requirements.txt

transformers
torch
gradio

app.py

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+import gradio as gr
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+
+# 載入 tokenizer 和模型
+tokenizer = BertTokenizer.from_pretrained("ckiplab/bert-base-chinese")
+model = BertForSequenceClassification.from_pretrained("ckiplab/bert-base-chinese", num_labels=2)
+model.eval()
+
+# 預測函式
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        prediction = torch.argmax(logits, dim=1).item()
+        return "詐騙訊息" if prediction == 1 else "正常訊息"
+
+# Gradio UI
+iface = gr.Interface(fn=predict, inputs="text", outputs="text", title="Line詐騙訊息辨識器")
+iface.launch()