run.ipynb

{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatbot_dialogpt"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch transformers"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from transformers import AutoModelForCausalLM, AutoTokenizer\n", "import torch\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/DialoGPT-medium\")\n", "model = AutoModelForCausalLM.from_pretrained(\"microsoft/DialoGPT-medium\")\n", "\n", "def user(message, history):\n", "    return \"\", history + [[message, None]]\n", "\n", "def bot(history):\n", "    user_message = history[-1][0]\n", "    new_user_input_ids = tokenizer.encode(\n", "        user_message + 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([]), new_user_input_ids], dim=-1)\n", "\n", "    # generate a response\n", "    response = model.generate(\n", "        bot_input_ids, max_length=1000, 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(response[0]).split(\"<|endoftext|>\")\n", "    response = [\n", "        (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)\n", "    ]  # convert to tuples of list\n", "    history[-1] = response[0]\n", "    return history\n", "\n", "with gr.Blocks() as demo:\n", "    chatbot = gr.Chatbot()\n", "    msg = gr.Textbox()\n", "    clear = gr.Button(\"Clear\")\n", "\n", "    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(\n", "        bot, chatbot, chatbot\n", "    )\n", "    clear.click(lambda: None, None, chatbot, queue=False)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}