Update app.py

app.py

@@ -62,14 +62,14 @@ def predict(sequence_input):
         raise gr.Error("模型未能加载或初始化失败，请检查后台日志。")
     
     if not sequence_input or not isinstance(sequence_input, str):
-        raise gr.Error("请输入有效的生物序列。")
+        raise gr.Error("Please enter a valid biological sequence.")
     
     cleaned_sequence = sequence_input.strip().upper()
     
     # --- 新增：在这里进行长度检查 ---
     EXPECTED_LENGTH = 49  # 定义期望的序列长度
     if len(cleaned_sequence) != EXPECTED_LENGTH:
-        raise gr.Error(f"输入序列长度错误！模型要求序列长度必须为 {EXPECTED_LENGTH} 个字符，但您输入的长度为 {len(cleaned_sequence)}。")
+        raise gr.Error(f"Wrong input sequence length! The model expects a sequence length of  {EXPECTED_LENGTH} characters, but you entered a length of  {len(cleaned_sequence)}.")
     
     sequence_list = [cleaned_sequence]
 
@@ -84,7 +84,7 @@ def predict(sequence_input):
     
     probabilities = torch.sigmoid(outputs).squeeze().cpu().numpy()
     
-    labels = ["类别 A (a)", "类别 C (c)", "类别 M (m)", "类别 S (s)"]
+    labels = ["Acetyllysine (Kac)", "Crotonyllysine (Kcr)", "Methyllysine (Kme)", "Succinyllysine (Ksucc)"]
     result = {label: float(prob) for label, prob in zip(labels, probabilities)}
     
     return result
@@ -96,14 +96,14 @@ demo = gr.Interface(
     fn=predict,
     inputs=gr.Textbox(
         lines=7, 
-        label="输入生物序列 (Input Sequence)", 
+        label=" (Input Sequence)", 
         # 在占位符中提示长度要求
-        placeholder="请在这里粘贴长度为 49 的序列..."
+        placeholder="Please paste your 49-length sequence here, making sure your lysine is in the middle..."
     ),
-    outputs=gr.Label(num_top_classes=4, label="预测概率 (Prediction Probabilities)"),
-    title="CAFN 模型部署：多标签序列分类器",
+    outputs=gr.Label(num_top_classes=4, label=" (Prediction Probabilities)"),
+    title="CAFN Model Deployment: Multi-label Classifier for Lysine Modifications",
     # 在描述中明确强调长度要求
-    description="输入一个生物序列，模型将预测它属于四个类别 (A, C, M, S) 中每一个的概率。\n\n**重要提示：本模型要求输入的序列长度必须为 49 个字符。**",
+    description="Given a biological sequence as input, the model predicts the probability of it belonging to each of the four classes (Kac, Kcr, Kme, Ksucc).\n\n**Important note: This model requires the input sequence to be 49 characters long.**",
     # 提供一个或多个长度正确的示例
     examples=[
         [valid_example_sequence],