Updated app.py and README.md

README.md

@@ -66,7 +66,7 @@ C1=CC=C(C=C1)NC(=O)C2=CC=CC=N2
 The model processes each drug–target pair in sequence to predict binding affinity. For visualization, only the final SMILES entry is shown.
 If “Remove duplicate SMILES” is enabled, any repeated SMILES strings are filtered out before analysis.
 Final results follow the original SMILES order, with adjusted index numbers when duplicates have been removed.
-You can only insert **up to 2000 SMILES** at a time in batch processing.
+You can only insert **up to 2000 SMILES** at a time in batch processing; exceeding lines will be truncated.
 
 ### Programmatic usage
 

app.py

@@ -351,9 +351,10 @@ def smiles_preprocessing(drug_smiles, remove_dupl):
                 kept += 1
 
         logger.info(f"{kept-len(drugs)} duplicate smiles removed!")
-        return sorted_drugs
-    return drugs    
 
+    drugs = sorted_drugs
+    return drugs[:2000]
+    
 def predict_wrapper(target_seq, drug_smiles, remove_dups):
     """Wrapper function for Gradio interface"""
     if not target_seq.strip() or not drug_smiles.strip():
@@ -379,16 +380,6 @@ def load_model_wrapper(model_path):
     else:
         return "Failed to load model. Check the path and files."
 
-# Add after the other state variables (around line 408)
-drug_input_prev_lines = gr.State(value=0)
-
-def truncate_drug_input(drug_smiles):
-    """Truncate drug input to maximum 2000 lines"""
-    lines = drug_smiles.split('\n')
-    if len(lines) > 2000:
-        return '\n'.join(lines[:2000])
-    return drug_smiles
-
 # Create Gradio interface
 with gr.Blocks(title="Drug-Target Interaction Predictor", theme=gr.themes.Soft()) as demo:
     gr.HTML("""
@@ -427,13 +418,6 @@ with gr.Blocks(title="Drug-Target Interaction Predictor", theme=gr.themes.Soft()
                     max_lines=5,
                 )
 
-                # Auto-truncate to 2000 lines when input changes
-                drug_input.blur(
-                    fn=truncate_drug_input,
-                    inputs=drug_input,
-                    outputs=drug_input
-                )
-
                 remove_dups_checkbox = gr.Checkbox(
                     label="Remove duplicate SMILES",
                     value=False