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')