import gradio as gr

def greet(name):
    return "Hello " + name + "!!"

iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch()
'''
import gradio as gr
import torch
import torch.nn as nn
import torchtext
import spacy
import string

# 下载和加载情感分类模型
model = torch.hub.load('pytorch/vision', 'resnet18', pretrained=True)
model.eval()

# 创建一个文本处理pipeline
nlp = spacy.load("en_core_web_sm")
tokenizer = torchtext.data.utils.get_tokenizer("spacy", language="en_core_web_sm")
def preprocess_text(text):
    text = text.lower()
    text = ''.join([char for char in text if char not in string.punctuation])
    text = ' '.join([token.text for token in nlp(text)])
    text = tokenizer(text)
    return ' '.join(text)

# 定义文本情感分类函数
def classify_text(text):
    text = preprocess_text(text)
    # 在这里实际上应该用您自己的情感分类模型进行预测
    # 此示例仅用ResNet-18模型进行占位
    return {"emotion": "Placeholder"}

# 创建Gradio界面
iface = gr.Interface(
    fn=classify_text,
    inputs="text",
    outputs="label",
    interpretation="default",
    title="Text Emotion Classification",
    description="Enter a text and get its emotion classification."
)

# 启动界面
iface.launch()
'''