冒險24_《紅樓夢》生成器.ipynb

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  "metadata": {
    "colab": {
      "name": "冒險24_《紅樓夢》生成器",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yenlung/Python-AI-Book/blob/main/%E5%86%92%E9%9A%AA24_%E3%80%8A%E7%B4%85%E6%A8%93%E5%A4%A2%E3%80%8B%E7%94%9F%E6%88%90%E5%99%A8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MspAqe3DUKcr"
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yyh-PyRXQc44"
      },
      "source": [
        "### 1. 讀入套件"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BuS7XODDUOrp"
      },
      "source": [
        "import tensorflow as tf\n",
        "import pickle\n",
        "\n",
        "from tensorflow.keras.models import load_model, model_from_json"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "708oJY4vURVw"
      },
      "source": [
        "### 2. 讀入訓練好的 RNN 紅樓夢生成器"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget --no-check-certificate \\\n",
        "    https://github.com/yenlung/Python-AI-Book/blob/main/dream_rnn.zip?raw=true \\\n",
        "    -O /content/dream_rnn.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hSN8yx434wKC",
        "outputId": "c9fd5dcf-a01b-46d9-aa3b-bbfb4f925a75"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-02-19 09:52:39--  https://github.com/yenlung/Python-AI-Book/blob/main/dream_rnn.zip?raw=true\n",
            "Resolving github.com (github.com)... 140.82.114.4\n",
            "Connecting to github.com (github.com)|140.82.114.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github.com/yenlung/Python-AI-Book/raw/main/dream_rnn.zip [following]\n",
            "--2023-02-19 09:52:39--  https://github.com/yenlung/Python-AI-Book/raw/main/dream_rnn.zip\n",
            "Reusing existing connection to github.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/yenlung/Python-AI-Book/main/dream_rnn.zip [following]\n",
            "--2023-02-19 09:52:39--  https://raw.githubusercontent.com/yenlung/Python-AI-Book/main/dream_rnn.zip\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 11088004 (11M) [application/zip]\n",
            "Saving to: ‘/content/dream_rnn.zip’\n",
            "\n",
            "/content/dream_rnn. 100%[===================>]  10.57M  --.-KB/s    in 0.09s   \n",
            "\n",
            "2023-02-19 09:52:40 (112 MB/s) - ‘/content/dream_rnn.zip’ saved [11088004/11088004]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import zipfile\n",
        "\n",
        "local_zip = '/content/dream_rnn.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/content')\n",
        "zip_ref.close()"
      ],
      "metadata": {
        "id": "OFWxNWFi5U5W"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-AghtUi3V8xB"
      },
      "source": [
        "f = open('architecture.json', 'r')\n",
        "loaded_model = f.read()\n",
        "f.close()"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K-GTCtDzWQdB",
        "outputId": "de312c8e-acf2-4fb4-a96d-2d7061434a76"
      },
      "source": [
        "model = model_from_json(loaded_model)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F-ucoaMEWhf5"
      },
      "source": [
        "model.load_weights(\"weights.h5\")"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "【更新】TensorFlow 更新後, 我們發現需要這個步驟。"
      ],
      "metadata": {
        "id": "mG6aVwV4J6rN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install Keras-Preprocessing"
      ],
      "metadata": {
        "id": "shOtiTrWGRPE",
        "outputId": "eecedc55-9e82-44a2-de91-86e34760ac12",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting Keras-Preprocessing\n",
            "  Downloading Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/42.6 KB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.6/42.6 KB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.8/dist-packages (from Keras-Preprocessing) (1.15.0)\n",
            "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.8/dist-packages (from Keras-Preprocessing) (1.21.6)\n",
            "Installing collected packages: Keras-Preprocessing\n",
            "Successfully installed Keras-Preprocessing-1.1.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZgCD002QWsUb"
      },
      "source": [
        "f = open('tokenizer.pkl', 'rb')\n",
        "tokenizer = pickle.load(f)\n",
        "f.close()"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8uMS1542VbWw"
      },
      "source": [
        "#### 【已下載紅樓夢生成器請執行這段】\n",
        "\n",
        "如果你已經在[我的 GitHub](https://github.com/yenlung/Deep-Learning-Basics) 中下載紅樓夢生成模型, 這包括:\n",
        "\n",
        "1. `dream_rnn` 資料夾, 這是模型和訓練好的權重。\n",
        "2. `dream_tokenizer2.pkl` 檔案, 這是模型使用的 tokenizer。\n",
        "\n",
        "存到你的 Google Drive, 放 Colab 程式的地方 (預設是 `Colab Notebooks` 資料夾)。那執行下面這一段, 不用再讀入一次模型資料。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gVTnboB5cV6J"
      },
      "source": [
        "#from google.colab import drive\n",
        "\n",
        "#drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I3SHSNpedT1D"
      },
      "source": [
        "#%cd '/content/drive/MyDrive/Colab Notebooks/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RJDe7oqFWeBL"
      },
      "source": [
        "#model = load_model('dream_rnn')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cxeqaCs4Y7G9"
      },
      "source": [
        "#f = open('dream_tokenizer2.pkl', 'rb')\n",
        "#tokenizer = pickle.load(f)\n",
        "#f.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gV5LKUzwY_Yw"
      },
      "source": [
        "### 3. 打造紅樓夢生成器"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6U2xs9ZOgoB-"
      },
      "source": [
        "max_id = len(tokenizer.word_index)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_id"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zuo_3igF6UnB",
        "outputId": "a4d281a3-b322-42e7-e21a-f1fb7ed7bbca"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4522"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sDMLOoVqJ0k5"
      },
      "source": [
        "接下來是一段文字, 我們用事先訓練好的 tokenizer 換成一段數字, 最後用 one-hot encoding 回傳。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R5b60vWjgo9t"
      },
      "source": [
        "def preprocess(texts):\n",
        "    X = np.array(tokenizer.texts_to_sequences([texts]))-1\n",
        "    return tf.one_hot(X, max_id)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-qwEE6kEKJBW"
      },
      "source": [
        "這段程式主要依輸入的一段文字, 用我們的 model 去預測下一個字。注意像平常的分類問題, 這裡輸出是每人個字出現機率最高的。但都照這樣, 我們輸入同一段文字, 之後出現的文字永遠是一樣的! 常用的手法是去設定 `temperature`, `temperature` 接近 0, 大致上就取機率最高的字; `temperature` 越大就越隨機。太隨機就變成亂數取字! 一般 `temperature`設 1 左右效果最佳。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ddA1r_Ggtoh"
      },
      "source": [
        "def next_char(texts, temperature=1):\n",
        "    X_new = preprocess(texts)\n",
        "    y_predict = model.predict(X_new)[0, -1:, :]\n",
        "    rescaled_logits = tf.math.log(y_predict) / temperature\n",
        "    char_id = tf.random.categorical(rescaled_logits, num_samples=1) + 1\n",
        "    return tokenizer.sequences_to_texts(char_id.numpy())[0]"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YBuIiQ59Liwk"
      },
      "source": [
        "最後就一段文字進來，再產生 `n_chars` 這麼多個字。我們原本一段文字只能生一個字, 那就一次生一個字, 最後要生多少個字就生多少個字。\n",
        "\n",
        "原本訓練我們一段是 100 個字去訓練的, 這裡超過 100 字時我們就取最後 100 個字丟入模型。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aCOZ4EtlgwFc"
      },
      "source": [
        "def complete_text(texts, n_chars=50, temperature=1):\n",
        "    n_chars=int(n_chars)\n",
        "    for _ in range(n_chars):\n",
        "        texts = texts + next_char(texts[-100:], temperature)\n",
        "    return texts"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. 測試紅樓夢生成器"
      ],
      "metadata": {
        "id": "BNqrbch-3BeM"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "IQRXr6KQg36C",
        "outputId": "d298bca0-0617-4857-b03f-36708c1d255f"
      },
      "source": [
        "complete_text(\"自孫悟空從石頭中蹦出來之後，\", n_chars=300, temperature=1.2)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "text/plain": [
              "'自孫悟空從石頭中蹦出來之後，先飛報單的。賈政嘆口氣。手又賞了一本，與他老先邀雨村回話。\\n\\n那瓦礫明乍寒了一番補入，毫不進，說著：「悟如從此，從天入三洞天。因開燈，古道今日，原來如今的奇處，顏色大大道如此。」士隱自趕的道：「二老爺通報仙草已經失了那仙翁，你把這條猩紅氈的斗篷，向小子同賈珍珠方相認，俟薛姨媽那邊設停寺內。\\n\\n次日，鳳姐等分頭取藥那劉姥姥家的，眾人打發人去給邢夫人辭了眾人。王夫人命賈蘭跟了侍兒去，分頭只一顧頭回來看著賈環的相思。王夫人便告訴了平兒，在賈芸家外沒有，不敢答言，只有好心盛妝。\\n\\n那空空牢牢記著此言，又把五更五更收心起題，寫著書去。回奏：「皇上隆恩，內眷們不及大理的。」賈政帶著賈蘭場中迷失。\\n\\n賈政回'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dpUaHxdug6sI"
      },
      "source": [
        "### 5. 用 `gradio` 做成一個網路 app\n",
        "\n",
        "我們準備用 [`gradio`](https://gradio.app/) 套件, 神速做完一個 web app。最酷的是, 最後出現 `https://xxxx.gradio.app` 那個網址, 在你的 Colab 還在執行的時候, 任何人都可以用任何瀏覽器連進來使用!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yd5zdVNHhPla",
        "outputId": "308da706-d5f9-48d6-833b-b5db4491ff7a"
      },
      "source": [
        "!pip install gradio"
      ],
      "execution_count": 20,
      "outputs": [
        {
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            "\u001b[?25hCollecting httpcore<0.17.0,>=0.15.0\n",
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            "\u001b[?25hCollecting sniffio\n",
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            "Requirement already satisfied: certifi in /usr/local/lib/python3.8/dist-packages (from httpx->gradio) (2022.12.7)\n",
            "Collecting rfc3986[idna2008]<2,>=1.3\n",
            "  Downloading rfc3986-1.5.0-py2.py3-none-any.whl (31 kB)\n",
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            "Collecting uc-micro-py\n",
            "  Downloading uc_micro_py-1.0.1-py3-none-any.whl (6.2 kB)\n",
            "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.8/dist-packages (from importlib-resources>=1.4.0->jsonschema>=3.0->altair>=4.2.0->gradio) (3.13.0)\n",
            "Building wheels for collected packages: ffmpy, python-multipart\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4711 sha256=62dcd1242caa0ffc57f81d22418bec87abb8a65803a3c9524626b786df3a57a7\n",
            "  Stored in directory: /root/.cache/pip/wheels/ff/5b/59/913b443e7369dc04b61f607a746b6f7d83fb65e2e19fcc958d\n",
            "  Building wheel for python-multipart (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for python-multipart: filename=python_multipart-0.0.5-py3-none-any.whl size=31678 sha256=4e22df397560bb9f971ccb15994a61e7627e22a9dd442d8389daf8dff0bf6459\n",
            "  Stored in directory: /root/.cache/pip/wheels/9e/fc/1c/cf980e6413d3ee8e70cd8f39e2366b0f487e3e221aeb452eb0\n",
            "Successfully built ffmpy python-multipart\n",
            "Installing collected packages: rfc3986, pydub, ffmpy, websockets, uc-micro-py, sniffio, python-multipart, pycryptodome, orjson, mdurl, h11, aiofiles, uvicorn, markdown-it-py, linkify-it-py, anyio, starlette, mdit-py-plugins, httpcore, httpx, fastapi, gradio\n",
            "Successfully installed aiofiles-23.1.0 anyio-3.6.2 fastapi-0.92.0 ffmpy-0.3.0 gradio-3.18.0 h11-0.14.0 httpcore-0.16.3 httpx-0.23.3 linkify-it-py-1.0.3 markdown-it-py-2.1.0 mdit-py-plugins-0.3.4 mdurl-0.1.2 orjson-3.8.6 pycryptodome-3.17 pydub-0.25.1 python-multipart-0.0.5 rfc3986-1.5.0 sniffio-1.3.0 starlette-0.25.0 uc-micro-py-1.0.1 uvicorn-0.20.0 websockets-10.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qNiAIAyehTXi"
      },
      "source": [
        "import gradio as gr"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iface = gr.Interface(\n",
        "    fn=complete_text,\n",
        "    inputs=[\n",
        "        \"text\",\n",
        "        gr.Slider(minimum=50, maximum=200, value=50, step=1),\n",
        "        gr.Slider(minimum=0.2, maximum=2, value=1, step=0.2)],\n",
        "    outputs=\"text\",\n",
        "    title=\"紅樓夢生成器\",\n",
        "    description=\"起個頭, 幫你完成一段紅樓夢。可以改變 temperature, 越小生出的字越固定, 越大越隨機。\")\n",
        "iface.launch(share=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "6UdJCXIqGXc8",
        "outputId": "4e8fc187-75a2-462c-f2e4-6e7093dc697d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://1f503da7-20ec-4fbe.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://1f503da7-20ec-4fbe.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    }
  ]
}