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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/VijayRameshkumar/Cab_booking-Chatbot/blob/master/GODEL.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "GODEL - (Grounded Open\n",
        "Dialogue Language Model https://www.microsoft.com/en-us/research/uploads/prod/2022/05/2206.11309.pdf"
      ],
      "metadata": {
        "id": "nibJ0hl2OQTQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 0: Install 🤗 `transformers` & `gradio`"
      ],
      "metadata": {
        "id": "Y23PiFb-lh7Q"
      }
    },
    {
      "cell_type": "code",
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "850c4168-5020-4981-b8c6-bb23d133fead"
      },
      "outputs": [
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          "name": "stdout",
          "text": [
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            "\u001b[K     |████████████████████████████████| 856 kB 68.7 MB/s \n",
            "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for python-multipart (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "! pip install transformers gradio -q"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 1 — Setting up the Chatbot Model - Microsoft GODEL"
      ],
      "metadata": {
        "id": "EZXsjh1Olmt3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
        "import torch\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/GODEL-v1_1-base-seq2seq\")\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(\"microsoft/GODEL-v1_1-base-seq2seq\")"
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    {
      "cell_type": "markdown",
      "source": [
        "# Step 2 — Defining a `predict` function with `state` and model prediction"
      ],
      "metadata": {
        "id": "bVESAC1tlqHN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def predict(input, history=[]):\n",
        "\n",
        "    instruction = 'Instruction: given a dialog context, you need to response empathically'\n",
        "\n",
        "    knowledge = '  '\n",
        "\n",
        "    s = list(sum(history, ()))\n",
        "\n",
        "    s.append(input)\n",
        "\n",
        "    #print(s)\n",
        "\n",
        "    dialog = ' EOS ' .join(s)\n",
        "\n",
        "    #print(dialog)\n",
        "\n",
        "    query = f\"{instruction} [CONTEXT] {dialog} {knowledge}\"\n",
        "\n",
        "    top_p = 0.9\n",
        "    min_length = 8\n",
        "    max_length = 64\n",
        "\n",
        "\n",
        "    # tokenize the new input sentence\n",
        "    new_user_input_ids = tokenizer.encode(f\"{query}\", return_tensors='pt')\n",
        "\n",
        "\n",
        "    output = model.generate(new_user_input_ids, min_length=int(\n",
        "        min_length), max_length=int(max_length), top_p=top_p, do_sample=True).tolist()\n",
        "    \n",
        "  \n",
        "    response = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "\n",
        " \n",
        "    history.append((input, response))\n",
        "\n",
        "    return history, history\n",
        "    "
      ],
      "metadata": {
        "id": "x1rCy-Qvlanr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 3 — Creating a Gradio Chatbot UI "
      ],
      "metadata": {
        "id": "i_3GMa_5lwHz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "\n",
        "\n",
        "gr.Interface(fn=predict,\n",
        "             inputs=[\"text\",'state'],\n",
        "             outputs=[\"chatbot\",'state']).launch(debug = True, share = True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "CNjOQNZDlfCC",
        "outputId": "69a10ce9-fec9-4511-f2ef-63f42ff9206a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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://a1217eeb7f33b9cd.gradio.app\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/html": [
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        {
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          "text": [
            "Keyboard interruption in main thread... closing server.\n"
          ]
        },
        {
          "output_type": "execute_result",
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              "(<gradio.routes.App at 0x7f7446385050>,\n",
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}