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Add ability add a single message from the bot/user side #3165
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7cff328
chatbot fices
dawoodkhan82 0a3ccdd
chagnelog
dawoodkhan82 e421906
merge main
dawoodkhan82 6f6db29
Merge branch 'main' into chatbot-fixes
abidlabs a801baa
merge main
dawoodkhan82 eb129c1
remove starts_with
dawoodkhan82 12be984
more fixes
dawoodkhan82 05fd55c
added chatbot multimodal demo
abidlabs 3c391cf
fix height
dawoodkhan82 e56ed3c
height
dawoodkhan82 bcce732
update demo
dawoodkhan82 67783b2
Merge branch 'main' into chatbot-fixes
dawoodkhan82 165d0c3
changelog
dawoodkhan82 b540411
format
dawoodkhan82 9256cd7
Merge branch 'main' into chatbot-fixes
dawoodkhan82 ef5b9f6
format
dawoodkhan82 24d4181
Merge branch 'main' into chatbot-fixes
abidlabs 7775f24
Update gradio/components.py
dawoodkhan82 c9141e6
fix
dawoodkhan82 f2e113d
Merge branch 'main' into chatbot-fixes
dawoodkhan82 57ac78e
format
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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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This should fix the height issue @yvrjsharma was facing with his demo