app.py

import os

import gradio as gr
import torch
from transformers.models.bert import BertTokenizer, BertForSequenceClassification
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("sundea/text1")
model = AutoModelForSequenceClassification.from_pretrained("sundea/text1")
model.eval()



def get_output(text):
    output=[]
    model_input = tokenizer(text, return_tensors="pt", padding=True)
    model_output = model(**model_input, return_dict=False)
    prediction = torch.argmax(model_output[0].cpu(), dim=-1)
    prediction = [p.item() for p in prediction]
    for i in range(len(prediction)):
        if prediction[i]==0:
            output.append("消极")
        else:
            output.append('积极')



    return output

demo=gr.Interface(fn=get_output,inputs='text',outputs='text')
demo.launch()