app.py

import gradio as gr
import torch
from transformers import BertTokenizer, BertForSequenceClassification

model_path = "LukeJacob2023/religion-classifier"

# 分类名称
labels = ["基督教", "佛教", "无信仰"]
# 1. 加载tokenizer和模型
tokenizer = BertTokenizer.from_pretrained(model_path)
model = BertForSequenceClassification.from_pretrained(model_path)

# 确保模型在评估模式
model.eval()


def predict(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)
    probabilities = torch.nn.functional.softmax(outputs.logits / 5.0, dim=-1)[0]
    return {label: float(prob) for label, prob in zip(labels, probabilities)}


# 创建Gradio接口
iface = gr.Interface(
    fn=predict,
    inputs=gr.Textbox(lines=2, label="Input Text"),
    outputs=gr.Label(num_top_classes=3, label="Predictions"),
    title="Religion Classification",
    description="请输入内容（繁体中文）"
)

# 启动Gradio应用
iface.launch()