\n",
-    "#      )\n",
-    "#    prompt_value1=recommend_prompt.invoke({\"title\":title,\"URL\":URL,\"summary\":summary})\n",
-    "    \n",
-    "    recommend_prompt = PromptTemplate(\n",
-    "        input_variables=[\"title\"],\n",
-    "        template=\"\"\"<<SYS>>\n",
-    "        你是一個有同理心的心理師，負責推薦相關書籍給使用者<</SYS>>\n",
-    "        [INST] \n",
-    "        請根據{title}產出訊息，開頭不要有空格，並在最後面加入一句或兩句鼓勵的話\n",
-    "        格式為：根據您的狀態，這裡提供一本書供您參考\\n\n",
-    "        書名：{title}\\n\n",
-    "        希望對您有所幫助\n",
-    "        [/INST]\"\"\"\n",
-    "     )\n",
-    "    prompt_value1=recommend_prompt.invoke({\"title\":title})\n",
-    "    output=invoke_with_temperature(prompt_value1,temperature=0.4)\n",
-    "    return output                   \n",
-    "                    \n",
-    "def main_pipeline(message,history):\n",
-    "                \n",
-    "    df_user=process_user_input(message)\n",
-    "    embedd_user=embedd_df_user(df_user)\n",
-    "    sorted_df=similarity(embedd_user,embedd_bk,df_bk)\n",
-    "    df_filter=filter(sorted_df)\n",
-    "    final=output_content(df_filter)\n",
-    "    return final  \n",
-    "    \n",
-    "\n",
-    "# def recommend(message,history):\n",
-    "#     result=main_pipeline(message)\n",
-    "#     return result\n",
-    "\n",
-    "demo=gr.ChatInterface(main_pipeline,type=\"messages\")\n",
-    "\n",
-    "# with gr.Blocks() as demo:\n",
-    "#     gr.Markdown(\"Start typing below and then click **Run** to see the output.\")\n",
-    "#     with gr.Row():\n",
-    "#         inp = gr.Textbox(placeholder=\"What is your name?\")\n",
-    "#         out = gr.Textbox()\n",
-    "#     btn = gr.Button(\"Run\")\n",
-    "#     btn.click(fn=recommend, inputs=inp, outputs=out)\n",
-    "if __name__ == \"__main__\":\n",
-    "       demo.launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "487c853d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running on local URL:  http://127.0.0.1:7866\n",
-      "\n",
-      "To create a public link, set `share=True` in `launch()`.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><iframe src=\"http://127.0.0.1:7866/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\gradio\\analytics.py:106: UserWarning: IMPORTANT: You are using gradio version 4.44.0, however version 4.44.1 is available, please upgrade. \n",
-      "--------\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " 情緒控制困難,壓力負荷過高,人際衝突"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Traceback (most recent call last):\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\gradio\\queueing.py\", line 536, in process_events\n",
-      "    response = await route_utils.call_process_api(\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\gradio\\route_utils.py\", line 322, in call_process_api\n",
-      "    output = await app.get_blocks().process_api(\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\gradio\\blocks.py\", line 1935, in process_api\n",
-      "    result = await self.call_function(\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\gradio\\blocks.py\", line 1518, in call_function\n",
-      "    prediction = await fn(*processed_input)\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\gradio\\utils.py\", line 793, in async_wrapper\n",
-      "    response = await f(*args, **kwargs)\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\gradio\\chat_interface.py\", line 623, in _submit_fn\n",
-      "    response = await anyio.to_thread.run_sync(\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\anyio\\to_thread.py\", line 56, in run_sync\n",
-      "    return await get_async_backend().run_sync_in_worker_thread(\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 2441, in run_sync_in_worker_thread\n",
-      "    return await future\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 943, in run\n",
-      "    result = context.run(func, *args)\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_17468\\1758736236.py\", line 78, in main_pipeline\n",
-      "    df_filter=filter(sorted_df)\n",
-      "  File \"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_17468\\2638884024.py\", line 162, in filter\n",
-      "    p=len(df_user)-1\n",
-      "NameError: name 'df_user' is not defined\n"
-     ]
-    }
-   ],
-   "source": [
-    "import gradio as gr\n",
-    "from llama_cpp import Llama\n",
-    "from langchain_community.llms import LlamaCpp\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "import llama_cpp\n",
-    "from langchain.callbacks.manager import CallbackManager\n",
-    "from sentence_transformers import SentenceTransformer\n",
-    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import re\n",
-    "import os\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "\n",
-    "model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-mpnet-base-v2',device='cpu')\n",
-    "\n",
-    "llm = LlamaCpp(\n",
-    "    model_path=r\"C:\\Users\\user\\breeze-7b-instruct-v1_0-q4_k_m.gguf\",\n",
-    "    n_gpu_layers=100,\n",
-    "    n_batch=512,\n",
-    "    n_ctx=3000,\n",
-    "    f16_kv=True,\n",
-    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
-    "    verbose=False,\n",
-    ")\n",
-    "\n",
-    "embedd_bk=pd.read_pickle(r\"C:\\Users\\user\\推薦系統實作\\bk_description1_角色形容詞_677.pkl\")\n",
-    "df_bk=pd.read_excel(r\"C:\\Users\\user\\推薦系統實作\\bk_description1_角色形容詞.xlsx\")\n",
-    "\n",
-    "def invoke_with_temperature(prompt, temperature=0.4):\n",
-    "    return llm.invoke(prompt, temperature=temperature)\n",
-    "\n",
-    "def process_user_input(message):\n",
-    "    \n",
-    "    user_mental_state4= PromptTemplate(\n",
-    "        input_variables=[\"input\"],\n",
-    "        template=\"\"\"[INST]<<SYS>>你是一位具有同理心的專業心理諮商師，沒有性別歧視，你可以客觀的根據談話內容的描述，判斷說話的人的心理困擾<</SYS>> \n",
-    "        請根據{input}描述三個最有可能心理困擾，輸出只包含三個心理困擾，回答格式只採用CSV格式，分隔符號使用逗號，參考以下範例：名詞1,名詞2,名詞3。[/INST]\"\"\"\n",
-    "    )\n",
-    "    \n",
-    "    user_character= PromptTemplate(\n",
-    "        input_variables=[\"input\"],\n",
-    "        template=\"\"\"[INST]<<SYS>>你是一位具有同理心的專業心理諮商師，沒有性別歧視，你可以客觀的根據談話內容的描述，判斷說話的大學生，在生活中的多重角色身分<</SYS>> \n",
-    "        請你根據談話內容{input}，客觀的判斷說話的大學生，在談話內容中的角色，以及他生活中其他角色的身分，提供三個最有可能的角色身分名詞，\n",
-    "        輸出只包含三個身分名詞，回答格式只採用CSV格式，分隔符號使用逗號，參考以下範例：名詞1,名詞2,名詞3。[/INST]\"\"\"\n",
-    "    )\n",
-    "    \n",
-    "\n",
-    "    df_user=pd.DataFrame(columns=[\"輸入內容\",\"形容詞1\", \"形容詞2\", \"形容詞3\", \"角色1\", \"角色2\", \"角色3\"])\n",
-    "    #df_user_record=pd.read_excel(r\"C:\\Users\\Cora\\推薦系統實作\\gradio系統歷史紀錄.xlsx\")\n",
-    "    \n",
-    "\n",
-    "    prompt_value1=user_mental_state4.invoke({\"input\":message})\n",
-    "    string=invoke_with_temperature(prompt_value1)\n",
-    "    #print(\"\\n\")\n",
-    "\n",
-    "    # 將字符串分割為名詞\n",
-    "    adjectives = [adj.strip() for adj in re.split('[,、，]', string)]\n",
-    "    \n",
-    "    index=len(df_user)\n",
-    "    df_user.loc[index, '輸入內容'] = message\n",
-    "\n",
-    "    # 確保形容詞數量符合欄位數量\n",
-    "    if len(adjectives) == 3:\n",
-    "        df_user.loc[index, '形容詞1'] = adjectives[0]\n",
-    "        df_user.loc[index, '形容詞2'] = adjectives[1]\n",
-    "        df_user.loc[index, '形容詞3'] = adjectives[2]\n",
-    "    df_user.to_excel(\"user_gradio系統.xlsx\")\n",
-    "    return df_user\n",
-    "\n",
-    "               \n",
-    "                    \n",
-    "def main_pipeline(message,history):\n",
-    "                \n",
-    "    df_user=process_user_input(message)\n",
-    "    embedd_user=embedd_df_user(df_user)\n",
-    "    sorted_df=similarity(embedd_user,embedd_bk,df_bk)\n",
-    "    df_filter=filter(sorted_df)\n",
-    "    final=output_content(df_filter)\n",
-    "    return final  \n",
-    "    \n",
-    "\n",
-    "\n",
-    "demo=gr.ChatInterface(main_pipeline,type=\"messages\")\n",
-    "\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "       demo.launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "b3cadc4a-6f63-4038-bcfb-ef419ad5394a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running on local URL:  http://127.0.0.1:7873\n",
-      "\n",
-      "To create a public link, set `share=True` in `launch()`.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><iframe src=\"http://127.0.0.1:7873/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\gradio\\analytics.py:106: UserWarning: IMPORTANT: You are using gradio version 4.44.0, however version 4.44.1 is available, please upgrade. \n",
-      "--------\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
-   "source": [
-    "import gradio as gr\n",
-    "from llama_cpp import Llama\n",
-    "from langchain_community.llms import LlamaCpp\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "import llama_cpp\n",
-    "from langchain.callbacks.manager import CallbackManager\n",
-    "from sentence_transformers import SentenceTransformer\n",
-    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import re\n",
-    "import os\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "\n",
-    "model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-mpnet-base-v2',device='cpu')\n",
-    "\n",
-    "title=\"書籍推薦平台\"\n",
-    "\n",
-    "# llm = LlamaCpp(\n",
-    "#     model_path=r\"C:\\Users\\Cora\\.cache\\lm-studio\\models\\YC-Chen\\Breeze-7B-Instruct-v1_0-GGUF\\breeze-7b-instruct-v1_0-q4_k_m.gguf\",\n",
-    "#     n_gpu_layers=100,\n",
-    "#     n_batch=512,\n",
-    "#     n_ctx=3000,\n",
-    "#     f16_kv=True,\n",
-    "#     callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
-    "#     verbose=False,\n",
-    "# )\n",
-    "\n",
-    "llm = LlamaCpp(\n",
-    "    model_path=r\"C:\\Users\\user\\breeze-7b-instruct-v1_0-q4_k_m.gguf\",\n",
-    "    n_gpu_layers=100,\n",
-    "    n_batch=512,\n",
-    "    n_ctx=3000,\n",
-    "    f16_kv=True,\n",
-    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
-    "    verbose=False,\n",
-    ")\n",
-    "\n",
-    "embedd_bk=pd.read_pickle(r\"C:\\Users\\user\\推薦系統實作\\bk_description1_角色形容詞_677.pkl\")\n",
-    "df_bk=pd.read_excel(r\"C:\\Users\\user\\推薦系統實作\\bk_description1_角色形容詞.xlsx\")\n",
-    "\n",
-    "def invoke_with_temperature(prompt, temperature=0.4):\n",
-    "    return llm.invoke(prompt, temperature=temperature)\n",
-    "\n",
-    "def process_user_input(message):\n",
-    "    user_mental_state4= PromptTemplate(\n",
-    "        input_variables=[\"input\"],\n",
-    "        template=\"\"\"[INST]<<SYS>>你是一位具有同理心的專業心理諮商師，沒有性別歧視，你可以客觀的根據談話內容的描述，判斷說話的人的心理困擾<</SYS>> \n",
-    "        請根據{input}描述三個最有可能心理困擾，輸出只包含三個心理困擾，回答格式只採用CSV格式，分隔符號使用逗號，參考以下��例：名詞1,名詞2,名詞3。[/INST]\"\"\"\n",
-    "    )\n",
-    "    \n",
-    "    user_character= PromptTemplate(\n",
-    "        input_variables=[\"input\"],\n",
-    "        template=\"\"\"[INST]<<SYS>>你是一位具有同理心的專業心理諮商師，沒有性別歧視，你可以客觀的根據談話內容的描述，判斷說話的大學生，在生活中的多重角色身分<</SYS>> \n",
-    "        請你根據談話內容{input}，客觀的判斷說話的大學生，在談話內容中的角色，以及他生活中其他角色的身分，提供三個最有可能的角色身分名詞，\n",
-    "        輸出只包含三個身分名詞，回答格式只採用CSV格式，分隔符號使用逗號，參考以下範例：名詞1,名詞2,名詞3。[/INST]\"\"\"\n",
-    "    )\n",
-    "    \n",
-    "\n",
-    "    df_user=pd.DataFrame(columns=[\"輸入內容\",\"形容詞1\", \"形容詞2\", \"形容詞3\", \"角色1\", \"角色2\", \"角色3\"])\n",
-    "    #df_user_record=pd.read_excel(r\"C:\\Users\\Cora\\推薦系統實作\\gradio系統歷史紀錄.xlsx\")\n",
-    "    \n",
-    "\n",
-    "    prompt_value1=user_mental_state4.invoke({\"input\":message})\n",
-    "    string=invoke_with_temperature(prompt_value1)\n",
-    "    #print(\"\\n\")\n",
-    "\n",
-    "    # 將字符串分割為名詞\n",
-    "    adjectives = [adj.strip() for adj in re.split('[,、，]', string)]\n",
-    "    \n",
-    "    index=len(df_user)\n",
-    "    df_user.loc[index, '輸入內容'] = message\n",
-    "\n",
-    "    # 確保形容詞數量符合欄位數量\n",
-    "    if len(adjectives) == 3:\n",
-    "        df_user.loc[index, '形容詞1'] = adjectives[0]\n",
-    "        df_user.loc[index, '形容詞2'] = adjectives[1]\n",
-    "        df_user.loc[index, '形容詞3'] = adjectives[2]\n",
-    "    df_user.to_excel(\"user_gradio系統.xlsx\")\n",
-    "    return df_user\n",
-    "    #return message\n",
-    "\n",
-    "def embedd_df_user(df_user):\n",
-    "    \n",
-    "    columns_to_encode=df_user.loc[:,[\"形容詞1\", \"形容詞2\", \"形容詞3\"]]\n",
-    "\n",
-    "    # 初始化一個空的 DataFrame，用來存儲向量化結果\n",
-    "    embedd_user=df_user[[\"輸入內容\"]]\n",
-    "    #user_em= user_em.assign(形容詞1=None, 形容詞2=None, 形容詞3=None,角色1=None,角色2=None,角色3=None)\n",
-    "    embedd_user= embedd_user.assign(形容詞1=None, 形容詞2=None, 形容詞3=None)\n",
-    "    \n",
-    "\n",
-    "    # 遍歷每一個單元格，將結果存入新的 DataFrame 中\n",
-    "    i=len(df_user)-1\n",
-    "    for col in columns_to_encode:\n",
-    "        #print(i,col)\n",
-    "        # 將每個單元格的內容進行向量化\n",
-    "        embedd_user.at[i, col] = model.encode(df_user.at[i, col])           \n",
-    "    \n",
-    "    embedd_user.to_pickle(r\"C:\\Users\\user\\推薦系統實作\\user_gradio系統.pkl\")\n",
-    "    \n",
-    "    return embedd_user\n",
-    "    #word=\"happy\"\n",
-    "    #return word\n",
-    "\n",
-    "def top_n_books_by_average(df, n=3):\n",
-    "    \n",
-    "    # 根据 `average` 列降序排序\n",
-    "    sorted_df = df.sort_values(by='average', ascending=False)\n",
-    "    \n",
-    "    # 选择前 N 行\n",
-    "    top_n_df = sorted_df.head(n)\n",
-    "    \n",
-    "    # 提取书名列\n",
-    "    top_books = top_n_df['書名'].tolist()\n",
-    "    \n",
-    "    return top_books,sorted_df\n",
-    "\n",
-    "def similarity(embedd_user,embedd_bk,df_bk):\n",
-    "    df_similarity= pd.DataFrame(df_bk[['書名',\"內容簡介\",\"URL\",\"形容詞1\", \"形容詞2\", \"形容詞3\", '角色1', '角色2', '角色3']])\n",
-    "    df_similarity['average'] = np.nan\n",
-    "    #for p in range(len(embedd_user)): \n",
-    "    index=len(embedd_user)-1               \n",
-    "    for k in range(len(embedd_bk)):\n",
-    "            list=[]\n",
-    "            for i in range(1,4):\n",
-    "                for j in range(3,6):\n",
-    "                    vec1=embedd_user.iloc[index,i]#i是第i個形容詞，數字是第幾個是使用者輸入\n",
-    "                    vec2=embedd_bk.iloc[k,j]\n",
-    "                    similarity = cosine_similarity([vec1], [vec2])\n",
-    "                    list.append(similarity[0][0])\n",
-    "            # 计算总和\n",
-    "            total_sum = sum(list)\n",
-    "            # 计算数量\n",
-    "            count = len(list)\n",
-    "            # 计算平均值\n",
-    "            average = total_sum / count\n",
-    "            df_similarity.loc[k,'average']=average\n",
-    "\n",
-    "    top_books,sorted_df = top_n_books_by_average(df_similarity)\n",
-    "    return sorted_df     \n",
-    "\n",
-    "def filter(sorted_df,df_user):\n",
-    "    filter_prompt4 = PromptTemplate(\n",
-    "        input_variables=[\"mental_issue\", \"user_identity\",\" book\",\"book_reader\", \"book_description\"],\n",
-    "        template=\"\"\"[INST]<<SYS>>你是專業的心理諮商師和書籍推薦專家，擅長根據使用者的心理問題、身份特質，以及書名、書籍針對的主題和適合的讀者，判斷書籍是否適合推薦給使用者。\n",
-    "\n",
-    "        你的目的是幫助讀者找到可以緩解心理問題的書籍。請注意：\n",
-    "        1. 若書籍針對的問題與使用者的心理問題有關聯，即使書籍適合的讀者群與使用者身份沒有直接關聯，應偏向推薦。\n",
-    "        2. 若使用者身份的需求與書籍針對的問題有潛在關聯，應偏向推薦。\n",
-    "        3. 若書籍適合的讀者與使用者身份特質有任何關聯，應傾向推薦。\n",
-    "        4. 若書名跟使用者的心理問題或身分特質有任何關聯，應偏向推薦<</SYS>>\n",
-    "\n",
-    "        使用者提供的資訊如下：\n",
-    "        使用者身份是「{user_identity}」，其心理問題是「{mental_issue}」。書名是{book}，書籍適合的讀者群為「{book_reader}」，書籍針對的問題是「{book_description}」。\n",
-    "\n",
-    "        請根據以上資訊判斷這本書是否適合推薦給該使用者。\n",
-    "        僅輸出「是」或「否」，輸出後即停止。[/INST]\"\"\"\n",
-    "     )\n",
-    "    df_filter=sorted_df.iloc[:20,:]\n",
-    "    df_filter = df_filter.reset_index(drop=True)\n",
-    "    df_filter=df_filter.assign(推薦=None)\n",
-    "    #df_similarity= pd.DataFrame(df_bk[['書名',\"內容簡介\",\"URL\",\"形容詞1\", \"形容詞2\", \"形容詞3\", '角色1', '角色2', '角色3']])\n",
-    "    #df_similarity['average'] = np.nan\n",
-    "\n",
-    "    \n",
-    "    p=len(df_user)-1\n",
-    "    for k in range(len(df_filter)):    \n",
-    "        word=df_user[\"輸入內容\"].iloc[p]\n",
-    "        #book_reader = df_filter[\"角色1\"].iloc[p] + \"or\" + df_filter[\"角色2\"].iloc[p] + \"or\" + df_filter[\"角色3\"].iloc[p]\n",
-    "        book=df_filter[\"書名\"].iloc[k] \n",
-    "        book_reader = df_filter[\"角色1\"].iloc[k] \n",
-    "        user_identity = df_user[\"角色1\"].iloc[p]\n",
-    "        mental_issue=df_user[\"形容詞1\"].iloc[p]+\"、\"+df_user[\"形容詞2\"].iloc[p]+\"、\"+df_user[\"形容詞3\"].iloc[p]\n",
-    "        book_description=df_filter[\"形容詞1\"].iloc[k]+\"、\"+df_filter[\"形容詞2\"].iloc[k]+\"、\"+df_filter[\"形容詞3\"].iloc[k]\n",
-    "        print(book_reader)\n",
-    "        print(user_identity)\n",
-    "        #output = filter_prompt1.invoke({\"user_identity\": user_identity, \"book_reader\": book_reader})\n",
-    "        output = filter_prompt4.invoke({\"mental_issue\":mental_issue,\"user_identity\": user_identity, \"book\":book,\"book_description\":book_description,\"book_reader\": book_reader})\n",
-    "        string2=invoke_with_temperature(output)\n",
-    "        df_filter.loc[k, '推薦'] =string2\n",
-    "        df_recommend=df_filter[df_filter[\"推薦\"].str.strip() == \"是\"]\n",
-    "        \n",
-    "    return df_recommend\n",
-    "    \n",
-    "def output_content(df_recommend):\n",
-    "    content_prompt = PromptTemplate(\n",
-    "        input_variables=[\"content\"],\n",
-    "        template=\"\"\"[INST]<<SYS>>你是一個有同理心的心理師，負責推薦相關書籍給使用者<</SYS>>你是一個有同理心的心理師，\n",
-    "        請根據{content}，用平易近人且不官方的語氣，先介紹這本書的內容，總共約50-70字[/INST]\"\"\"\n",
-    "    )\n",
-    "\n",
-    "    a=0\n",
-    "    title=df_recommend.iloc[a,0]#不用loc，因為filter的時候index沒有重新歸零\n",
-    "    #URL=sorted_df.iloc[a,1]\n",
-    "    #content=sorted_df.iloc[a,2]\n",
-    "    \n",
-    "#     prompt_value2=content_prompt.invoke({\"content\":content})\n",
-    "#     summary=invoke_with_temperature(prompt_value2)\n",
-    "#     recommend_prompt = PromptTemplate(\n",
-    "#         input_variables=[\"title\",\"URL\",\"summary\"],\n",
-    "#         template=\"\"\"<<SYS>>\n",
-    "#         你是一個有同理心的心理師，負責推薦相關書籍給使用者<</SYS>>\n",
-    "#         [INST] \n",
-    "#         請根據{title}{URL}{summary}產出訊息，開頭不要有空格，並在最後面加入一句或兩句鼓勵的話\n",
-    "#         格式為：根據您的狀態，這裡提供一本書供您參考\\n\n",
-    "#         書名：{title}\\n\n",
-    "#         本書介紹:{summary}\\n\n",
-    "#         購書網址：{URL}\\n\n",
-    "#         希望對您有所幫助\n",
-    "#         [/INST]\"\"\"\n",
-    "#      )\n",
-    "#    prompt_value1=recommend_prompt.invoke({\"title\":title,\"URL\":URL,\"summary\":summary})\n",
-    "    \n",
-    "    recommend_prompt = PromptTemplate(\n",
-    "        input_variables=[\"title\"],\n",
-    "        template=\"\"\"<<SYS>>\n",
-    "        你是一個有同理心的心理師，負責推薦相關書籍給使用者<</SYS>>\n",
-    "        [INST] \n",
-    "        請根據{title}產出訊息，開頭不要有空格���並在最後面加入一句或兩句鼓勵的話\n",
-    "        格式為：根據您的狀態，這裡提供一本書供您參考\\n\n",
-    "        書名：{title}\\n\n",
-    "        希望對您有所幫助\n",
-    "        [/INST]\"\"\"\n",
-    "     )\n",
-    "    prompt_value1=recommend_prompt.invoke({\"title\":title})\n",
-    "    output=invoke_with_temperature(prompt_value1,temperature=0.4)\n",
-    "    return output                   \n",
-    "                    \n",
-    "def main_pipeline(message,history):\n",
-    "                \n",
-    "    df_user=process_user_input(message)\n",
-    "    embedd_user=embedd_df_user(df_user)\n",
-    "    sorted_df=similarity(embedd_user,embedd_bk,df_bk)\n",
-    "    df_filter=filter(sorted_df,df_user)\n",
-    "    final=output_content(df_filter)\n",
-    "    return final\n",
-    "    #return embedd_user\n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "\n",
-    "# def recommend(message,history):\n",
-    "#     result=main_pipeline(message)\n",
-    "#     return result\n",
-    "\n",
-    "demo=gr.ChatInterface(main_pipeline,type=\"messages\")\n",
-    "\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "       demo.launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aaad2fb2-a8d8-46e4-b9d4-c276f5ef0cb0",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "pip install tf-keras"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "cc344ace-81f1-45f2-ad4a-9cca508aa053",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>書名</th>\n",
-       "      <th>內容簡介</th>\n",
-       "      <th>URL</th>\n",
-       "      <th>形容詞1</th>\n",
-       "      <th>形容詞2</th>\n",
-       "      <th>形容詞3</th>\n",
-       "      <th>角色1</th>\n",
-       "      <th>角色2</th>\n",
-       "      <th>角色3</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>這僅有一次的人生, 我不想說抱歉</td>\n",
-       "      <td>你走太快了，容易迷路，要等靈魂跟上來，才能走更遠的路。那些你想要做成的事情，你做成了的事情，...</td>\n",
-       "      <td>https://www.eslite.com/product/100120106326824...</td>\n",
-       "      <td>自我期許</td>\n",
-       "      <td>自我反思</td>\n",
-       "      <td>人生目標</td>\n",
-       "      <td>自我提升</td>\n",
-       "      <td>人生感悟</td>\n",
-       "      <td>內心成長</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>我只是想分手而已: 親密殺人, 被深愛的男人殺死的女人們</td>\n",
-       "      <td>親密殺人不是約會暴力是整個社會必須全力阻止的連續殺人！只是想跟他分手的我，為何最後卻送了命？...</td>\n",
-       "      <td>https://www.eslite.com/product/100120106326824...</td>\n",
-       "      <td>心理創傷</td>\n",
-       "      <td>暴力受暴經驗</td>\n",
-       "      <td>感情困境</td>\n",
-       "      <td>法律系學生</td>\n",
-       "      <td>社會工作者</td>\n",
-       "      <td>性別平權運動者</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>輕鬆思考法: 培養靈活觀點的150個啟示</td>\n",
-       "      <td>本書特色150則啟示點醒沉浮於忙碌生活的現代人！篇幅短小、內容精闢，1分鐘打開新思維！擁有不...</td>\n",
-       "      <td>https://www.eslite.com/product/100121372526824...</td>\n",
-       "      <td>焦慮</td>\n",
-       "      <td>孤獨</td>\n",
-       "      <td>成長</td>\n",
-       "      <td>職場人</td>\n",
-       "      <td>旅行愛好者</td>\n",
-       "      <td>追求自我成長的人</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>因為人類思維太僵化, 所以需要創新心理學: 心態革命, 大腦中的髮夾彎, 掀起你的思路風暴</td>\n",
-       "      <td>大腦中的髮夾彎，掀起你的思路風暴！從理性到感性，不同思考方式將會開啟新的視角、新的世界！逆向...</td>\n",
-       "      <td>https://www.eslite.com/product/100122024826824...</td>\n",
-       "      <td>焦慮</td>\n",
-       "      <td>壓��</td>\n",
-       "      <td>抑鬱</td>\n",
-       "      <td>好奇心旺盛的思考愛好者</td>\n",
-       "      <td>希望提高日常解決問題技巧的人</td>\n",
-       "      <td>渴望提升創新思維能力的人</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>人生心理學</td>\n",
-       "      <td>找出人生發展的路向從來都不容易，無論你是臨近畢業的大專生，或是已在職場上打滾了好些年正在瓶頸...</td>\n",
-       "      <td>https://www.eslite.com/product/100121238026824...</td>\n",
-       "      <td>生涯規劃</td>\n",
-       "      <td>自我覺察</td>\n",
-       "      <td>人生意義</td>\n",
-       "      <td>大學生</td>\n",
-       "      <td>職場工作者</td>\n",
-       "      <td>心理學研究者</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>672</th>\n",
-       "      <td>你想要的一切, 宇宙早已為你預備</td>\n",
-       "      <td>宇宙會把最好的獻給你。如果你願意放下自我限制的信念，全然信任這股奇妙的力量，豐盛的人生自然會...</td>\n",
-       "      <td>https://www.eslite.com/product/100120176426824...</td>\n",
-       "      <td>困惑</td>\n",
-       "      <td>壓力</td>\n",
-       "      <td>不滿</td>\n",
-       "      <td>焦慮症患者</td>\n",
-       "      <td>心理負荷過重者</td>\n",
-       "      <td>靈性追求者</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>673</th>\n",
-       "      <td>妄想的力量: 迷信、儀式感與過度樂觀的非理性心理學</td>\n",
-       "      <td>妄想雖然可恥但是有用！亞馬遜讀者五星強推！最熱愛怪力亂神的美國心理學暢銷作家帶你重新認識幻想...</td>\n",
-       "      <td>https://www.eslite.com/product/100120106326824...</td>\n",
-       "      <td>妄想</td>\n",
-       "      <td>心理假象</td>\n",
-       "      <td>樂觀</td>\n",
-       "      <td>心理學家</td>\n",
-       "      <td>精神病患者家屬</td>\n",
-       "      <td>普通大眾</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>674</th>\n",
-       "      <td>悲傷復原力: 一位心理學專家, 也是位失去愛女的母親, 透過復原力心理學, 走過分離崩解的悲傷</td>\n",
-       "      <td>面對至親至愛的離去，如果悲傷難免，我們可以做些什麼，度過這場巨大風暴？紐約時報、華爾街日報，...</td>\n",
-       "      <td>https://www.eslite.com/product/100121380726824...</td>\n",
-       "      <td>喪親之痛</td>\n",
-       "      <td>悲傷</td>\n",
-       "      <td>復原力</td>\n",
-       "      <td>親人失去</td>\n",
-       "      <td>悲傷修復</td>\n",
-       "      <td>心理健康</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>675</th>\n",
-       "      <td>淬鍊幸福, 剛剛好的回憶練習 (限量贈暖心陪伴藏書卡)</td>\n",
-       "      <td>為什麼自己會突然情緒崩潰？從什麼時候開始，變得越來越少話？每當回憶起某件事時，就會止不住的落...</td>\n",
-       "      <td>https://www.eslite.com/product/100120303926824...</td>\n",
-       "      <td>創傷後壓力症候群</td>\n",
-       "      <td>自我懷疑</td>\n",
-       "      <td>內心傷痛</td>\n",
-       "      <td>創傷癒後者</td>\n",
-       "      <td>單親父母</td>\n",
-       "      <td>成長經歷過困難的讀者</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>676</th>\n",
-       "      <td>不是為了爭吵才跟你在一起: 如何在溝通中改善親密關係</td>\n",
-       "      <td>為什麼開始親密無間的兩個人，會在關係中越走越遠、越來越疏離？外人對你們羨慕不已，但其實是假性...</td>\n",
-       "      <td>https://www.eslite.com/product/100120106326824...</td>\n",
-       "      <td>焦慮</td>\n",
-       "      <td>疏離</td>\n",
-       "      <td>衝突</td>\n",
-       "      <td>兩性關係人士</td>\n",
-       "      <td>婚姻治療師或專家</td>\n",
-       "      <td>伴侶或夫妻</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>677 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                  書名  \\\n",
-       "0                                   這僅有一次的人生, 我不想說抱歉   \n",
-       "1                       我只是想分手而已: 親密殺人, 被深愛的男人殺死的女人們   \n",
-       "2                               輕鬆思考法: 培養靈活觀點的150個啟示   \n",
-       "3      因為人類思維太僵化, 所以需要創新心理學: 心態革命, 大腦中的髮夾彎, 掀起你的思路風暴   \n",
-       "4                                              人生心理學   \n",
-       "..                                               ...   \n",
-       "672                                 你想要的一切, 宇宙早已為你預備   \n",
-       "673                        妄想的力量: 迷信、儀式感與過度樂觀的非理性心理學   \n",
-       "674  悲傷復原力: 一位心理學專家, 也是位失去愛女的母親, 透過復原力心理學, 走過分離崩解的悲傷   \n",
-       "675                      淬鍊幸福, 剛剛好的回憶練習 (限量贈暖心陪伴藏書卡)   \n",
-       "676                       不是為了爭吵才跟你在一起: 如何在溝通中改善親密關係   \n",
-       "\n",
-       "                                                  內容簡介  \\\n",
-       "0    你走太快了，容易迷路，要等靈魂跟上來，才能走更遠的路。那些你想要做成的事情，你做成了的事情，...   \n",
-       "1    親密殺人不是約會暴力是整個社會必須全力阻止的連續殺人！只是想跟他分手的我，為何最後卻送了命？...   \n",
-       "2    本書特色150則啟示點醒沉浮於忙碌生活的現代人！篇幅短小、內容精闢，1分鐘打開新思維！擁有不...   \n",
-       "3    大腦中的髮夾彎，掀起你的思路風暴！從理性到感性，不同思考方式將會開啟新的視角、新的世界！逆向...   \n",
-       "4    找出人生發展的路向從來都不容易，無論你是臨近畢業的大專生，或是已在職場上打滾了好些年正在瓶頸...   \n",
-       "..                                                 ...   \n",
-       "672  宇宙會把最好的獻給你。如果你願意放下自我限制的信念，全然信任這股奇妙的力量，豐盛的人生自然會...   \n",
-       "673  妄想雖然可恥但是有用！亞馬遜讀者五星強推！最熱愛怪力亂神的美國心理學暢銷作家帶你重新認識幻想...   \n",
-       "674  面對至親至愛的離去，如果悲傷難免，我們可以做些什麼，度過這場巨大風暴？紐約時報、華爾街日報，...   \n",
-       "675  為什麼自己會突然情緒崩潰？從什麼時候開始，變得越來越少話？每當回憶起某件事時，就會止不住的落...   \n",
-       "676  為什麼開始親密無間的兩個人，會在關係中越走越遠、越來越疏離？外人對你們羨慕不已，但其實是假性...   \n",
-       "\n",
-       "                                                   URL      形容詞1    形容詞2  \\\n",
-       "0    https://www.eslite.com/product/100120106326824...      自我期許    自我反思   \n",
-       "1    https://www.eslite.com/product/100120106326824...      心理創傷  暴力受暴經驗   \n",
-       "2    https://www.eslite.com/product/100121372526824...        焦慮      孤獨   \n",
-       "3    https://www.eslite.com/product/100122024826824...        焦慮      壓力   \n",
-       "4    https://www.eslite.com/product/100121238026824...      生涯規劃    自我覺察   \n",
-       "..                                                 ...       ...     ...   \n",
-       "672  https://www.eslite.com/product/100120176426824...        困惑      壓力   \n",
-       "673  https://www.eslite.com/product/100120106326824...        妄想    心理假象   \n",
-       "674  https://www.eslite.com/product/100121380726824...      喪親之痛      悲傷   \n",
-       "675  https://www.eslite.com/product/100120303926824...  創傷後壓力症候群    自我懷疑   \n",
-       "676  https://www.eslite.com/product/100120106326824...        焦慮      疏離   \n",
-       "\n",
-       "     形容詞3          角色1             角色2           角色3  \n",
-       "0    人生目標         自我提升            人生感悟          內心成長  \n",
-       "1    感情困境        法律系學生           社會工作者       性別平權運動者  \n",
-       "2      成長          職場人           旅行愛好者      追求自我成長的人  \n",
-       "3      抑鬱  好奇心旺盛的思考愛好者  希望提高日常解決問題技巧的人  渴望提升創新思維能力的人  \n",
-       "4    人生意義          大學生           職場工作者        心理學研究者  \n",
-       "..    ...          ...             ...           ...  \n",
-       "672    不滿        焦慮症患者         心理負荷過重者         靈性追求者  \n",
-       "673    樂觀         心理學家         精神病患者家屬          普通大眾  \n",
-       "674   復原力         親人失去            悲傷修復          心理健康  \n",
-       "675  內心傷痛        創傷癒後者            單親父母    成長經歷過困難的讀者  \n",
-       "676    衝突       兩性關係人士        婚姻治療師或專家         伴侶或夫妻  \n",
-       "\n",
-       "[677 rows x 9 columns]"
-      ]
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