Delete app_all_seq.py

app_all_seq.py

@@ -1,116 +0,0 @@
-import gradio as gr
-import pandas as pd
-import torch
-from transformers import AutoTokenizer, AutoModelForMaskedLM
-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)):
-        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, :]
-        # Decode top n tokens
-        top_n_tokens = torch.topk(mask_token_logits, n, dim=1).indices[0].tolist()
-        mutation = [tokenizer.decode([token]) for token in top_n_tokens]
-        top_n_mutations[(sequence[i], i)] = mutation 
-
-        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)
-    
-    # Save the figure to a BytesIO object
-    buf = BytesIO()
-    plt.savefig(buf, format='png')
-    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 (in order of decreasing likelihood)': mutations,
-        'Position': positions
-    })
-
-    df = df[start_index:end_index]
-        
-    return df, img
-
-demo = gr.Interface(
-    fn=process_sequence,
-    inputs=[
-        "text",
-        gr.Dataframe(
-            headers=["start", "end"],
-            datatype=["number", "number"],
-            row_count=(1, "fixed"),
-            col_count=(2, "fixed"),
-        ),
-        gr.Dropdown([i for i in range(1, 21)]),  # Dropdown with numbers from 1 to 20 as integers
-    ],
-    outputs=["dataframe", "image"],
-    description="Choose a number between 1-20 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()