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import transformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
import logging
import torch
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import gradio as gr
def get_heatmap(sequence):
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()
all_logits = []
for i in range(len(sequence)):
# add a masked token
masked_seq = sequence[:i] + '<mask>' + sequence[i+1:]
# tokenize masked sequence
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()}
# predict logits for the masked token
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, :]
top_1_tokens = torch.topk(mask_token_logits, 1, dim=1).indices[0].item()
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)
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 = (all_logits_array - all_logits_array.min()) / (all_logits_array.max() - all_logits_array.min())
transposed_logits_array = normalized_logits_array.T
# Plotting the heatmap
step = 50
y_tick_positions = np.arange(0, len(sequence), step)
y_tick_labels = [str(pos) for pos in y_tick_positions]
plt.figure(figsize=(15, 8))
sns.heatmap(transposed_logits_array, cmap='plasma', xticklabels=y_tick_labels, yticklabels=filtered_tokens)
plt.title('Logits for masked per residue tokens')
plt.ylabel('Token')
plt.xlabel('Residue Index')
plt.yticks(rotation=0)
plt.xticks(y_tick_positions, y_tick_labels, rotation = 0)
fig = plt.gcf()
plt.close(fig)
return fig
demo = gr.Interface(fn=get_heatmap, inputs="text", outputs="image")
demo.launch() |