Delete heatmap.py

heatmap.py

@@ -1,73 +0,0 @@
-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
-
-
-
-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()
-
-
-# fix this to take dynamic input
-sequence = 'MCNTNMS'
-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)
-plt.show()
-plt.savefig(f'heatmap_{i}.png', dpi=300, bbox_inches='tight')
-