Upload app.py

app.py

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+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", flagged=False),
+    title="Religion Classification",
+    description="请输入内容（繁体中文）"
+)
+
+# 启动Gradio应用
+iface.launch()