app.py

import pandas as pd
import torch
import re
import gradio as gr
from io import StringIO
from transformers import EsmForSequenceClassification, EsmTokenizer

tokenizer = EsmTokenizer.from_pretrained("facebook/esm2_t6_8M_UR50D", do_lower_case=False)
model = EsmForSequenceClassification.from_pretrained("model/best_model5")
model.eval() 

def read_fasta(fasta_string):
    """Parses FASTA format input and returns sequences with validation."""
    sequences = []
    headers = []
    seq_buffer = []
    header = None

    for line in StringIO(fasta_string):
        line = line.strip()
        if line.startswith(">"):
            if seq_buffer:
                sequences.append("".join(seq_buffer))
                seq_buffer.clear()
            header = line
            headers.append(header)
        else:
            if not re.match(r'^[ACDEFGHIKLMNPQRSTVWY]+$', line):
                raise ValueError("Invalid FASTA format: Sequences must contain only natural amino acids (ACDEFGHIKLMNPQRSTVWY).")
            if len(line) > 30:
                raise ValueError(f"Sequence too long: '{line}' (Max 30 characters allowed).")
            seq_buffer.append(line)

    if seq_buffer:
        sequences.append("".join(seq_buffer))

    return headers, sequences

def predict_peptide_class(sequences):
    """Predicts peptide classifications in batch."""
    inputs = tokenizer(sequences, return_tensors='pt')
    with torch.no_grad():
        logits = model(**inputs).logits
    probs = torch.sigmoid(logits)[:, 1].cpu().numpy()
    classes = ["B3PP" if p > 0.5 else "Non-B3PP" for p in probs]
    return probs, classes

def predict_from_fasta(fasta_input):
    """Processes FASTA input and returns predictions in a table format."""
    try:
        headers, sequences = read_fasta(fasta_input)
        if not sequences:
            return pd.DataFrame({"Error": ["No valid sequences found."]})

        probs, classes = predict_peptide_class(sequences)
        return pd.DataFrame({"Header": headers, "Sequence": sequences, "Probability": probs, "Predicted Class": classes})

    except ValueError as e:
        return pd.DataFrame({"Error": [str(e)]})
    except Exception:
        return pd.DataFrame({"Error": ["An unexpected error occurred."]})

iface = gr.Interface(
    fn=predict_from_fasta,
    inputs=gr.Textbox(lines=10, placeholder="Paste your peptide sequences in FASTA format here"),
    outputs=gr.Dataframe(),
    title="B3PP Predictor",
    description="Submit peptide sequences in FASTA format to determine their potential as blood-brain barrier penetration peptides. Sequences must consist exclusively of natural amino acids in uppercase letters, with a maximum length of 30 characters."
)

iface.launch()