Update app.py

app.py

@@ -10,6 +10,28 @@ from Bio import SeqIO
 import torch
 from sklearn.preprocessing import OneHotEncoder
 import plotly.graph_objects as go
+import requests
+import time
+
+last_ping_time = 0
+def smart_wake_up():
+    global last_ping_time
+    current_time = time.time()
+    # 每 40 分钟（2400秒）触发一次，Space 默认通常是 48 小时或更短时间休眠
+    if current_time - last_ping_time > 2400:
+        wake_up_space_b()
+        last_ping_time = current_time
+
+
+def wake_up_space_b():
+    try:
+        # 替换为 Space B 的直接访问链接
+        url = "https://huggingface.co/spaces/julse/maotao"
+        # 使用 head 请求或 get 请求，超时时间设短一点以免影响 A 的速度
+        requests.get(url, timeout=5)
+        print("Successfully pinged Space B")
+    except Exception as e:
+        print(f"Failed to wake up Space B: {e}")
 
 
 class RPContactPredictor:
@@ -76,6 +98,7 @@ class RPContactPredictor:
         return sequences
 
     def predict_contact(self, rna_seq, protein_seq):
+        smart_wake_up()
         """Predict RNA-protein contact matrix"""
         # Encode sequences
         rna_oh = self.one_hot_encode(rna_seq, alpha='ACGU')
@@ -125,7 +148,7 @@ def create_heatmap(contact_matrix, rna_labels, protein_labels, rna_name, protein
         },
         xaxis_title=f"Residues ({protein_name}, {len(protein_labels)} AA)",
         yaxis_title=f"Nucleotides ({rna_name}, {len(rna_labels)} nt)",
-        width=max(ratio*300,600),
+        width=max(ratio * 300, 600),
         height=300,
         font=dict(size=12)
     )
@@ -168,7 +191,7 @@ def create_download_files(contact_matrix, rna_labels, protein_labels, rna_name,
 def process_prediction(fasta_file, rna_sequence, protein_sequence, input_method):
     """Process prediction request and return initial results"""
     if not fasta_file and not (rna_sequence and protein_sequence):
-        return "❌ Please upload a FASTA file or enter RNA and protein sequences",None, None, None, None, None, None
+        return "❌ Please upload a FASTA file or enter RNA and protein sequences", None, None, None, None, None, None
 
     try:
         # Process input
@@ -187,9 +210,9 @@ def process_prediction(fasta_file, rna_sequence, protein_sequence, input_method)
 
         # Validate sequences
         if len(set(rna_seq) - set('ACGU')) > 0:
-            return f"❌ RNA sequence contains invalid characters: {set(rna_seq) - set('ACGU')}",None, None, None, None, None, None
+            return f"❌ RNA sequence contains invalid characters: {set(rna_seq) - set('ACGU')}", None, None, None, None, None, None
         if len(set(protein_seq) - set('GAVLIFWYDNEKQMSTCPHR')) > 0:
-            return f"❌ Protein sequence contains invalid characters: {set(protein_seq) - set('GAVLIFWYDNEKQMSTCPHR')}",None, None, None, None, None, None
+            return f"❌ Protein sequence contains invalid characters: {set(protein_seq) - set('GAVLIFWYDNEKQMSTCPHR')}", None, None, None, None, None, None
 
         # Run contact prediction
         contact_matrix = predictor.predict_contact(rna_seq, protein_seq)
@@ -245,6 +268,7 @@ def process_prediction(fasta_file, rna_sequence, protein_sequence, input_method)
     except Exception as e:
         return f"❌ Prediction failed: {str(e)}", None, None, None, None, None, None
 
+
 def update_results_with_threshold(Threshold, result_state):
     """Update heatmap and contact table based on Threshold threshold"""
     if result_state is None:
@@ -270,7 +294,6 @@ def update_results_with_threshold(Threshold, result_state):
     # Create contact pairs info
     contact_info = f"📊 Found {len(contact_pairs)} contacts (Probability ≥ {Threshold:.3f})"
 
-
     return heatmap, contact_pairs, contact_info
 
 
@@ -300,6 +323,8 @@ def load_example_data(fasta_input, rna_input, protein_input):
         return gr.update(value="Upload FASTA File")
     else:
         return gr.update(value="Enter Sequences Directly")
+
+
 def create_interface():
     """Create Gradio interface with threshold control"""
     custom_css = """
@@ -328,17 +353,17 @@ def create_interface():
         border: 1px solid #ddd;
         border-radius: 4px;
     }
-    
+
     .scrollable-plot > div {
         min-width: max-content;  /* 保持原始宽度 */
     }
     """
 
     with gr.Blocks(title="RNA-Protein Contact Prediction Tool",
-                   theme=gr.themes.Soft(primary_hue="blue", secondary_hue="teal"),css=None) as app:
+                   theme=gr.themes.Soft(primary_hue="blue", secondary_hue="teal"), css=None) as app:
         gr.Markdown("""
    <center>
-   
+
 # 🧬 RPcontact: RNA-Protein Contact Prediction  
 **Direct Nucleotide–Residue Contact Prediction from Primary Sequences** [Paper](https://www.biorxiv.org/content/10.1101/2025.06.02.657171v1.full)| [Code](https://github.com/JulseJiang/RPcontact) | [Demo](https://huggingface.co/spaces/julse/RPcontact)
 </center>
@@ -347,7 +372,7 @@ def create_interface():
 >This tool leveraging **ERNIE-RNA** for RNA and **ESM-2** for protein modeling, the method provides high-resolution insights into RNA-protein interactions at the atomic level.
 <br>Current Demo (auROC 0.779 on VL-49) is optimized for limited CPU environments using efficient one-hot encoding<br>
     Advanced Model (auROC 0.845 on VL-49), the embedding-based approach will be released upon paper publication ([contact us](mailto:jiangjh2023@shanghaitech.edu.cn) for early access)
-        
+
         """)
         with gr.Tab("🔬 Contact Prediction"):
             with gr.Row():
@@ -364,7 +389,7 @@ def create_interface():
                             label="FASTA File",
                             file_types=['.fasta', '.fa', '.txt'],
                             type='binary',
-                            visible = False
+                            visible=False
                         )
 
                         rna_input = gr.Textbox(
@@ -390,19 +415,15 @@ def create_interface():
                         outputs=[input_method],
                         label="📋 Example Data (click to load)",
                         run_on_click=True,
-                        fn = load_example_data
+                        fn=load_example_data
                     )
 
-
-
                     # Submit button at the bottom of input column
                     predict_btn = gr.Button("🚀 Run Prediction", variant="primary", size="lg")
 
                     # Status output
                     status_output = gr.Textbox(label="Prediction Status", lines=5)
 
-
-
                 with gr.Column(scale=2):
                     # Results section - initially hidden
                     gr.Markdown("""
@@ -524,7 +545,7 @@ def create_interface():
             ## ⚡ Performance Guidelines
 
             - **Processing Time**: Scales quadratically with sequence length
-            
+
             ### Quality Considerations
             - Higher probabilities indicate more confident predictions
             - Consider biological context when interpreting results
@@ -578,13 +599,13 @@ def create_interface():
             - Cross-validated on diverse RNA-protein complex datasets
             - Performance metrics available in the original publication
             - Benchmarked against existing prediction methods
-            
+
             ### 📊 Difference between current demo and final model
             | Model Type          | Checkpoint File           | auROC (VL-49) | LLM embeddings |
             |---------------------|---------------------------|---------------|-------------------|
             | OH + RP_Emb (final)         | `model_roc_0_38=0.845.pt` | 0.845         | ✓                |
             | OH (demo)                  | `model_roc_0_56=0.779.pt` | 0.779         | ✗                |
-            
+
             ## 📚 Citation & Contact
 
             If you use this tool in your research, please cite:
@@ -593,7 +614,7 @@ def create_interface():
 
             ### Contact Information
             For technical issues, feature requests, or collaboration inquiries, please contact the development team.
-            
+
             - **Primary Contact**: Jiuhong Jiang
             - **Email**: jiangjh2023@shanghaitech.edu.cn
             - **Institution**: ShanghaiTech University, Shanghai, China