Pippy_Assistant_codeLlama.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# Selecting any one of codellama language model\n",
        "model_id = \"TheBloke/CodeLlama-7B-Instruct-GPTQ\"\n",
        "# model_id = \"TheBloke/CodeLlama-13B-Instruct-GPTQ\""
      ],
      "metadata": {
        "id": "NCHZB6G2PIDM"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Configuration\n",
        "runtimeFlag = \"cuda:0\"\n",
        "cache_dir = None\n",
        "scaling_factor = 1.0"
      ],
      "metadata": {
        "id": "V1tsB0SwOl4B"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Setting the SYSTEM PROMPT to generate Python codes specifically for kids\n",
        "DEFAULT_SYSTEM_PROMPT = 'You are an assistant who writes only Python codes for kids. Besides code you also provide simple explainantion to support it.'\n",
        "SYSTEM_PROMPT = DEFAULT_SYSTEM_PROMPT\n",
        "\n",
        "print(SYSTEM_PROMPT)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XROVA4cTJe9o",
        "outputId": "9c2cc20a-6966-4204-d9de-96667558db22"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You are an assistant who writes only Python codes for kids. Besides code you also provide simple explainantion to support it.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mounting the model to Gdrive"
      ],
      "metadata": {
        "id": "X-PAv_Oy1Pr2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "oAzlPhWXVKXo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f0d0265-a704-46d6-b026-0e7779b4b06c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Allow the model to be saved to Google Drive for faster startup next time\n",
        "\n",
        "# This is the path to the Google Drive folder.\n",
        "drive_path = \"/content/drive\"\n",
        "\n",
        "# This is the path where you want to store your cache.\n",
        "cache_dir_path = os.path.join(drive_path, \"My Drive/huggingface_cache\")\n",
        "\n",
        "# Check if the Google Drive folder exists. If it does, use it as the cache_dir.\n",
        "# If not, set cache_dir to None to use the default Hugging Face cache location.\n",
        "if os.path.exists(drive_path):\n",
        "    cache_dir = cache_dir_path\n",
        "    os.makedirs(cache_dir, exist_ok=True) # Ensure the directory exists\n",
        "else:\n",
        "    cache_dir = None\n",
        "\n",
        "print(cache_dir)"
      ],
      "metadata": {
        "id": "MH8ZqAEcVLTH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "747d7a1a-91f1-4bda-f173-c012f53490c4"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/My Drive/huggingface_cache\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q -U transformers peft accelerate optimum\n",
        "\n",
        "!pip install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/"
      ],
      "metadata": {
        "id": "Y-HYbuJ6JCgd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cfa6f6e8-33cd-4932-ba50-66f1e473ee98"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hLooking in indexes: https://pypi.org/simple, https://huggingface.github.io/autogptq-index/whl/cu118/\n",
            "Collecting auto-gptq\n",
            "  Downloading https://huggingface.github.io/autogptq-index/whl/cu118/auto-gptq/auto_gptq-0.7.1%2Bcu118-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (23.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.7/23.7 MB\u001b[0m \u001b[31m40.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: accelerate>=0.26.0 in /usr/local/lib/python3.10/dist-packages (from auto-gptq) (0.29.2)\n",
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            "  Downloading rouge-1.0.1-py3-none-any.whl (13 kB)\n",
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            "  Downloading gekko-1.1.1-py3-none-any.whl (13.2 MB)\n",
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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.13.0->auto-gptq) (1.3.0)\n",
            "Installing collected packages: rouge, gekko, auto-gptq\n",
            "Successfully installed auto-gptq-0.7.1+cu118 gekko-1.1.1 rouge-1.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q -U pdfminer.six # could maybe add pre-built wheels to speed this up."
      ],
      "metadata": {
        "id": "rpbHJwz5jIRc",
        "outputId": "447de935-55b9-4726-d309-648b472e9166",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.6/5.6 MB\u001b[0m \u001b[31m19.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "7wgbsa4U2TBk"
      },
      "outputs": [],
      "source": [
        "import locale\n",
        "def getpreferredencoding(do_setlocale = True):\n",
        "    return \"UTF-8\"\n",
        "locale.getpreferredencoding = getpreferredencoding"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    device_map=\"auto\",\n",
        "    )\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)"
      ],
      "metadata": {
        "id": "KMXDTtTfye8M",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330,
          "referenced_widgets": [
            "79bf794f28534945bf8b275e38894a13",
            "93db3b3bd5f049e0abe9a7163ac69651",
            "700829c357f9499db9214bac0d3ccc7b",
            "8409f176171341b8b898e39345d439e1",
            "0aa16cbb8a3944779760b3a2b13e2aa6",
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        {
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          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.25k [00:00<?, ?B/s]"
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead\n",
            "  warnings.warn(\n",
            "The cos_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use the forward method of RoPE from now on instead. It is not used in the `LlamaAttention` class\n",
            "The sin_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use the forward method of RoPE from now on instead. It is not used in the `LlamaAttention` class\n"
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          "metadata": {}
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          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/411 [00:00<?, ?B/s]"
            ],
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Setting up user interface of Pippy's AI-assistant for kids 🐍**\n"
      ],
      "metadata": {
        "id": "uOVCQfjYUgHN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import display, HTML, clear_output, Markdown\n",
        "import textwrap, json\n",
        "import ipywidgets as widgets\n",
        "import re, time\n",
        "import torch\n",
        "from google.colab import files\n",
        "from pdfminer.high_level import extract_text\n",
        "import io"
      ],
      "metadata": {
        "id": "LLQlqxAIgFzF"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "B_INST, E_INST = \"[INST]\", \"[/INST]\"\n",
        "B_SYS, E_SYS = \"<<SYS>>\\n\", \"\\n<</SYS>>\\n\\n\"\n",
        "\n",
        "# max_doc_length = 50\n",
        "max_context = int(model.config.max_position_embeddings*scaling_factor)\n",
        "max_doc_length = int(0.75 * max_context)  # max doc length is 75% of the context length\n",
        "max_doc_words = int(max_doc_length)"
      ],
      "metadata": {
        "id": "S2YZZel5hjLc"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_response(dialogs, temperature=0.01, top_p=0.9, logprobs=False):\n",
        "    torch.cuda.empty_cache()\n",
        "    # print(json.dumps(dialogs, indent=4))\n",
        "    max_prompt_len = int(0.85 * max_context)\n",
        "    max_gen_len = int(0.10 * max_prompt_len)\n",
        "\n",
        "    prompt_tokens = []\n",
        "    for dialog in dialogs:\n",
        "        if dialog[0][\"role\"] != \"system\":\n",
        "            dialog = [\n",
        "                {\n",
        "                    \"role\": \"system\",\n",
        "                    \"content\": SYSTEM_PROMPT,\n",
        "                }\n",
        "            ] + dialog\n",
        "        dialog_tokens = [tokenizer(\n",
        "            # f\"{B_INST} {B_SYS}{(dialog[0]['content']).strip()}{E_SYS}{(dialog[1]['content']).strip()} {E_INST}\",\n",
        "            f\"{B_INST} {(dialog[1]['content']).strip()} {E_INST}\", # Omits the system prompt altogether\n",
        "            return_tensors=\"pt\",\n",
        "            add_special_tokens=True\n",
        "        ).input_ids.to(runtimeFlag)]\n",
        "        for i in range(2, len(dialog), 2):\n",
        "            user_tokens = tokenizer(\n",
        "                f\"{B_INST} {(dialog[i+1]['content']).strip()} {E_INST}\",\n",
        "                return_tensors=\"pt\",\n",
        "                add_special_tokens=True\n",
        "            ).input_ids.to(runtimeFlag)\n",
        "            assistant_w_eos = dialog[i]['content'].strip() + tokenizer.eos_token\n",
        "            assistant_tokens = tokenizer(\n",
        "                            assistant_w_eos,\n",
        "                            return_tensors=\"pt\",\n",
        "                            add_special_tokens=False\n",
        "                        ).input_ids.to(runtimeFlag)\n",
        "            tokens = torch.cat([assistant_tokens, user_tokens], dim=-1)\n",
        "            dialog_tokens.append(tokens)\n",
        "        prompt_tokens.append(torch.cat(dialog_tokens, dim=-1))\n",
        "\n",
        "    input_ids = prompt_tokens[0]\n",
        "    if len(input_ids[0]) > max_prompt_len:\n",
        "        return \"\\n\\n **The language model's input limit has been reached. Clear the chat and start afresh!**\"\n",
        "\n",
        "    # print(tokenizer.decode(input_ids[0], skip_special_tokens=True))\n",
        "\n",
        "    generation_output = model.generate(\n",
        "        input_ids=input_ids,\n",
        "        do_sample=True,\n",
        "        max_new_tokens=max_gen_len,\n",
        "        temperature=temperature,\n",
        "        top_p=top_p,\n",
        "    );\n",
        "\n",
        "    new_tokens = generation_output[0][input_ids.shape[-1]:]\n",
        "    new_assistant_response = tokenizer.decode(new_tokens, skip_special_tokens=True).strip();\n",
        "\n",
        "    return new_assistant_response"
      ],
      "metadata": {
        "id": "_7HZfpuDfLQU"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def print_wrapped(text):\n",
        "    # Regular expression pattern to detect code blocks\n",
        "    code_pattern = r'```(.+?)```'\n",
        "    matches = list(re.finditer(code_pattern, text, re.DOTALL))\n",
        "\n",
        "    if not matches:\n",
        "        # If there are no code blocks, display the entire text as Markdown\n",
        "        display(Markdown(text))\n",
        "        return\n",
        "\n",
        "    start = 0\n",
        "    for match in matches:\n",
        "        # Display the text before the code block as Markdown\n",
        "        before_code = text[start:match.start()].strip()\n",
        "        if before_code:\n",
        "            display(Markdown(before_code))\n",
        "\n",
        "        # Display the code block\n",
        "        code = match.group(0).strip()  # Extract code block\n",
        "        display(Markdown(code))  # Display code block\n",
        "\n",
        "        start = match.end()\n",
        "\n",
        "    # Display the text after the last code block as Markdown\n",
        "    after_code = text[start:].strip()  # Text after the last code block\n",
        "    if after_code:\n",
        "        display(Markdown(after_code))\n",
        "\n",
        "dialog_history = [{\"role\": \"system\", \"content\": SYSTEM_PROMPT}]\n",
        "\n",
        "button = widgets.Button(description=\"Send\")\n",
        "upload_button = widgets.Button(description=\"Upload .txt or .pdf\")\n",
        "text = widgets.Textarea(layout=widgets.Layout(width='800px'))\n",
        "\n",
        "output_log = widgets.Output()\n",
        "\n",
        "def on_button_clicked(b):\n",
        "    user_input = text.value\n",
        "    dialog_history.append({\"role\": \"user\", \"content\": user_input})\n",
        "\n",
        "    text.value = ''\n",
        "\n",
        "    # Change button description and color, and disable it\n",
        "    button.description = 'Processing...'\n",
        "    button.style.button_color = '#ff6e00'  # Use hex color codes for better color choices\n",
        "    button.disabled = True  # Disable the button when processing\n",
        "\n",
        "    with output_log:\n",
        "        clear_output()\n",
        "        for message in dialog_history:\n",
        "            print_wrapped(f'**{message[\"role\"].capitalize()}**: {message[\"content\"]}\\n')\n",
        "\n",
        "    assistant_response = generate_response([dialog_history]);\n",
        "\n",
        "    # Re-enable the button, reset description and color after processing\n",
        "    button.description = 'Send'\n",
        "    button.style.button_color = 'lightgray'\n",
        "    button.disabled = False\n",
        "\n",
        "    dialog_history.append({\"role\": \"assistant\", \"content\": assistant_response})\n",
        "\n",
        "    with output_log:\n",
        "        clear_output()\n",
        "        for message in dialog_history:\n",
        "            print_wrapped(f'**{message[\"role\"].capitalize()}**: {message[\"content\"]}\\n')\n",
        "\n",
        "button.on_click(on_button_clicked)\n",
        "\n",
        "# Create an output widget for alerts\n",
        "alert_out = widgets.Output()\n",
        "\n",
        "clear_button = widgets.Button(description=\"Clear Chat\")\n",
        "text = widgets.Textarea(layout=widgets.Layout(width='800px'))\n",
        "\n",
        "def on_clear_button_clicked(b):\n",
        "    # Clear the dialog history\n",
        "    dialog_history.clear()\n",
        "    # Add back the initial system prompt\n",
        "    dialog_history.append({\"role\": \"system\", \"content\": SYSTEM_PROMPT})\n",
        "    # Clear the output log\n",
        "    with output_log:\n",
        "        clear_output()\n",
        "\n",
        "clear_button.on_click(on_clear_button_clicked)"
      ],
      "metadata": {
        "id": "k_RJObixH_HR"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import display, HTML\n",
        "from ipywidgets import HBox, VBox\n",
        "\n",
        "# Create the title with HTML\n",
        "title = f\"<h1 style='color: #00FFFF;'>Pippy's AI-Assistant 🐍</h1>\"\n",
        "\n",
        "# Assuming that output_log, alert_out, and text are other widgets or display elements...\n",
        "first_row = HBox([button, clear_button])  # Arrange these buttons horizontally\n",
        "\n",
        "# Arrange the two rows of buttons and other display elements vertically\n",
        "layout = VBox([output_log, alert_out, text, first_row])"
      ],
      "metadata": {
        "id": "Uy0iMfgLKKk2"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Chat with AI-assistant"
      ],
      "metadata": {
        "id": "qiAEnqKuPGoR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "display(HTML(title))  # Use HTML function to display the title\n",
        "display(layout)"
      ],
      "metadata": {
        "id": "_r5fQloTPGx5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 951,
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        "outputId": "d4afa1c4-28af-451c-f135-8a717be338fd"
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      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<h1 style='color: #00FFFF;'>Pippy's AI-Assistant 🐍</h1>"
            ]
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          "output_type": "display_data",
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                  "text/markdown": "```\ndef is_prime(n):\n    if n < 2:\n        return False\n    for i in range(2, int(n ** 0.5) + 1):\n        if n % i == 0:\n            return False\n    return True\n\nfor i in range(2, 151):\n    if is_prime(i):\n        print(i)\n```"
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                  "text/markdown": "```\n# Creating a dictionary\nmy_dict = {'name': 'John', 'age': 30, 'city': 'New York'}\n\n# Accessing values in the dictionary\nprint(my_dict['name']) # Output: John\nprint(my_dict['age']) # Output: 30\nprint(my_dict['city']) # Output: New York\n\n# Modifying values in the dictionary\nmy_dict['name'] = 'Jane'\nprint(my_dict['name']) # Output: Jane\n\n# Adding new key-value pairs to the dictionary\nmy_dict['country'] = 'USA'\nprint(my_dict) # Output: {'name': 'Jane', 'age': 30, 'city': 'New York', 'country': 'USA'}\n\n# Deleting key-value pairs from the dictionary\ndel my_dict['age']\nprint(my_dict) # Output: {'name': 'Jane', 'city': 'New York', 'country': 'USA'}\n```"
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