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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() | |
''' | |