AI_Chatbot.ipynb

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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "base_uri": "https://localhost:8080/"
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        "id": "cBtpRpiXEv4C",
        "outputId": "f09fb736-02e7-4f09-b83e-c9c6442c936e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "!pip install gradio\n",
        "import gradio as gr\n",
        "import torch"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "title = \"My AI ChatBot\"\n",
        "description = \"The First AI chatbot I've ever created :)\"\n",
        "examples = [[\"How are you?\"]]"
      ],
      "metadata": {
        "id": "okkTgqIZFHL0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/DialoGPT-large\")\n",
        "model = AutoModelForCausalLM.from_pretrained(\"microsoft/DialoGPT-large\")"
      ],
      "metadata": {
        "id": "BXQeZJTNFXnN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def predict(input, history=[]):\n",
        "    # tokenize the new input sentence\n",
        "    new_user_input_ids = tokenizer.encode(\n",
        "        input + tokenizer.eos_token, return_tensors=\"pt\"\n",
        "    )\n",
        "\n",
        "    # append the new user input tokens to the chat history\n",
        "    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)\n",
        "\n",
        "    # generate a response\n",
        "    history = model.generate(\n",
        "        bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id\n",
        "    ).tolist()\n",
        "\n",
        "    # convert the tokens to text, and then split the responses into lines\n",
        "    response = tokenizer.decode(history[0]).split(\"<|endoftext|>\")\n",
        "    # print('decoded_response-->>'+str(response))\n",
        "    response = [\n",
        "        (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)\n",
        "    ]  # convert to tuples of list\n",
        "    # print('response-->>'+str(response))\n",
        "    return response, history"
      ],
      "metadata": {
        "id": "hDpujwK2FX6D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gr.Interface(\n",
        "    fn=predict,\n",
        "    title=title,\n",
        "    description=description,\n",
        "    examples=examples,\n",
        "    inputs=[\"text\", \"state\"],\n",
        "    outputs=[\"chatbot\", \"state\"],\n",
        "    theme=\"finlaymacklon/boxy_violet\",\n",
        ").launch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "kg1FtqFBFYB0",
        "outputId": "8903e449-77e8-4412-936a-55e5c56b6272"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gradio/blocks.py:982: UserWarning: Cannot load finlaymacklon/boxy_violet. Caught Exception: The space finlaymacklon/boxy_violet does not exist\n",
            "  warnings.warn(f\"Cannot load {theme}. Caught Exception: {str(e)}\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "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. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://3380ca288100ff98a5.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://3380ca288100ff98a5.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
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          "execution_count": 10
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    }
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}