chatbot.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HjSEg8Bh29OT"
      },
      "source": [
        "# Chatbot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "outputId": "2f4a3c04-e6c3-49de-ee69-14cfde3f73d9"
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      "source": [
        "%pip install -q accelerate\n",
        "%pip install -q bitsandbytes\n",
        "%pip install -q gradio\n",
        "%pip install -q huggingface_hub\n",
        "%pip install -q jinja2\n",
        "%pip install -q langchain\n",
        "%pip install -q python-dotenv\n",
        "%pip install -q scipy\n",
        "%pip install -q torch\n",
        "%pip install -q transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-10F4w9FlzPy"
      },
      "outputs": [],
      "source": [
        "# Provide Hugging Face access token\n",
        "import dotenv\n",
        "import os\n",
        "\n",
        "dotenv.load_dotenv('/.env')\n",
        "HF_ACCESS_TOKEN = os.getenv('HF_ACCESS_TOKEN')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ApVTl4VM02yH"
      },
      "source": [
        "## Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DJqC_-zuqTuY"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tnQ_4XK-tCi-"
      },
      "outputs": [],
      "source": [
        "model_id = 'meta-llama/Llama-2-7b-chat-hf'"
      ]
    },
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      "source": [
        "# Configure for 4-bit quantization (optimizes model deployment)\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    bnb_4bit_compute_dtype = 'float16',\n",
        "    bnb_4bit_quant_type='nf4',\n",
        "    load_in_4bit=True,\n",
        ")\n",
        "\n",
        "# Load model configuration\n",
        "model_config = AutoConfig.from_pretrained(\n",
        "    model_id,\n",
        "    use_auth_token=HF_ACCESS_TOKEN\n",
        ")"
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      "source": [
        "# Load model\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    config=model_config,\n",
        "    device_map='auto',\n",
        "    quantization_config=bnb_config,\n",
        "    use_auth_token=HF_ACCESS_TOKEN\n",
        ")\n",
        "\n",
        "# Load tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(\n",
        "    model_id,\n",
        "    use_auth_token=HF_ACCESS_TOKEN\n",
        ")\n",
        "\n",
        "# Set model into evaluation mode (optimizes inference)\n",
        "model.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DZ8M537Xo6h9"
      },
      "outputs": [],
      "source": [
        "# Set up the text-generation pipeline\n",
        "pipe = pipeline(\n",
        "    model=model,\n",
        "    task='text-generation',\n",
        "    tokenizer=tokenizer\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MG4H5X-s1iQP"
      },
      "source": [
        "## App"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "klrKCTHA-LZQ"
      },
      "outputs": [],
      "source": [
        "from langchain.llms import HuggingFacePipeline\n",
        "\n",
        "llm = HuggingFacePipeline(pipeline=pipe)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_ZZIxvQc3kMD"
      },
      "outputs": [],
      "source": [
        "from langchain.prompts.prompt import PromptTemplate\n",
        "\n",
        "# Template using jinja2 syntax\n",
        "template = \"\"\"\n",
        "<s>[INST] <<SYS>>\n",
        "The following is a friendly conversation between a human and an AI. \n",
        "The AI is talkative and provides lots of specific details from its context. \n",
        "If the AI does not know the answer to a question, it truthfully says it does not know.\n",
        "Please be concise.\n",
        "<</SYS>>\n",
        "\n",
        "Current conversation:\n",
        "{{ history }}\n",
        "\n",
        "{% if history %}\n",
        "    <s>[INST] Human: {{ input }} [/INST] AI: </s>\n",
        "{% else %}\n",
        "    Human: {{ input }} [/INST] AI: </s>\n",
        "{% endif %} \n",
        "\"\"\"\n",
        "\n",
        "prompt = PromptTemplate(\n",
        "    input_variables=[\"history\", \"input\"],\n",
        "    template=template,\n",
        "    template_format=\"jinja2\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hi0JMk7z62u_"
      },
      "outputs": [],
      "source": [
        "from langchain.chains import ConversationChain\n",
        "from langchain.memory import ConversationBufferMemory\n",
        "\n",
        "# Initialize the conversation chain\n",
        "conversation = ConversationChain(\n",
        "    llm=llm,\n",
        "    memory=ConversationBufferMemory(),\n",
        "    prompt=prompt,\n",
        "    verbose=False\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mAZk2rhC4H83"
      },
      "outputs": [],
      "source": [
        "# Start the conversation\n",
        "def predict(message: str, history: str):\n",
        "    response = conversation.predict(input=message)\n",
        "\n",
        "    return response"
      ]
    },
    {
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      "outputs": [],
      "source": [
        "import gradio as gr\n",
        "\n",
        "# Set up the user interface\n",
        "interface = gr.ChatInterface(\n",
        "    clear_btn=None,\n",
        "    fn=predict,\n",
        "    retry_btn=None,\n",
        "    undo_btn=None,\n",
        ")\n",
        "\n",
        "# Launch the user interface\n",
        "interface.launch(\n",
        "    height=600,\n",
        "    inline=True,\n",
        "    share=True,\n",
        "    width=800\n",
        ")"
      ]
    }
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