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import gradio as gr
import random
import time
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load models and tokenizers
model_names = ["lmsys/vicuna-7b", "gpt2"]
models = [AutoModelForCausalLM.from_pretrained(name) for name in model_names]
tokenizers = [AutoTokenizer.from_pretrained(name) for name in model_names]
with gr.Blocks() as demo:
with gr.Row():
vicuna_chatbot = gr.Chatbot(label="Vicuna", live=True)
gpt2_chatbot = gr.Chatbot(label="GPT-2", live=True)
msg = gr.Textbox()
clear = gr.ClearButton([msg, vicuna_chatbot, gpt2_chatbot])
def respond(message, chat_history, chatbot_idx):
input_ids = tokenizers[chatbot_idx].encode(message, return_tensors="pt")
output = models[chatbot_idx].generate(input_ids, max_length=50, num_beams=5, no_repeat_ngram_size=2)
bot_message = tokenizers[chatbot_idx].decode(output[0], skip_special_tokens=True)
chat_history.append((message, bot_message))
time.sleep(2)
return "", chat_history
msg.submit(respond, [msg, vicuna_chatbot, 0], [msg, gpt2_chatbot, 1])
demo.launch()
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