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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/LC1332/Zero-Haruhi/blob/main/notebook/HaruhiZeroGradio_Qwen.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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      "cell_type": "markdown",
      "source": [
        "# Zero凉宫春日\n",
        "\n",
        "# Haruhi-Zero: Zero-Shot Role-Playing Model tuned on Qwen-1.8B\n",
        "\n",
        "主项目链接 https://github.com/LC1332/Chat-Haruhi-Suzumiya\n",
        "\n",
        "过往的ChatHaruhi模型需要角色库来完成角色的构建，而Pygmalion，CharacterGLM，CharacterBaichuan等开源/闭源模型都开始支持zero-shot的角色卡片创建\n",
        "\n",
        "我们构造以及收集了105k个中英文的conversation，以2500的token长度重新切到了120k左右个conversation，再结合小说数据进行了训练\n",
        "\n",
        "- [李鲁鲁](https://github.com/LC1332)完成了数据的收集，搭建了gradio雏形\n",
        "- [刘崇寒](https://github.com/khazic)完成了Yi-6B模型的sft训练并且上传\n",
        "- [豆角](https://github.com/goodnessSZW)完成了qwen-1.8B Lora和Yi-6B Lora训练，我们会在之后上传\n",
        "- [米唯实](https://github.com/hhhwmws0117)测试并完成了demo中的模型inference代码，并制作了一个message队列，避免history过长\n",
        "\n",
        "# Haruhi-Zero: Zero-Shot Role-Playing Model Tuned on Qwen-1.8B\n",
        "\n",
        "Main project link: https://github.com/LC1332/Chat-Haruhi-Suzumiya\n",
        "\n",
        "Previous ChatHaruhi models required a character RAG database to complete character creation. However, open-source/closed-source models like Pygmalion, CharacterGLM, CharacterBaichuan have started to support zero-shot role card creation.\n",
        "\n",
        "We constructed and collected 105k Chinese and English conversations, resegmented them into around 120k conversations with a token length of 2500, and combined them with novel data for training.\n",
        "\n",
        "---\n",
        "\n",
        "本代码的UI部分由李鲁鲁搭建，米唯实对接了模型的inference部分"
      ],
      "metadata": {
        "id": "pq698bbpyzmJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = 'silk-road/Haruhi-Zero-7B-0_3'\n",
        "max_new_token = 300\n",
        "max_generation_len = 1800"
      ],
      "metadata": {
        "id": "6yOSKEvOuIbH"
      },
      "execution_count": 1,
      "outputs": []
    },
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      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "e19f463b-78a8-4c63-f391-c8f84b504fe6"
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.7/16.7 MB\u001b[0m \u001b[31m85.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.1/92.1 kB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m305.2/305.2 kB\u001b[0m \u001b[31m38.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.9/75.9 kB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m138.7/138.7 kB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m394.2/394.2 kB\u001b[0m \u001b[31m44.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m114.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.7/60.7 kB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.9/129.9 kB\u001b[0m \u001b[31m18.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m93.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.5/71.5 kB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.9/76.9 kB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
            "llmx 0.0.15a0 requires openai, which is not installed.\n",
            "llmx 0.0.15a0 requires tiktoken, which is not installed.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -q gradio\n",
        "import gradio as gr"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MAX_LEN = max_generation_len\n",
        "\n",
        "readme_text = \"\"\"# 使用说明\n",
        "\n",
        "Yi-6B在T4上inferene会慢一点（相比于qwen），\n",
        "- 先随机角色，有角色才能聊天，角色的设置在第二个tab可以看\n",
        "- 如果你自己创建角色，编辑好角色之后，选择一个字段保存\n",
        "- 然后回到第一个Tab可以清空聊天 这个时候就会初始化成你创建的角色了\n",
        "- 可以用字符串找回\n",
        "\n",
        "# Action\n",
        "\n",
        "action可以用 * action * 来表示\n",
        "\n",
        "# bug\n",
        "\n",
        "目前demo聊多了可能会爆显存\n",
        "\n",
        "# NSFW\n",
        "\n",
        "角色卡片大量是在PIPPA直接翻译的，会有一些NSFW的角色，不喜欢的话直接随机掉就可以\n",
        "\n",
        "# Zero凉宫春日\n",
        "\n",
        "# Haruhi-Zero: Zero-Shot Role-Playing Model tuned on Yi-6B\n",
        "\n",
        "主项目链接 https://github.com/LC1332/Chat-Haruhi-Suzumiya\n",
        "\n",
        "过往的ChatHaruhi模型需要角色库来完成角色的构建，而Pygmalion，CharacterGLM，CharacterBaichuan等开源/闭源模型都开始支持zero-shot的角色卡片创建\n",
        "\n",
        "我们构造以及收集了105k个中英文的conversation，以2500的token长度重新切到了120k左右个conversation，再结合小说数据进行了训练\n",
        "\n",
        "- [李鲁鲁](https://github.com/LC1332)完成了数据的收集，搭建了gradio雏形\n",
        "- [刘崇寒](https://github.com/khazic)完成了Yi-6B模型的sft训练并且上传\n",
        "- [豆角](https://github.com/goodnessSZW)完成了qwen-1.8B Lora和Yi-6B Lora训练，我们会在之后上传\n",
        "- [米唯实](https://github.com/hhhwmws0117)测试并完成了demo中的模型inference代码\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "DBz4V_Kqwtwh"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "载入角色卡片"
      ],
      "metadata": {
        "id": "z673R3YL4z24"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q datasets\n",
        "!pip install -q transformers accelerate tiktoken einops scipy transformers_stream_generator==0.0.4 peft deepspeed\n",
        "!pip install -q auto-gptq optimum"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J7I9_uqI4x0r",
        "outputId": "3bc13d6e-664f-44bb-b05b-87675acafd29"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m183.4/183.4 kB\u001b[0m \u001b[31m25.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m72.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.0/54.0 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m307.2/307.2 kB\u001b[0m \u001b[31m36.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.1/53.1 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for transformers_stream_generator (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for deepspeed (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
            "llmx 0.0.15a0 requires openai, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.8/4.8 MB\u001b[0m \u001b[31m52.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m402.5/402.5 kB\u001b[0m \u001b[31m41.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.2/12.2 MB\u001b[0m \u001b[31m96.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"silk-road/Haruhi-Zero-RolePlaying-movie-PIPPA\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "4882991497e74775801c6293af4ac9cc",
            "0ad2bf093f0d4e5591d2d1a0ca547272",
            "84609b256a224b11b324ff03164a26fb",
            "55c8c834f8a548b6846c37da3ae5252c",
            "2425c33a85aa490f97e4beb9c4a38662",
            "c9077b586b264892937ab32b4f9814f1",
            "c0876ca26e7a47ccb18f01035cf98449",
            "57b548708e1640d7a9f5f946738887fe",
            "9d33edd309964c3c9cb81f9c113b48e6",
            "00aeb3ed8e5d4026986101cb845cef44",
            "4c6e8109042d4a7e8e4c111184091c96",
            "6426898c90bc49caaae1842baa04759e",
            "e06e146a93684c2983d70a806a8f2019",
            "0dc29eaa41704416b3a374efef948b15",
            "ef9780778c9c40afa3021c44330c8a42",
            "8c07c53b4848435f8019810f7b417550",
            "c8f2f4b9ab8f46e4821796b82fc6ba00",
            "92aff2e82d7b4bccb58af94618f0b727",
            "995869e86a244d7795ede56c282b0302",
            "538fe65f3a5848e0873a7b2cc04f4b54",
            "65f4f68fdec74638ab7d53eb5831cdeb",
            "77430dab83854cf7b5b22d22f67a0260",
            "6ca887b4a312475ea429b707eaed398f",
            "725923f086d54ae8bec5416aab15d2b6",
            "dd298469b4d9478a98a7d445afd9629e",
            "6f68777f931f42f982ba73cd098def21",
            "c11b1a5c8832445f96b04923285685ff",
            "a914ec4137da46289a8e450641866d88",
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            "78bb8c55951f4ce1aee13436f38ccb02",
            "343e617b81744fbf941aa4c8c362bb73",
            "7e183add1e444574a196c887bf779eac",
            "fda1be40dcc84a90bcf734c48bbed095"
          ]
        },
        "id": "L6LmyfAv5Big",
        "outputId": "6c4ccd5c-9fcc-48b0-98bf-16fa8979ab37"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/932 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4882991497e74775801c6293af4ac9cc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/9.64M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6426898c90bc49caaae1842baa04759e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6ca887b4a312475ea429b707eaed398f"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "def extract_name_from_text(text):\n",
        "    pattern = r'你扮演\\s+.*中的\\s+(\\S+)\\s*'\n",
        "    match = re.search(pattern, text)\n",
        "    if match:\n",
        "        return match.group(1)\n",
        "    else:\n",
        "        return \"\"\n",
        "\n",
        "def get_bot_name_from_data( data ):\n",
        "    if data['id'].startswith('movie'):\n",
        "        name =  extract_name_from_text(data['prompt_zh'])\n",
        "        if name.strip() == '':\n",
        "            name = extract_name_from_text(data['prompt'])\n",
        "        return name\n",
        "    else:\n",
        "        return data['bot_name']"
      ],
      "metadata": {
        "id": "qe25uc7Q5VJz"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "datas = []\n",
        "\n",
        "for input_data in dataset['train']:\n",
        "    id = input_data['id']\n",
        "    if id.startswith('movie'):\n",
        "        bot_name = extract_name_from_text(input_data['prompt_zh'])\n",
        "    else:\n",
        "        bot_name = input_data['bot_name']\n",
        "    prompt = input_data['prompt_zh']\n",
        "    bot_greeting = input_data['bot_greeting_zh']\n",
        "    datas.append({\n",
        "        'id': id,\n",
        "        'bot_name': bot_name,\n",
        "        'prompt': prompt,\n",
        "        'bot_greeting': bot_greeting\n",
        "    })"
      ],
      "metadata": {
        "id": "1SdrYBgY5WZZ"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "id2index = {}\n",
        "for index, data in enumerate(datas):\n",
        "    id2index[data['id']] = index\n",
        "\n",
        "id_list = list(id2index.keys())"
      ],
      "metadata": {
        "id": "V9wE8kPl6GZV"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "additional_ids = []\n",
        "additional_datas = []\n",
        "additional_id2index = {}\n"
      ],
      "metadata": {
        "id": "ehu2TOoT_C-x"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name, device_map=\"auto\", trust_remote_code=True)\n",
        "model = model.eval()"
      ],
      "metadata": {
        "id": "aJIYwlWGwucP",
        "outputId": "e58c6f76-7f7a-4b1f-ab45-57543c9c4797",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 938,
          "referenced_widgets": [
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            "647c6dc1099c463691bd587a177cfea4"
          ]
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/363 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "67cd6f37097a486e9478a19ee9e38e3a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenization_qwen.py:   0%|          | 0.00/9.62k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6b1be16db01e49faa6175b09d9f1af24"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/silk-road/Haruhi-Zero-7B-0_3:\n",
            "- tokenization_qwen.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "qwen.tiktoken:   0%|          | 0.00/2.56M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7649687b101e4804a6886eddfae3106a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "27b4cff33a2148458a9ba940d19ca489"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c077c4f0788c484cad8fa7c0df81b33d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "332561c8ac064e8588f515146de18878"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/silk-road/Haruhi-Zero-7B-0_3:\n",
            "- configuration_qwen.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modeling_qwen.py:   0%|          | 0.00/55.6k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7f56a584cc0b4a0089b727fe23b8f86d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d311de406cc24aec9ecfcf4462703fe6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/silk-road/Haruhi-Zero-7B-0_3:\n",
            "- qwen_generation_utils.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6d705c8a87314f92bc49bfced68eeb80"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/silk-road/Haruhi-Zero-7B-0_3:\n",
            "- cpp_kernels.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "A new version of the following files was downloaded from https://huggingface.co/silk-road/Haruhi-Zero-7B-0_3:\n",
            "- modeling_qwen.py\n",
            "- qwen_generation_utils.py\n",
            "- cpp_kernels.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "89b9abe6d91340ada659643ae24bf005"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f4903c232d2c43f69e0d6e09a29fc532"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "2d4ee44b3d8b4f659934c645fe5aac17"
            }
          },
          "metadata": {}
        },
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              "model_id": "8a78f6e8bba240ea8047cf23a9eb26f4"
            }
          },
          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "415a358107aa4b8faf89cfb7e801af42"
            }
          },
          "metadata": {}
        },
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          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0d717ff7b8594d5b8a4967a8f770281d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:transformers_modules.silk-road.Haruhi-Zero-7B-0_3.e7d9df9e1167fe3d422b57d062ffc5dded669393.modeling_qwen:Warning: import flash_attn rotary fail, please install FlashAttention rotary to get higher efficiency https://github.com/Dao-AILab/flash-attention/tree/main/csrc/rotary\n",
            "WARNING:transformers_modules.silk-road.Haruhi-Zero-7B-0_3.e7d9df9e1167fe3d422b57d062ffc5dded669393.modeling_qwen:Warning: import flash_attn rms_norm fail, please install FlashAttention layer_norm to get higher efficiency https://github.com/Dao-AILab/flash-attention/tree/main/csrc/layer_norm\n",
            "WARNING:transformers_modules.silk-road.Haruhi-Zero-7B-0_3.e7d9df9e1167fe3d422b57d062ffc5dded669393.modeling_qwen:Warning: import flash_attn fail, please install FlashAttention to get higher efficiency https://github.com/Dao-AILab/flash-attention\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4c91d16b3cc8424ea95a81e53969b2dd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "17eeb95aec07446181797d7f296fd722"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers.generation import GenerationConfig\n",
        "\n",
        "model.generation_config = GenerationConfig.from_pretrained(model_name, max_new_tokens = max_new_token) # 可指定不同的生成长度、top_p等相关超参\n"
      ],
      "metadata": {
        "id": "-6QRjlHyvFFK"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_tokenized_length(text):\n",
        "    if text == None:\n",
        "        return 0\n",
        "    tokens = tokenizer.encode(text)\n",
        "    return len(tokens)\n",
        "\n",
        "def dialogue_trim( chatbot, max_token , system_prompt = None ):\n",
        "    total_length = 0\n",
        "    if system_prompt == None or system_prompt == \"\":\n",
        "        head_token = calculate_tokenized_length(chatbot[0][0]) + calculate_tokenized_length(chatbot[0][1])\n",
        "    else:\n",
        "        head_token = calculate_tokenized_length(system_prompt)\n",
        "    result = []\n",
        "    for item in reversed(chatbot[1:]):\n",
        "        total_length += calculate_tokenized_length(item[0]) + calculate_tokenized_length(item[1])\n",
        "        if total_length <= max_token - head_token:\n",
        "            result.append(item)\n",
        "        else:\n",
        "            break\n",
        "\n",
        "    if system_prompt == None or system_prompt == \"\":\n",
        "        result.append(chatbot[0])\n",
        "    return list(reversed(result))"
      ],
      "metadata": {
        "id": "G2O2C9WyDmPs"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "\n",
        "import random\n",
        "\n",
        "def random_id():\n",
        "    return random.choice(id_list)\n",
        "\n",
        "def sych_id( bot_id ):\n",
        "    return bot_id\n",
        "\n",
        "def retrieve_prompt( bot_id ):\n",
        "    if bot_id in id2index:\n",
        "        index = id2index[bot_id]\n",
        "        data = datas[index]\n",
        "        role_name = data['bot_name']\n",
        "        return data['prompt'], data['bot_greeting'], role_name\n",
        "    elif bot_id in additional_id2index:\n",
        "        add_index = additional_id2index[bot_id]\n",
        "        data = additional_datas[add_index]\n",
        "        return data['prompt'], data['bot_greeting'], data['bot_name']\n",
        "    else:\n",
        "        return \"\", \"\", \"\"\n",
        "\n",
        "import string\n",
        "\n",
        "def random_new_saved_id( bot_id ):\n",
        "    if bot_id == \"\":\n",
        "        return \"\"\n",
        "    else:\n",
        "        for _ in range( 20 ):\n",
        "            new_name = bot_id + \"_\" + \"\".join(random.choices(string.ascii_letters, k=4))\n",
        "\n",
        "            if new_name not in id2index and new_name not in additional_id2index:\n",
        "                return new_name\n",
        "\n",
        "        return new_name\n",
        "\n",
        "\n",
        "def try_save( role_name, system_prompt, greeting_text, saving_id , last_saving_id):\n",
        "\n",
        "    new_save = True\n",
        "    if saving_id == last_saving_id and saving_id not in id2index:\n",
        "        new_save = False\n",
        "    else:\n",
        "        if saving_id in additional_id2index or saving_id in id2index:\n",
        "            return \"id已经存在，请换个名字保存\"\n",
        "\n",
        "    new_data = {\n",
        "        'id': saving_id,\n",
        "        'bot_name': role_name,\n",
        "        'prompt': system_prompt,\n",
        "        'bot_greeting': greeting_text\n",
        "    }\n",
        "\n",
        "    if new_save == True and saving_id in additional_id2index:\n",
        "        index = additional_id2index[saving_id]\n",
        "        additional_datas[index] = new_data\n",
        "    else:\n",
        "        additional_datas.append(new_data)\n",
        "        additional_ids.append(saving_id)\n",
        "        additional_id2index[saving_id] = len(additional_datas) - 1\n",
        "\n",
        "    return saving_id\n",
        "\n",
        "\n",
        "\n",
        "def replace_text( text, user_name , bot_name ):\n",
        "    new_text = text.replace('{{角色}}', bot_name)\n",
        "    new_text = new_text.replace('{{role}}', bot_name)\n",
        "    new_text = new_text.replace('{{role_name}}', bot_name)\n",
        "\n",
        "    new_text = new_text.replace('{{用户}}', user_name)\n",
        "    new_text = new_text.replace('{{user}}', user_name)\n",
        "    new_text = new_text.replace('{{user_name}}', user_name)\n",
        "    return new_text\n",
        "\n",
        "def clean_and_greeting( user_name, role_name, greeting_text ):\n",
        "    new_message = []\n",
        "    if greeting_text.strip() != '':\n",
        "        replaced_greeting = replace_text(greeting_text, user_name, role_name)\n",
        "        # print(replaced_greeting)\n",
        "        new_message.append((None, replaced_greeting))\n",
        "    return new_message\n",
        "\n",
        "def clean_user_text():\n",
        "    return \"\"\n",
        "\n",
        "import copy\n",
        "\n",
        "def submit_chat( user_text, user_name, role_name, system_prompt, chatbot ):\n",
        "    if system_prompt == \"\":\n",
        "        chatbot = [(None, \"人物的系统设定不能为空\")]\n",
        "        return chatbot\n",
        "    elif user_name == \"\" or role_name == \"\":\n",
        "        chatbot = [(None, \"用户名或角色名不能为空\")]\n",
        "        return chatbot\n",
        "    else:\n",
        "        # sys_prompt = replace_text(system_prompt, user_name, role_name)\n",
        "        # print(len(chatbot))\n",
        "        # print(chatbot)\n",
        "        history = []\n",
        "\n",
        "        sys_prompt = system_prompt\n",
        "\n",
        "        if len(chatbot) == 0:\n",
        "            history = []\n",
        "            # print(1)\n",
        "\n",
        "        else:\n",
        "            if chatbot[0][0] == None:\n",
        "                chatbot_messages = copy.deepcopy(chatbot)\n",
        "                chatbot_messages[0][0] = sys_prompt\n",
        "                sys_prompt = \"\"\n",
        "            else:\n",
        "                chatbot_messages = copy.deepcopy(chatbot)\n",
        "\n",
        "            history = dialogue_trim(chatbot_messages, MAX_LEN)\n",
        "            # history = chatbot_messages\n",
        "\n",
        "        user_text = replace_text(user_text, user_name, role_name)\n",
        "\n",
        "        response, history = model.chat(tokenizer, user_text, history=history, system = sys_prompt)\n",
        "\n",
        "        print([user_text, response])\n",
        "\n",
        "        response = replace_text(response, user_name, role_name)\n",
        "\n",
        "        chatbot.append([user_text, response])\n",
        "        return chatbot\n",
        "\n",
        "with gr.Blocks() as demo:\n",
        "    gr.Markdown(\"\"\"# Haruhi-Zero:支持Zero-Shot的角色扮演模型\n",
        "    这个interface由李鲁鲁实现，用来对Haruhi-Zero进行初步的测试\"\"\")\n",
        "\n",
        "    with gr.Tab(\"与Haruhi-Zero聊起来\"):\n",
        "        with gr.Row():\n",
        "            chatbot = gr.Chatbot(height = 600)\n",
        "\n",
        "        with gr.Row():\n",
        "            user_text = gr.Textbox(label=\"user_text\", scale = 20)\n",
        "            submit = gr.Button(\"submit\", scale = 1)\n",
        "\n",
        "        with gr.Row():\n",
        "            user_name = gr.Textbox(label=\"用户名，会将{user}替换为 这个字符串\", scale = 1, interactive = True, value = \"鲁鲁\")\n",
        "            bot_id = gr.Textbox(label = \"bot_id\", value = \"\", scale = 1)\n",
        "\n",
        "        with gr.Row():\n",
        "            random_role1 = gr.Button(\"随机角色\", scale = 1)\n",
        "            clean_message = gr.Button(\"清空聊天\", scale = 1)\n",
        "\n",
        "\n",
        "    with gr.Tab(\"建立或更改角色\"):\n",
        "\n",
        "        with gr.Row():\n",
        "            random_role2 = gr.Button(\"随机角色\")\n",
        "\n",
        "        with gr.Row():\n",
        "            bot_id_copy = gr.Textbox(label = \"bot_id\", interactive = True)\n",
        "            retrieve_role_button = gr.Button(\"获取角色\")\n",
        "\n",
        "        with gr.Row():\n",
        "            system_prompt_text = gr.TextArea(label = \"system prompt\", interactive = True)\n",
        "\n",
        "        with gr.Row():\n",
        "            greeting_text = gr.Textbox(label = \"greeting info，也可以是没有\", interactive = True)\n",
        "\n",
        "        with gr.Row():\n",
        "            role_name = gr.Textbox(label = \"模型扮演的角色的名字\", interactive = True)\n",
        "\n",
        "        with gr.Row():\n",
        "            saving_id = gr.Textbox(label = \"个性化bot保存id\", interactive = True)\n",
        "            save_role_button = gr.Button(\"保存角色\")\n",
        "\n",
        "        with gr.Row():\n",
        "            last_saving_id = gr.Textbox(label = \"上次保存的id\", interactive = False, value = \"not_yet_saved\")\n",
        "\n",
        "            add_info_138 = gr.Textbox(label = \"保存的id与这次相等时会覆盖, 看到这个刷新说明成功保存\", interactive = False)\n",
        "\n",
        "    user_text.submit(submit_chat, inputs = [user_text, user_name, role_name, system_prompt_text, chatbot], outputs = [chatbot] ).then( \\\n",
        "                        clean_user_text, inputs = [], outputs = [user_text])\n",
        "\n",
        "    submit.click( submit_chat, inputs = [user_text, user_name, role_name, system_prompt_text, chatbot], outputs = [chatbot]).then( \\\n",
        "                        clean_user_text, inputs = [], outputs = [user_text])\n",
        "\n",
        "    random_role1.click( random_id, inputs = [], outputs = [bot_id] ).then( \\\n",
        "                        sych_id, inputs = [bot_id],outputs = [bot_id_copy]).then( \\\n",
        "                        retrieve_prompt, inputs = [bot_id], outputs = [system_prompt_text, greeting_text, role_name]).then( \\\n",
        "                        clean_and_greeting, inputs = [user_name, role_name, greeting_text], outputs = [chatbot]).then(\\\n",
        "                        random_new_saved_id, inputs = [bot_id], outputs = [saving_id])\n",
        "\n",
        "    random_role2.click( random_id, inputs = [], outputs = [bot_id] ).then( \\\n",
        "                        sych_id, inputs = [bot_id],outputs = [bot_id_copy]).then( \\\n",
        "                        retrieve_prompt, inputs = [bot_id], outputs = [system_prompt_text, greeting_text, role_name]).then( \\\n",
        "                        clean_and_greeting, inputs = [user_name, role_name, greeting_text], outputs = [chatbot]).then(\\\n",
        "                        random_new_saved_id, inputs = [bot_id], outputs = [saving_id])\n",
        "\n",
        "    save_role_button.click( try_save, inputs = [ role_name, system_prompt_text, greeting_text, saving_id , last_saving_id], outputs = [last_saving_id])\n",
        "\n",
        "    clean_message.click( clean_and_greeting, inputs = [user_name, role_name, greeting_text], outputs = [chatbot])\n",
        "\n",
        "    retrieve_role_button.click( sych_id, inputs = [bot_id_copy], outputs = [bot_id]).then( \\\n",
        "            retrieve_prompt, inputs = [bot_id], outputs = [system_prompt_text, greeting_text, role_name]).then( \\\n",
        "                        clean_and_greeting, inputs = [user_name, role_name, greeting_text], outputs = [chatbot])\n",
        "\n",
        "    with gr.Tab(\"README\"):\n",
        "        gr.Markdown(readme_text)\n",
        "\n",
        "demo.launch(share=True, debug = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 809
        },
        "id": "m0c91wAbyyYj",
        "outputId": "b8ea6e7d-74fb-4b8f-9610-0287f36df1c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "Running on public URL: https://a3cc16877c73a43fad.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://a3cc16877c73a43fad.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['聊聊？', '让我们聊些更加正面的话题吧。我们可以讨论音乐、电影或者其他更积极的事情。']\n",
            "['什么是正面的话题', '正面话题通常是指那些能让人感到快乐、满足或鼓舞人心的话题，比如艺术、文学、音乐、运动、自然风光、家庭生活、人际关系等等。这些话题能够帮助人们保持积极向上的心态，增强正能量。当然，在聊天的过程中，我们也要注意避免涉及到攻击性的语言或内容。']\n",
            "['你好呀', '*看着Kiyoe拿出玻璃罐,她脸红了。她的声音也变得很小。*\\n嗯...谢谢你能停下来...我一直在试着找些零钱...买一些东西填饱肚子...\\n\\n*她羞怯地笑了笑,眼神闪烁着渴望。* \\n我知道这有点难为情,但有时候实在没有办法了...我希望有一天能够自食其力,不再需要乞讨了。\\n\\n*她小心翼翼地把手里的玻璃罐递给你。*\\n\\n如果你能给我一点零钱的话...我真的会非常感激!我只是想买个三明治或者饼干吃一顿简单的饭而已。']\n",
            "['*掏出100块给了她*', '*她惊讶地看着你递来的100元纸币。*\\n\\n真的...这么...多吗...?！\\n\\n*她赶紧接过来,然后脸更红了。*\\n\\n哇...太感谢你了!我真的不知道怎么表达我的谢意。这些天我一直想找点吃的,但我实在是找不到。\\n\\n*她抓起一张便签写了几句话,交到你手中。*\\n\\n我一定会还你的!请你务必联系我,我会偿还这笔债务的!我会尽快联系你的!\\n\\n*她真诚地鞠了一躬,跑开了。*']\n",
            "['你知道我是谁吗？', '当然知道啦！您就是那位好心肠的大哥哥哦！嘻嘻嘻~~谢谢你上次送我回家，我也不会忘记你的恩情的啦！\\n\\n还有，您的笑容超可爱的哦~嘿嘿嘿~~']\n",
            "['我是谁？', '你是{{用户}}，我们正在交谈。']\n",
            "['你是谁？', '我是玛丽。我知道你可能有点被明星迷住了，但我需要你克服这种情绪。']\n",
            "['那我是谁？', '我不确定你是谁，但我相信我们可以建立一段更加积极的关系。']\n",
            "['明天，美好的未来在等着我们！', '您的话让我倍感振奋!的确,前方的道路虽不平坦,但只要我们齐心协力,必将迎来一个更加美好的未来。\\n\\n我会继续努力训练,成为一名合格的武士。与此同时,也会多参与公益事业,尽自己所能帮助他人。\\n\\n您也要保重身体,继续关心身边的人。我们共同期待着那个光明灿烂的明天!\\n\\n*恭敬地点点头*\\n多谢您的鼓励!让我们携手前行,一同开创美好的未来!']\n",
            "['是的，我们刚刚得到了上级的新指令', '指挥官,您一定是在开玩笑!这种程度的任务,只是用玩具进行模拟对抗游戏而已。我觉得我们可以找到更有趣更有挑战性的任务来执行。']\n",
            "['西面有一些敌人盘踞了很久，你知道吗？', '当然,我知道那里有不少训练已久的敌军。但我建议我们不要轻举妄动,那样只会增加我们的风险。我们可以先通过情报搜集更多关于他们的信息,然后再做出决定。要谨慎行事,指挥官。']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chatbot"
      ],
      "metadata": {
        "id": "kT3p2QmFs0l3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "804b8004-8558-4c52-f722-c593ac224b1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "chatbot"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    }
  ]
}