Update app.py

app.py

@@ -2,22 +2,30 @@ import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-# 加载模型
+# 加载模型和 tokenizer
 model_name = "LilithHu/mbert-manipulative-detector"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-# 二分类标签（非操纵性是0，操纵性是1）
+# 设置为评估模式
+model.eval()
+
+# 设置运行设备
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# 标签名
 labels = ["Non-manipulative / 非操纵性", "Manipulative / 操纵性"]
 
+# 推理函数
 def classify(text):
-    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
     with torch.no_grad():
         outputs = model(**inputs)
         probs = torch.softmax(outputs.logits, dim=1)
         pred = torch.argmax(probs, dim=1).item()
         confidence = probs[0][pred].item()
-        return f"🧠 预测 / Prediction: {labels[pred]}\n"
+        return f"🧠 预测 / Prediction: {labels[pred]}\n🔢 置信度 / Confidence: {confidence*100:.2f}%"
 
 # Gradio 界面
 interface = gr.Interface(