app.py

import gradio as gr
from transformers import BertTokenizer, BertForSequenceClassification
import numpy as np
import re
import torch

tokenizer = BertTokenizer.from_pretrained(r'./model')
model = BertForSequenceClassification.from_pretrained(r'./model', num_labels = 6)

labels = ['伤感', '快乐', '励志', '宣泄', '平静', '感人']

def preprocess(temp):
    temp = re.sub(u"\n\n", "\n", temp)
    temp = re.sub(u"(^\n)|(\n$)", "", temp)
    temp = re.sub(u",", "，", temp)
    temp = re.sub(u"\?", "？", temp)
    temp = re.sub(u"!", "！", temp)
    temp = re.sub(u"\.", "。", temp)
    temp = re.sub('[^\u4e00-\u9fa5，。？！\n]+', '', temp)
    temp = re.sub(u"\n", "，", temp)
    for _ in range(int(len(temp) / 2)):
        temp = re.sub(u"，，|！！|？？|。。", "，", temp)
        temp = re.sub(u"，！|！，", "！", temp)
        temp = re.sub(u"，？|？，", "？", temp)
        temp = re.sub(u"，。|。，", "。", temp)
    temp = temp.strip('，')

    return temp

def classify_text(inp):
    # print(inp)
    inp = preprocess(inp)
    # print(inp)
    inp = tokenizer(inp, padding=True, max_length=512, truncation=True, return_tensors="pt")
    # print(inp)
    with torch.no_grad():
        logits = model(**inp).logits
    # print(logits)
    # print(logits.shape)
    # logits = logits.argsort().squeeze(0)
    logits = torch.nn.Softmax(dim=1)(logits).squeeze(0)
    # print(logits)
    return {labels[i]: float(logits[i].item()) for i in range(len(labels))}
    # return {logits.argmax().item() : labels[logits.argmax().item()]}

input = '明天又是好日子\n千金的光阴不能等\n明天又是好日子\n赶上了盛世咱享太平\n今天是个好日子\n心想的事儿都能成\n明天又是好日子\n千金的光阴不能等\n今天明天都是好日子\n赶上了盛世咱享太平'

gr.Interface(
    classify_text,
    # gr.inputs.Image(),
    gr.inputs.Textbox(lines=5,default=""),
    outputs = 'label'
    # inputs='image',
    # outputs='label',
    # examples=[["images/cheetah1.jpg"], ["images/lion.jpg"]],
).launch(debug=True)