{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "blender-agent.ipynb",
      "private_outputs": true,
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rJ-QA0ODU4x9"
      },
      "source": [
        "# Install Dependancies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bx20ocfPWAYS"
      },
      "source": [
        "#install pytorch\n",
        "!pip3 install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio===0.9.1 -f https://download.pytorch.org/whl/torch_stable.html"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UcY5FPNHY72K"
      },
      "source": [
        "#install transformers\n",
        "!pip install transformers"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DKilCO-TU_s0"
      },
      "source": [
        "# Import Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VU33RjeOc8eG"
      },
      "source": [
        "#import model class and tokenizer\n",
        "from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6LZEzJXpc8pg"
      },
      "source": [
        "#download and setup the model and tokenizer\n",
        "model_name = 'facebook/blenderbot-400M-distill'\n",
        "tokenizer = BlenderbotTokenizer.from_pretrained(model_name)\n",
        "model = BlenderbotForConditionalGeneration.from_pretrained(model_name)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_T9nRG5eVENY"
      },
      "source": [
        "# make conversation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AkrWBF3af8Ns"
      },
      "source": [
        "#making an utterance \n",
        "utterance = \"My name is gold, I like football and coding\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JDDDJ2V3f8QT"
      },
      "source": [
        "#tokenize the utterance\n",
        "inputs = tokenizer(utterance, return_tensors=\"pt\")\n",
        "inputs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-QYclYycf8TN"
      },
      "source": [
        "#generate model results\n",
        "result = model.generate(**inputs)\n",
        "result"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "syB3F_iyf8Vt"
      },
      "source": [
        "tokenizer.decode(result[0])"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}