Update app.py

app.py

@@ -69,8 +69,8 @@ def predict_single_49mer(sequence_49mer):
         outputs = model(tensor_x1, tensor_x2)
     
     probabilities = torch.sigmoid(outputs).squeeze().cpu().numpy()
-    
-    labels = ["Acetyllysine (Kac)", "Crotonyllysine (Kcr)", "Methyllysine (Kme)", "Succinyllysine (Ksucc)"]
+#Lysine-Acetylation（K-Ac）    
+    labels = ["Lysine-Acetyllysine (K-Ac)", "Lysine-Crotonyllysine (K-Cr)", "Lysine-Methyllysine (K-Me)", "Lysine-Succinyllysine (K-Succ)"]
     result = {label: float(prob) for label, prob in zip(labels, probabilities)}
     
     return result
@@ -89,12 +89,12 @@ def process_fasta_and_predict(fasta_input):
     并返回用于Gradio HighlightedText组件的数据和一个包含预测结果的状态字典。
     """
     if not fasta_input or not isinstance(fasta_input, str):
-        raise gr.Error("请输入有效的FASTA格式序列。")
+        raise gr.Error("Please enter a valid FASTA format sequence.")
 
     sequence = parse_fasta(fasta_input)
     
     if len(sequence) < 49:
-        raise gr.Error(f"序列过短！需要至少49个氨基酸，当前长度为 {len(sequence)}。")
+        raise gr.Error(f"The sequence is too short! It needs to be at least 49 amino acids. The current length is {len(sequence)}。")
 
     # 存储每个可预测K位点（索引）及其预测结果
     predictions_map = {}
@@ -116,7 +116,7 @@ def process_fasta_and_predict(fasta_input):
 
     if not predictions_map:
         # 如果没有一个K位点可以被成功预测
-        return [(sequence, None)], {}, "在序列中没有找到任何可以进行预测的有效K位点（即K前后没有足够的氨基酸）。"
+        return [(sequence, None)], {}, "No valid K sites were found in the sequence for prediction (i.e., there were not enough amino acids before and after K)."
 
     # --- 构建Gradio HighlightedText的输入格式 ---
     highlight_data = []
@@ -134,7 +134,7 @@ def process_fasta_and_predict(fasta_input):
     # 添加最后一个K之后剩余的部分
     highlight_data.append((sequence[last_pos:], None))
     
-    initial_info = "处理完成！请点击下方序列中高亮的红色 'K' 位点查看其预测结果。"
+    initial_info = "Processing complete! Click on the highlighted 'K' site in the sequence below to see its prediction."
 
     return highlight_data, predictions_map, initial_info
 
@@ -153,49 +153,49 @@ def show_results_for_site(evt: gr.SelectData, state_data):
         result_dict = state_data.get(k_index)
         
         if result_dict:
-            site_info = f"以位置 {k_index + 1} 的 'K' 为中心的片段预测结果："
+            site_info = f"Prediction results for the segment centered at 'K' at position {k_index + 1}:"
             return result_dict, site_info
             
     # 如果没有选择或出现错误
-    return None, "请点击上方序列中高亮的红色 'K' 位点查看结果。"
+    return None, "Please click on the highlighted 'K' site in the sequence above to view the results."
 
 
 # --- 7. 创建并启动 Gradio 界面 (使用 gr.Blocks) ---
-fasta_example = """>sp|P0C9F0|NUSA_BACAN Nickel-binding GTPase (Fragment)
-MNEKNKSQLRIIFVCGKSFIWSSTLFKHKRIHTGEKPYKCEECGKAFNHSQILLHIRHKR
-MHTGEKPYKCEECGKAFSRSSHLTTHIRHTGEKPYVCKECGKAFNRSSHLTTHIRHTGEK
-PYKCEECGKAFSRSSHLTTHIRHTGEKPYKCEECGKAFSRSSHLTTHIRHTGEKPYKCEE
-CGKAFSRSSHLTTHIRHTGEKPYKCEECGKAFNRSSHLTTHIRHTGEKPYKCEECGKAFN
-RSSTLTTHIRHTGEKPYKCEECGKAFSRSSH"""
+fasta_example = """>sp|P05141|ADT2_HUMAN ADP/ATP translocase 2 OS=Homo sapiens OX=9606 GN=SLC25A5 PE=1 SV=7
+MTDAAVSFAKDFLAGGVAAAISKTAVAPIERVKLLLQVQHASKQITADKQYKGIIDCVVR
+IPKEQGVLSFWRGNLANVIRYFPTQALNFAFKDKYKQIFLGGVDKRTQFWLYFAGNLASG
+GAAGATSLCFVYPLDFARTRLAADVGKAGAEREFRGLGDCLVKIYKSDGIKGLYQGFNVS
+VQGIIIYRAAYFGIYDTAKGMLPDPKNTHIVISWMIAQTVTAVAGLTSYPFDTVRRRMMM
+QSGRKGTDIMYTGTLDCWRKIARDEGGKAFFKGAWSNVLRGMGGAFVLVLYDEIKKYT"""
 
 with gr.Blocks(css=".predictable-k {color: red; font-weight: bold;}") as demo:
     gr.Markdown(
         """
-        # CAFN Model: Multi-label Classifier for Lysine Modifications
-        **支持FASTA格式输入，交互式查看蛋白序列中各个赖氨酸位点的修饰可能性。**
+        # DeepKMulti Model: Multi-label Classifier for Lysine Modifications
+        **Supports FASTA format input, allowing interactive viewing of the modification possibilities of each lysine site in the protein sequence.**
         """
     )
     with gr.Row():
         with gr.Column(scale=2):
             fasta_input = gr.Textbox(
                 lines=10, 
-                label="输入FASTA格式的蛋白质序列 (Input FASTA Sequence)",
-                placeholder="请在此处粘贴您的FASTA格式序列..."
+                label="Input FASTA format protein sequence",
+                placeholder="Please paste your FASTA formatted sequence here..."
             )
-            submit_btn = gr.Button("提交预测", variant="primary")
+            submit_btn = gr.Button("Submit Prediction", variant="primary")
 
         with gr.Column(scale=3):
-            gr.Markdown("### 预测结果 (Prediction Results)")
-            info_text = gr.Textbox(label="状态", interactive=False, value="等待输入...")
+            gr.Markdown("### Prediction Results")
+            info_text = gr.Textbox(label="State", interactive=False, value="Waiting for input...")
             # 用于存储所有位点的预测结果，对用户不可见
             predictions_state = gr.State({}) 
-            results_output = gr.Label(num_top_classes=4, label="点击红色'K'位点后，此处将显示结果")
+            results_output = gr.Label(num_top_classes=4, label="After clicking on the colored 'K' site, the results will be displayed here")
 
     gr.Markdown("---")
-    gr.Markdown("### 可视化序列 (Visualized Sequence)")
+    gr.Markdown("### Visualized Sequence")
     # 使用 a[class='predictable-k'] 来应用CSS
     highlighted_output = gr.HighlightedText(
-        label="序列分析",
+        label="Sequence Analysis",
         color_map={"predictable-k": "red"}, # 旧版Gradio的用法
         # 在新版Gradio中，CSS通过gr.Blocks的css参数全局定义更可靠
     )
@@ -203,7 +203,7 @@ with gr.Blocks(css=".predictable-k {color: red; font-weight: bold;}") as demo:
     gr.Examples(
         examples=[[fasta_example]],
         inputs=fasta_input,
-        label="示例序列"
+        label="Example sequence"
     )
 
     # --- 设定事件逻辑 ---