{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i_g3-tUqCEQN",
        "outputId": "6fdc0d31-a2ca-4896-ea47-8ff7cfde008e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fri Mar 10 06:08:27 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   67C    P0    29W /  70W |   2275MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "# Runtime > Change runtime type > Hardware accelerator: GPU\n",
        "# Install Driver\n",
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8EkL3CcxFtjh",
        "outputId": "bd44b1fb-33e4-45cb-9b77-917a6eca0743"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m87.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.2/199.2 KB\u001b[0m \u001b[31m22.6 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[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.1/49.1 KB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m73.5/73.5 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[32m62.8/62.8 KB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m65.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for llama-index (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for openai (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "# Install LLM tools\n",
        "! pip install -q langchain gpt-index llama-index transformers sentence_transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C8nj-t_YFuSH"
      },
      "outputs": [],
      "source": [
        "# Install visualization tools\n",
        "!pip install -q gradio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xqn1yfpEDy8D"
      },
      "outputs": [],
      "source": [
        "# Import Libraries\n",
        "import gradio as gr\n",
        "\n",
        "## Features\n",
        "from llama_index import Document\n",
        "\n",
        "## Model\n",
        "from langchain.llms.base import LLM\n",
        "from llama_index import LLMPredictor, GPTSimpleVectorIndex\n",
        "from transformers import pipeline\n",
        "import torch\n",
        "\n",
        "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
        "from llama_index import LangchainEmbedding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sL4ORAReD766"
      },
      "outputs": [],
      "source": [
        "# Feature Functions\n",
        "\n",
        "## Text to Document\n",
        "def text_to_document(input_text):\n",
        "    text_list = [input_text]\n",
        "    return [Document(t) for t in text_list]  # documents"
      ]
    },
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        "# Model Functions & Class\n",
        "class FlanLLM(LLM):\n",
        "    model_name = \"google/flan-t5-base\"\n",
        "    pipeline = pipeline(\"text2text-generation\", model=model_name,\n",
        "                        device=0, model_kwargs={\"torch_dtype\": torch.bfloat16})\n",
        "\n",
        "    def _call(self, prompt, stop=None):\n",
        "        return self.pipeline(prompt, max_length=9999)[0][\"generated_text\"]\n",
        "\n",
        "    def _identifying_params(self):\n",
        "        return {\"name_of_model\": self.model_name}\n",
        "\n",
        "    def _llm_type(self):\n",
        "        return \"custom\"\n",
        "\n",
        "llm_predictor = LLMPredictor(llm=FlanLLM())\n",
        "hfemb = HuggingFaceEmbeddings()\n",
        "embed_model = LangchainEmbedding(hfemb)\n",
        "\n",
        "def document_to_index(documents):\n",
        "    global index\n",
        "    index = GPTSimpleVectorIndex(\n",
        "        documents, embed_model=embed_model, llm_predictor=llm_predictor)\n",
        "    return('Index successfully saved.')\n",
        "\n",
        "def query_and_response(chat_history, user_query):\n",
        "    bot_response = index.query(user_query)\n",
        "    response = \"\"\n",
        "    for letter in ''.join(bot_response.response):\n",
        "        response += f'{letter}'\n",
        "        yield chat_history + [(user_query, response)]"
      ]
    },
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          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "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://1010e1d1a156b247ae.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"
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          "text": [
            "Keyboard interruption in main thread... closing server.\n",
            "Killing tunnel 127.0.0.1:7860 <> https://1010e1d1a156b247ae.gradio.live\n"
          ]
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      "source": [
        "# App\n",
        "def text_to_index(input_text):\n",
        "    documents = text_to_document(input_text)\n",
        "    document_to_index(documents)\n",
        "\n",
        "with gr.Blocks() as demo:\n",
        "    gr.Markdown('Q&A Chatbot')\n",
        "    with gr.Tab(\"Input Text\"):\n",
        "        text_input = gr.Textbox()\n",
        "        text_output = gr.Textbox()\n",
        "        text_button = gr.Button(\"Learn From Text\")\n",
        "        text_button.click(text_to_index, text_input, text_output)\n",
        "    with gr.Tab(\"Chat\"):\n",
        "        chatbot = gr.Chatbot()\n",
        "        message = gr.Textbox(\"Please summarize this document\")\n",
        "        message.submit(query_and_response, [chatbot, message], chatbot)\n"
      ]
    }
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