app.py

import gradio as gr
import pandas as pd
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
import torch.nn.functional as F
import logging
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from io import BytesIO
from PIL import Image
from contextlib import contextmanager
import warnings
import sys
import os
import zipfile

logging.getLogger("transformers.modeling_utils").setLevel(logging.ERROR)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

# Load the tokenizer and model
model_name = "ChatterjeeLab/FusOn-pLM"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained(model_name, trust_remote_code=True)
model.to(device)
model.eval()

@contextmanager
def suppress_output():
    with open(os.devnull, 'w') as devnull:
        old_stdout = sys.stdout
        sys.stdout = devnull
        try:
            yield
        finally:
            sys.stdout = old_stdout

def process_sequence(sequence, domain_bounds, n):
    AAs_tokens = ['L', 'A', 'G', 'V', 'S', 'E', 'R', 'T', 'I', 'D', 'P', 'K', 'Q', 'N', 'F', 'Y', 'M', 'H', 'W', 'C']
    AAs_tokens_indices = {'L' : 4, 'A' : 5, 'G' : 6, 'V': 7, 'S' : 8, 'E' : 9, 'R' : 10, 'T' : 11, 'I': 12, 'D' : 13, 'P' : 14,
                          'K' : 15, 'Q' : 16, 'N' : 17, 'F' : 18, 'Y' : 19, 'M' : 20, 'H' : 21, 'W' : 22, 'C' : 23}
    # checking sequence inputs
    if not sequence.strip():
      raise gr.Error("Error: The sequence input is empty. Please enter a valid protein sequence.")
      return None, None, None
    if any(char not in AAs_tokens for char in sequence):
      raise gr.Error("Error: The sequence input contains non-amino acid characters. Please enter a valid protein sequence.")
      return None, None, None

    # checking domain bounds inputs
    try:
      start = int(domain_bounds['start'][0])
      end = int(domain_bounds['end'][0])
    except ValueError:
      raise gr.Error("Error: Start and end indices must be integers.")
      return None, None, None
    if start >= end:
      raise gr.Error("Start index must be smaller than end index.")
      return None, None, None
    if start == 0 and end != 0:
      raise gr.Error("Indexing starts at 1. Please enter valid domain bounds.")
      return None, None, None
    if start <= 0 or end <= 0:
      raise gr.Error("Domain bounds must be positive integers. Please enter valid domain bounds.")
      return None, None, None
    if start > len(sequence) or end > len(sequence):
      raise gr.Error("Domain bounds exceed sequence length.")
      return None, None, None

    # checking top n tokens input
    if n == None:
      raise gr.Error("Choose Top N Tokens from the dropdown menu.")
      return None, None, None

    start_index = int(domain_bounds['start'][0]) - 1
    end_index = int(domain_bounds['end'][0])

    top_n_mutations = {}
    all_logits = []

    # these 2 lists are for the 2nd heatmap
    originals_logits = []
    conservation_likelihoods = {}

    for i in range(len(sequence)):
      # only iterate through the residues inside the domain
          if start_index <= i <= (end_index - 1):
              original_residue = sequence[i]
              original_residue_index = AAs_tokens_indices[original_residue]
              masked_seq = sequence[:i] + '<mask>' + sequence[i+1:]
              inputs = tokenizer(masked_seq, return_tensors="pt", padding=True, truncation=True, max_length=2000)
              inputs = {k: v.to(device) for k, v in inputs.items()}
              with torch.no_grad():
                  logits = model(**inputs).logits
              mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1]
              mask_token_logits = logits[0, mask_token_index, :]

              # Pick top N tokens
              all_tokens_logits = mask_token_logits.squeeze(0)
              top_tokens_indices = torch.argsort(all_tokens_logits, dim=0, descending=True)
              top_tokens_logits = all_tokens_logits[top_tokens_indices]
              mutation = []
              # make sure we don't include non-AA tokens
              for token_index in top_tokens_indices:
                  decoded_token = tokenizer.decode([token_index.item()])
                  # decoded all tokens, pick the top n amino acid ones
                  if decoded_token in AAs_tokens:
                      mutation.append(decoded_token)
                      if len(mutation) == n:
                          break
              top_n_mutations[(sequence[i], i)] = mutation

              # collecting logits for the heatmap
              logits_array = mask_token_logits.cpu().numpy()
              # filter out non-amino acid tokens
              filtered_indices = list(range(4, 23 + 1))
              filtered_logits = logits_array[:, filtered_indices]
              all_logits.append(filtered_logits)

              # code for the second heatmap
              normalized_mask_token_logits = F.softmax(torch.tensor(mask_token_logits).cpu(), dim=-1).numpy()
              normalized_mask_token_logits = np.squeeze(normalized_mask_token_logits)
              originals_logit = normalized_mask_token_logits[original_residue_index]
              originals_logits.append(originals_logit)

              if originals_logit > 0.7:
                  conservation_likelihoods[(original_residue, i)] = 1
              else:
                  conservation_likelihoods[(original_residue, i)] = 0



   # Plotting heatmap 2
    domain_len = end - start
    if 500 > domain_len > 100:
      step_size = 50
    elif 500 <= domain_len:
      step_size = 100
    elif domain_len < 10:
      step_size = 1
    else:
      step_size = 10
    x_tick_positions = np.arange(start_index, end_index, step_size)
    x_tick_labels = [str(pos + 1) for pos in x_tick_positions]

    all_logits_array = np.vstack(originals_logits)
    transposed_logits_array = all_logits_array.T
    conservation_likelihoods_array = np.array(list(conservation_likelihoods.values())).reshape(1, -1)
    # combine to make a 2D heatmap
    combined_array = np.vstack((transposed_logits_array, conservation_likelihoods_array))

    plt.figure(figsize=(15, 5))
    plt.rcParams.update({'font.size': 16.5})
    sns.heatmap(combined_array, cmap='viridis', xticklabels=x_tick_labels, yticklabels=['Residue \nLogits', 'Residue \nConservation'], cbar=True)
    plt.xticks(x_tick_positions - start_index + 0.5, x_tick_labels, rotation=0)
    plt.title('Original Residue Probability and Conservation')
    plt.xlabel('Residue Index')
    plt.show()
    buf = BytesIO()
    plt.savefig(buf, format='png', dpi=300)
    buf.seek(0)
    plt.close()
    img_2 = Image.open(buf)


# plotting heatmap 1
    token_indices = torch.arange(logits.size(-1))
    tokens = [tokenizer.decode([idx]) for idx in token_indices]
    filtered_tokens = [tokens[i] for i in filtered_indices]
    all_logits_array = np.vstack(all_logits)
    normalized_logits_array = F.softmax(torch.tensor(all_logits_array), dim=-1).numpy()
    transposed_logits_array = normalized_logits_array.T


    plt.figure(figsize=(15, 8))
    plt.rcParams.update({'font.size': 16.5})
    sns.heatmap(transposed_logits_array, cmap='plasma', xticklabels=x_tick_labels, yticklabels=filtered_tokens)
    plt.title('Token Probability')
    plt.ylabel('Amino Acid')
    plt.xlabel('Residue Index')
    plt.yticks(rotation=0)
    plt.xticks(x_tick_positions - start_index + 0.5, x_tick_labels, rotation=0)

    buf = BytesIO()
    plt.savefig(buf, format='png', dpi = 300)
    buf.seek(0)
    plt.close()

    img_1 = Image.open(buf)

# store the predicted mutations in a dataframe
    original_residues = []
    mutations = []
    positions = []

    for key, value in top_n_mutations.items():
        original_residue, position = key
        original_residues.append(original_residue)
        mutations.append(value)
        positions.append(position + 1)

    df = pd.DataFrame({
        'Original Residue': original_residues,
        'Predicted Residues': mutations,
        'Position': positions
    })
    df.to_csv("predicted_tokens.csv", index=False)
    img_1.save("heatmap.png", dpi=(300, 300))
    img_2.save("heatmap_2.png", dpi=(300, 300))
    zip_path = "outputs.zip"
    with zipfile.ZipFile(zip_path, 'w') as zipf:
        zipf.write("predicted_tokens.csv")
        zipf.write("heatmap.png")
        zipf.write("heatmap_2.png")

    return df, img_1, img_2, zip_path

# launch the demo
demo = gr.Interface(
    fn=process_sequence,
    inputs=[
        gr.Textbox(label="Sequence", placeholder="Enter the protein sequence here"),
        gr.Dataframe(
            value = [[1, 1]],
            headers=["start", "end"],
            datatype=["number", "number"],
            row_count=(1, "fixed"),
            col_count=(2, "fixed"),
            label="Domain Bounds"
        ),
        gr.Dropdown([i for i in range(1, 21)], label="Top N Tokens"),
    ],
     outputs=[
        gr.Dataframe(label="Predicted Tokens (in order of decreasing likelihood)"),
        gr.Image(type="pil", label="Probability Distribution for All Tokens"),
        gr.Image(type="pil", label="Residue Conservation"),
        gr.File(label="Download Outputs"),
    ],
)
if __name__ == "__main__":
    with suppress_output():
      demo.launch()