Update app_new.py

app_new.py

@@ -0,0 +1,117 @@
+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)):
+        if start_index <= i <= end_index:
+            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()