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Add ability add a single message from the bot/user side (#3165)
* chatbot fices * chagnelog * remove starts_with * more fixes * added chatbot multimodal demo * fix height * height * update demo * changelog * format * format * Update gradio/components.py Co-authored-by: Abubakar Abid <abubakar@huggingface.co> * fix * format --------- Co-authored-by: Abubakar Abid <abubakar@huggingface.co>
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{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chatbot_demo"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"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 predict(input, history=[]):\n", " # tokenize the new input sentence\n", " new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')\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(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()\n", "\n", " # convert the tokens to text, and then split the responses into lines\n", " response = tokenizer.decode(history[0]).split(\"<|endoftext|>\")\n", " response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)] # convert to tuples of list\n", " return response, history\n", "\n", "with gr.Blocks() as demo:\n", " chatbot = gr.Chatbot()\n", " state = gr.State([])\n", "\n", " with gr.Row():\n", " txt = gr.Textbox(show_label=False, placeholder=\"Enter text and press enter\").style(container=False)\n", " \n", " txt.submit(predict, [txt, state], [chatbot, state])\n", " \n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} | ||
{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chatbot_demo"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"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 predict(input, history=[]):\n", " # tokenize the new input sentence\n", " new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')\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(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()\n", "\n", " # convert the tokens to text, and then split the responses into lines\n", " response = tokenizer.decode(history[0]).split(\"<|endoftext|>\")\n", " response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)] # convert to tuples of list\n", " return response, history\n", "\n", "with gr.Blocks() as demo:\n", " chatbot = gr.Chatbot()\n", " state = gr.State([])\n", "\n", " with gr.Row():\n", " txt = gr.Textbox(show_label=False, placeholder=\"Enter text and press enter\").style(container=False)\n", "\n", " txt.submit(predict, [txt, state], [chatbot, state])\n", " \n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} |
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