test

app.py

@@ -3,10 +3,12 @@ from transformers import AutoTokenizer, AutoModelForMaskedLM
 import torch
 from torch.distributions.categorical import Categorical
 
+'''
 # Load the model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained("TianlaiChen/PepMLM-650M")
 model = AutoModelForMaskedLM.from_pretrained("TianlaiChen/PepMLM-650M")
 
+
 def generate_peptide(protein_seq, peptide_length, top_k):
     print('Within')
     peptide_length = int(peptide_length)
@@ -29,6 +31,11 @@ def generate_peptide(protein_seq, peptide_length, top_k):
 
     generated_peptide = tokenizer.decode(predicted_token_ids, skip_special_tokens=True)
     return f"Generated Sequence: {generated_peptide.replace(' ', '')}"
+'''
+
+def hello():
+    print('hello')
+    return "hello"
 
 # Define the Gradio interface
 interface = gr.Interface(