Delete app_latest.py

app_latest.py

@@ -1,134 +0,0 @@
-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
-
-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()
-
-def process_sequence(sequence, domain_bounds, n):
-    start_index = int(domain_bounds['start'][0]) - 1  
-    end_index = int(domain_bounds['end'][0])      
-
-    top_n_mutations = {}
-    all_logits = []
-
-    for i in range(len(sequence)):
-          if start_index <= i <= (end_index - 1):
-              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, :]
-
-              # Define amino acid tokens
-              AAs_tokens = ['L', 'A', 'G', 'V', 'S', 'E', 'R', 'T', 'I', 'D', 'P', 'K', 'Q', 'N', 'F', 'Y', 'M', 'H', 'W', 'C']
-              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()])
-                  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)
-
-    
-    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
-
-   # Plotting the heatmap
-    x_tick_positions = np.arange(start_index, end_index, 10)
-    x_tick_labels = [str(pos + 1) for pos in x_tick_positions]
-    
-    plt.figure(figsize=(15, 8))
-    plt.rcParams.update({'font.size': 18})
-
-    sns.heatmap(transposed_logits_array, cmap='plasma', xticklabels=x_tick_labels, yticklabels=filtered_tokens)
-    plt.title('Token Probability Heatmap')
-    plt.ylabel('Token')
-    plt.xlabel('Residue Index')
-    plt.yticks(rotation=0)
-    plt.xticks(x_tick_positions - start_index + 0.5, x_tick_labels, rotation=0) 
-    
-    # Save the figure to a BytesIO object
-    buf = BytesIO()
-    plt.savefig(buf, format='png', dpi = 300)
-    buf.seek(0)
-    plt.close()
-    
-    # Convert BytesIO object to an image
-    img = Image.open(buf)
-
-    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
-    })
-            
-    return df, img
-
-demo = gr.Interface(
-    fn=process_sequence,
-    inputs=[
-        gr.Textbox(label="Sequence", placeholder="Enter the protein sequence here"),
-        gr.Dataframe(
-            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="Heatmap"),
-    ],
-    description="Choose a number from the dropdown to predict N tokens for each position. Choose the start and end index of the domain of interest (indexing starts at 1).",
-)
-
-if __name__ == "__main__":
-    demo.launch()