Gradio机器学习模型快速部署工具接口状态

import gradio as gr
scores = []
def track_score(score):
    scores.append(score)
    top_scores = sorted(scores, reverse=True)[:3]
    return top_scores
demo = gr.Interface(
    track_score, 
    gr.Number(label="Score"), 
    gr.JSON(label="Top Scores")
)
demo.launch()

import gradio as gr
from transfORMers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
def user(message, history):
    return "", history + [[message, None]]
#     bot_message = random.choice(["Yes", "No"])
#     history[-1][1] = bot_message
#     time.sleep(1)
#     return history
# def predict(input, history=[]):
#     # tokenize the new input sentence
def bot(history):
    user_message = history[-1][0]
    new_user_input_ids = tokenizer.encode(user_message + tokenizer.eos_token, return_tensors='pt')
    # append the new user input tokens to the chat history
    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
    # generate a response 
    history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
    # convert the tokens to text, and then split the responses into lines
    response = tokenizer.decode(history[0]).split("<|endoftext|>")
    response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
    return history
with gr.Blocks() as demo:
    chatbot = gr.Chatbot()
    msg = gr.Textbox()
    clear = gr.Button("Clear")
    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
        bot, chatbot, chatbot
    )
    clear.click(lambda: None, None, chatbot, queue=False)
demo.launch()