app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForMaskedLM
import torch
from torch.distributions.categorical import Categorical
import numpy as np
import pandas as pd

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("ChatterjeeLab/PepMLM-650M")
model = AutoModelForMaskedLM.from_pretrained("ChatterjeeLab/PepMLM-650M")

def compute_pseudo_perplexity(model, tokenizer, protein_seq, binder_seq):
    sequence = protein_seq + binder_seq
    tensor_input = tokenizer.encode(sequence, return_tensors='pt').to(model.device)
    total_loss = 0

    # Loop through each token in the binder sequence
    for i in range(-len(binder_seq)-1, -1):
        # Create a copy of the original tensor
        masked_input = tensor_input.clone()

        # Mask one token at a time
        masked_input[0, i] = tokenizer.mask_token_id
        # Create labels
        labels = torch.full(tensor_input.shape, -100).to(model.device)
        labels[0, i] = tensor_input[0, i]

        # Get model prediction and loss
        with torch.no_grad():
            outputs = model(masked_input, labels=labels)
            total_loss += outputs.loss.item()

    # Calculate the average loss
    avg_loss = total_loss / len(binder_seq)

    # Calculate pseudo perplexity
    pseudo_perplexity = np.exp(avg_loss)
    return pseudo_perplexity
    

def generate_peptide(protein_seq, peptide_length, top_k, num_binders):

    peptide_length = int(peptide_length)
    top_k = int(top_k)
    num_binders = int(num_binders)

    binders_with_ppl = []

    for _ in range(num_binders):
        # Generate binder
        masked_peptide = '<mask>' * peptide_length
        input_sequence = protein_seq + masked_peptide
        inputs = tokenizer(input_sequence, return_tensors="pt").to(model.device)

        with torch.no_grad():
            logits = model(**inputs).logits
        mask_token_indices = (inputs["input_ids"] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
        logits_at_masks = logits[0, mask_token_indices]
    
        # Apply top-k sampling
        top_k_logits, top_k_indices = logits_at_masks.topk(top_k, dim=-1)
        probabilities = torch.nn.functional.softmax(top_k_logits, dim=-1)
        predicted_indices = Categorical(probabilities).sample()
        predicted_token_ids = top_k_indices.gather(-1, predicted_indices.unsqueeze(-1)).squeeze(-1)

        generated_binder = tokenizer.decode(predicted_token_ids, skip_special_tokens=True).replace(' ', '')

        # Compute PPL for the generated binder
        ppl_value = compute_pseudo_perplexity(model, tokenizer, protein_seq, generated_binder)

        # Add the generated binder and its PPL to the results list
        binders_with_ppl.append([generated_binder, ppl_value])
        
        # Convert the list of lists to a pandas dataframe
        df = pd.DataFrame(binders_with_ppl, columns=["Binder", "Perplexity"])
    
        # Save the dataframe to a CSV file
        output_filename = "output.csv"
        df.to_csv(output_filename, index=False)


    return binders_with_ppl, output_filename


# Define the Gradio interface
interface = gr.Interface(
    fn=generate_peptide,
    inputs=[
        gr.Textbox(label="Protein Sequence", info="Enter protein sequence here", type="text"),
        gr.Slider(3, 50, value=15, label="Peptide Length", step=1, info='Default value is 15'),
        gr.Slider(1, 10, value=3, label="Top K Value", step=1, info='Default value is 3'),
        gr.Dropdown(choices=[1, 2, 4, 8, 16, 32], label="Number of Binders", value=1)
    ],
    outputs=[
    gr.Dataframe(
        headers=["Binder", "Perplexity"],
        datatype=["str", "number"],
        col_count=(2, "fixed")
    ),
    gr.outputs.File(label="Download CSV")
    ],
    title="PepMLM: Target Sequence-Conditioned Generation of Peptide Binders via Masked Language Modeling"
)

interface.launch()