app.py

import gradio as gr
import torch
import numpy as np
from transformers import AutoModel, AutoTokenizer, AutoConfig, RobertaModel
from modeling_dlmberta import InteractionModelATTNForRegression, StdScaler
from configuration_dlmberta import InteractionModelATTNConfig
from chemberta import ChembertaTokenizer
import json
import os
from pathlib import Path
import logging

# Import visualization functions
from analysis import plot_crossattention_weights, plot_presum
from PIL import Image, ImageDraw, ImageFont

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def create_placeholder_image(width=600, height=400, text="No visualization available", bg_color=(0, 0, 0, 0)):
    """
    Create a transparent placeholder image with text
    
    Args:
        width (int): Image width
        height (int): Image height  
        text (str): Text to display
        bg_color (tuple): Background color (R, G, B, A) - (0,0,0,0) for transparent
        
    Returns:
        PIL.Image: Transparent placeholder image
    """
    # Create image with transparent background
    img = Image.new('RGBA', (width, height), bg_color)
    draw = ImageDraw.Draw(img)
    
    # Try to use a default font, fallback to default if not available
    try:
        font = ImageFont.truetype("arial.ttf", 16)
    except:
        try:
            font = ImageFont.load_default()
        except:
            font = None
    
    # Get text size and position for centering
    if font:
        bbox = draw.textbbox((0, 0), text, font=font)
        text_width = bbox[2] - bbox[0]
        text_height = bbox[3] - bbox[1]
    else:
        # Rough estimation if no font available
        text_width = len(text) * 8
        text_height = 16
    
    x = (width - text_width) // 2
    y = (height - text_height) // 2
    
    # Draw text in gray
    draw.text((x, y), text, fill=(128, 128, 128, 255), font=font)
    
    return img

class DrugTargetInteractionApp:
    def __init__(self):
        self.model = None
        self.target_tokenizer = None
        self.drug_tokenizer = None
        self.scaler = None
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        
    def load_model(self, model_path="./"):
        """Load the pre-trained model and tokenizers"""
        try:
            # Load configuration
            config = InteractionModelATTNConfig.from_pretrained(model_path)
            
            # Load drug encoder (ChemBERTa)
            drug_encoder_config = AutoConfig.from_pretrained("DeepChem/ChemBERTa-77M-MTR")
            drug_encoder_config.pooler = None
            drug_encoder = RobertaModel(config=drug_encoder_config, add_pooling_layer=False)
            
            # Load target encoder
            target_encoder = AutoModel.from_pretrained("IlPakoZ/RNA-BERTa9700")
            
            # Load scaler if exists
            scaler_path = os.path.join(model_path, "scaler.config")
            scaler = None
            if os.path.exists(scaler_path):
                scaler = StdScaler()
                scaler.load(model_path)
            
            self.model = InteractionModelATTNForRegression.from_pretrained(
                        model_path,
                        config=config,
                        target_encoder=target_encoder,
                        drug_encoder=drug_encoder, 
                        scaler=scaler
                    )

            self.model.to(self.device)
            self.model.eval()
            
            # Load tokenizers
            self.target_tokenizer = AutoTokenizer.from_pretrained(
                os.path.join(model_path, "target_tokenizer")
            )
            
            # Load drug tokenizer (ChemBERTa)
            vocab_file = os.path.join(model_path, "drug_tokenizer", "vocab.json")
            self.drug_tokenizer = ChembertaTokenizer(vocab_file)
            
            logger.info("Model and tokenizers loaded successfully!")
            return True
            
        except Exception as e:
            logger.error(f"Error loading model: {str(e)}")
            return False
    
    def predict_interaction(self, target_sequence, drug_smiles, max_length=512):
        """Predict drug-target interaction"""
        if self.model is None:
            return "Error: Model not loaded. Please load a model first."
        
        try:
            # Tokenize inputs
            target_inputs = self.target_tokenizer(
                target_sequence,
                padding="max_length", 
                truncation=True, 
                max_length=512,
                return_tensors="pt"
            ).to(self.device)
            
            drug_inputs = self.drug_tokenizer(
                drug_smiles,
                padding="max_length", 
                truncation=True, 
                max_length=512,
                return_tensors="pt"
            ).to(self.device)

            # Make prediction
            self.model.INTERPR_DISABLE_MODE()
            with torch.no_grad():
                prediction = self.model(target_inputs, drug_inputs)
                
                # Unscale if scaler exists
                if self.model.scaler is not None:
                    prediction = self.model.unscale(prediction)
                
                prediction_value = prediction.cpu().numpy()[0][0]
            
            return f"Predicted Binding Affinity: {prediction_value:.4f}"
            
        except Exception as e:
            logger.error(f"Prediction error: {str(e)}")
            return f"Error during prediction: {str(e)}"

    def visualize_interaction(self, target_sequence, drug_smiles):
        """
        Generate visualization images for drug-target interaction
        
        Args:
            target_sequence (str): RNA sequence
            drug_smiles (str): Drug SMILES notation
            
        Returns:
            tuple: (cross_attention_image, raw_contribution_image, normalized_contribution_image, status_message)
        """
        if self.model is None:
            return None, None, None, "Error: Model not loaded. Please load a model first."
        
        try:
            # Tokenize inputs
            target_inputs = self.target_tokenizer(
                target_sequence,
                padding="max_length", 
                truncation=True, 
                max_length=512,
                return_tensors="pt"
            ).to(self.device)
            
            drug_inputs = self.drug_tokenizer(
                drug_smiles,
                padding="max_length", 
                truncation=True, 
                max_length=512,
                return_tensors="pt"
            ).to(self.device)
            
            # Enable interpretation mode
            self.model.INTERPR_ENABLE_MODE()

            # Make prediction and extract visualization data
            with torch.no_grad():
                prediction = self.model(target_inputs, drug_inputs)
                
                # Unscale if scaler exists
                if self.model.scaler is not None:
                    prediction = self.model.unscale(prediction)
                
                prediction_value = prediction.cpu().numpy()[0][0]
                
                # Extract data needed for visualizations
                presum_values = self.model.model.presum_layer  # Shape: (1, seq_len)
                cross_attention_weights = self.model.model.crossattention_weights  # Shape: (batch, heads, seq_len, seq_len)
                
                # Get model parameters for scaling
                w = self.model.model.w.squeeze(1)
                b = self.model.model.b
                scaler = self.model.model.scaler
                
            logger.info(f"Target inputs shape: {target_inputs['input_ids'].shape}")
            logger.info(f"Drug inputs shape: {drug_inputs['input_ids'].shape}")
            
            # Generate visualizations
            try:
                # 1. Cross-attention heatmap
                cross_attention_img = None
                logger.info(f"Cross-attention weights type: {type(cross_attention_weights)}")
                if cross_attention_weights is not None:
                    logger.info(f"Cross-attention weights shape: {cross_attention_weights.shape if hasattr(cross_attention_weights, 'shape') else 'No shape attr'}")
                    
                    try:
                        cross_attn_matrix = cross_attention_weights[0, 0]
                            
                        if cross_attn_matrix is not None:
                            logger.info(f"Extracted cross-attention matrix shape: {cross_attn_matrix.shape}")
                            logger.info(f"Target attention mask shape: {target_inputs['attention_mask'].shape}")
                            logger.info(f"Drug attention mask shape: {drug_inputs['attention_mask'].shape}")

                            cross_attention_img = plot_crossattention_weights(
                                target_inputs["attention_mask"][0],
                                drug_inputs["attention_mask"][0],
                                target_inputs,
                                drug_inputs,
                                cross_attn_matrix,
                                self.target_tokenizer,
                                self.drug_tokenizer
                            )
                        else:
                            logger.warning("Could not extract valid cross-attention matrix")
                            
                    except (IndexError, TypeError, AttributeError) as e:
                        logger.warning(f"Error extracting cross-attention matrix: {str(e)}")
                        cross_attn_matrix = None
                else:
                    logger.warning("Cross-attention weights are None")
                    
            except Exception as e:
                logger.error(f"Cross-attention visualization error: {str(e)}")
                cross_attention_img = None
            
            try:
                # 2. Normalized contribution visualization (only if pKd > 0)
                normalized_img = None
                if presum_values is not None:
                    normalized_img = plot_presum(
                        target_inputs,
                        presum_values.detach(),  # Detach the tensor
                        scaler,
                        w.detach(),  # Detach the tensor
                        b.detach(),  # Detach the tensor
                        self.target_tokenizer,
                        raw_affinities=False
                    )
                else:
                    if prediction_value <= 0:
                        logger.info("Skipping normalized affinities visualization as pKd <= 0")
                    if presum_values is None:
                        logger.warning("Cannot generate raw visualization: presum values are None")

                    
            except Exception as e:
                logger.error(f"Normalized contribution visualization error: {str(e)}")
                normalized_img = None
            
            try:
                # 3. Raw contribution visualization (always generate)
                raw_img = None
                if prediction_value > 0 and presum_values is not None:
                    raw_img = plot_presum(
                        target_inputs,
                        presum_values.detach(),  # Detach the tensor
                        scaler,
                        w.detach(),  # Detach the tensor
                        b.detach(),  # Detach the tensor
                        self.target_tokenizer,
                        raw_affinities=True
                    )
                else:
                    logger.warning("Presum values are None")
                        
            except Exception as e:
                logger.error(f"Raw contribution visualization error: {str(e)}")
                raw_img = None
            
            # Disable interpretation mode after use
            self.model.INTERPR_DISABLE_MODE()
            
            # Create placeholder images if generation failed
            if cross_attention_img is None:
                cross_attention_img = create_placeholder_image(
                    text="Cross-Attention Heatmap\nFailed to generate"
                )
            if normalized_img is None:
                normalized_img = create_placeholder_image(
                    text="Normalized Contribution\nFailed to generate"
                )
            if raw_img is None and prediction_value > 0:
                raw_img = create_placeholder_image(
                    text="Raw Contribution\nFailed to generate"
                )
            elif raw_img is None:
                raw_img = create_placeholder_image(
                    text="Raw Contribution\nSkipped (pKd ≤ 0)"
                )
                
            status_msg = f"Predicted Binding Affinity: {prediction_value:.4f}"
            if prediction_value <= 0:
                status_msg += " (Raw contribution visualization skipped due to non-positive pKd)"
            if cross_attention_weights is None:
                status_msg += " (Cross-attention visualization failed: weights not available)"
                
            return cross_attention_img, raw_img, normalized_img, status_msg
            
        except Exception as e:
            logger.error(f"Visualization error: {str(e)}")
            # Make sure to disable interpretation mode even if there's an error
            try:
                self.model.INTERPR_DISABLE_MODE()
            except:
                pass
            return None, None, None, f"Error during visualization: {str(e)}"


# Initialize the app
app = DrugTargetInteractionApp()

def predict_wrapper(target_seq, drug_smiles):
    """Wrapper function for Gradio interface"""
    if not target_seq.strip() or not drug_smiles.strip():
        return "Please provide both target sequence and drug SMILES."
    
    return app.predict_interaction(target_seq, drug_smiles)

def visualize_wrapper(target_seq, drug_smiles):
    """Wrapper function for visualization"""
    if not target_seq.strip() or not drug_smiles.strip():
        return None, None, None, "Please provide both target sequence and drug SMILES."

    return app.visualize_interaction(target_seq, drug_smiles)

def load_model_wrapper(model_path):
    """Wrapper function to load model"""
    if app.load_model(model_path):
        return "Model loaded successfully!"
    else:
        return "Failed to load model. Check the path and files."

# Create Gradio interface
with gr.Blocks(title="Drug-Target Interaction Predictor", theme=gr.themes.Soft()) as demo:
    gr.HTML("""
    <div style="text-align: center; margin-bottom: 30px;">
        <h1 style="color: #2E86AB; font-size: 2.5em; margin-bottom: 10px;">
            🧬 Drug-Target Interaction Predictor
        </h1>
        <p style="font-size: 1.2em; color: #666;">
            Predict binding affinity between drugs and target RNA sequences using deep learning
        </p>
    </div>
    """)
    
    # Create state variables to share images between tabs
    viz_state1 = gr.State()
    viz_state2 = gr.State()
    viz_state3 = gr.State()
    
    with gr.Tab("🔮 Prediction & Analysis"):
        with gr.Row():
            with gr.Column(scale=1):
                target_input = gr.Textbox(
                    label="Target RNA Sequence",
                    placeholder="Enter RNA sequence (e.g., AUGCUAGCUAGUACGUA...)",
                    lines=4,
                    max_lines=6
                )
                
                drug_input = gr.Textbox(
                    label="Drug SMILES",
                    placeholder="Enter SMILES notation (e.g., CC(C)CC1=CC=C(C=C1)C(C)C(=O)O)",
                    lines=2
                )
                
                with gr.Row():
                    predict_btn = gr.Button("🚀 Predict Interaction", variant="primary", size="lg")
                    visualize_btn = gr.Button("📊 Generate Visualizations", variant="secondary", size="lg")
            
            with gr.Column(scale=1):
                prediction_output = gr.Textbox(
                    label="Prediction Result",
                    interactive=False,
                    lines=4
                )
        
        # Example inputs
        gr.HTML("<h3 style='margin-top: 20px; color: #2E86AB;'>📚 Example Inputs:</h3>")
        
        examples = gr.Examples(
            examples=[
                [
                    "AUGCUAGCUAGUACGUAUAUCUGCACUGC",
                    "CC(C)CC1=CC=C(C=C1)C(C)C(=O)O"
                ],
                [
                    "AUGCGAUCGACGUACGUUAGCCGUAGCGUAGCUAGUGUAGCUAGUAGCU",
                    "C1=CC=C(C=C1)NC(=O)C2=CC=CC=N2"
                ]
            ],
            inputs=[target_input, drug_input],
            outputs=prediction_output,
            fn=predict_wrapper,
            cache_examples=False
        )
        
        # Button click events
        predict_btn.click(
            fn=predict_wrapper,
            inputs=[target_input, drug_input],
            outputs=prediction_output
        )
        
        def visualize_and_update(target_seq, drug_smiles):
            """Generate visualizations and update both status and state"""
            img1, img2, img3, status = visualize_wrapper(target_seq, drug_smiles)
            # Combine prediction result with visualization status
            combined_status = status + "\n\nVisualization analysis complete. Please navigate to the Visualizations tab to view the generated images."
            return img1, img2, img3, combined_status
        
        visualize_btn.click(
            fn=visualize_and_update,
            inputs=[target_input, drug_input],
            outputs=[viz_state1, viz_state2, viz_state3, prediction_output]
        )
    
    with gr.Tab("📊 Visualizations"):
        gr.HTML("""
        <div style="text-align: center; margin-bottom: 20px;">
            <h2 style="color: #2E86AB;">🔬 Interaction Analysis & Visualizations</h2>
            <p style="font-size: 1.1em; color: #666;">
                Generated visualizations will appear here after clicking "Generate Visualizations" in the Prediction tab
            </p>
        </div>
        """)
        
        # Visualization outputs - Large and vertically aligned
        viz_image1 = gr.Image(
            label="Cross-Attention Heatmap",
            type="pil",
            interactive=False,
            container=True,
            height=500,
            value=create_placeholder_image(text="Cross-Attention Heatmap\n(Generate visualizations in the Prediction tab)")
        )
        
        viz_image2 = gr.Image(
            label="Raw pKd Contribution Visualization",
            type="pil", 
            interactive=False,
            container=True,
            height=500,
            value=create_placeholder_image(text="Raw pKd Contribution\n(Generate visualizations in the Prediction tab)")
        )
        
        viz_image3 = gr.Image(
            label="Normalized pKd Contribution Visualization",
            type="pil",
            interactive=False,
            container=True,
            height=500,
            value=create_placeholder_image(text="Normalized pKd Contribution\n(Generate visualizations in the Prediction tab)")
        )
        
        # Update visualization images when state changes
        viz_state1.change(
            fn=lambda x: x,
            inputs=viz_state1,
            outputs=viz_image1
        )
        
        viz_state2.change(
            fn=lambda x: x,
            inputs=viz_state2,
            outputs=viz_image2
        )
        
        viz_state3.change(
            fn=lambda x: x,
            inputs=viz_state3,
            outputs=viz_image3
        )
    
    with gr.Tab("⚙️ Model Settings"):
        gr.HTML("<h3 style='color: #2E86AB;'>Model Configuration</h3>")
        
        model_path_input = gr.Textbox(
            label="Model Path",
            value="./",
            placeholder="Path to model directory"
        )
        
        load_model_btn = gr.Button("📥 Load Model", variant="secondary")
        model_status = gr.Textbox(
            label="Status",
            interactive=False,
            value="No model loaded"
        )
        
        load_model_btn.click(
            fn=load_model_wrapper,
            inputs=model_path_input,
            outputs=model_status
        )
    
    with gr.Tab("📊 Dataset"):
        gr.Markdown("""
        ## Training and Test Datasets
        
        ### Fine-tuning Dataset (Training)
        
        The model was trained on a dataset comprising **1,439 RNA–drug interaction pairs**, including:
        - **759 unique compounds** (SMILES representations)
        - **294 unique RNA sequences**
        - Dissociation constants (pKd values) for binding affinity prediction
        
        **RNA Sequence Distribution by Type:**
        
        | RNA Sequence Type | Number of Interactions |
        |-------------------|------------------------|
        | Aptamers          | 520                    |
        | Ribosomal         | 295                    |
        | Viral RNAs        | 281                    |
        | miRNAs            | 146                    |
        | Riboswitches      | 100                    |
        | Repeats           | 97                     |
        | **Total**         | **1,439**              |
        
        ### External Evaluation Dataset (Test)
        
        Model validation was performed using external ROBIN classification datasets containing **5,534 RNA–drug pairs**:
        - **2,991 positive interactions**
        - **2,538 negative interactions**
        
        **Test Dataset Composition:**
        - **1,617 aptamer pairs** (5 unique RNA sequences)
        - **1,828 viral RNA pairs** (6 unique RNA sequences)  
        - **1,459 riboswitch pairs** (5 unique RNA sequences)
        - **630 miRNA pairs** (3 unique RNA sequences)
        
        ### Dataset Downloads

        - [Training Dataset Download](https://huggingface.co/spaces/IlPakoZ/DLRNA-BERTa/resolve/main/datasets/training_data.csv?download=true)
        - [Test Dataset Download](https://huggingface.co/spaces/IlPakoZ/DLRNA-BERTa/resolve/main/datasets/test_data.csv?download=true)

        ### Citation
        
        Original datasets published by:
        **Krishnan et al.** - Available on the RSAPred website in PDF format.

        *Reference:*
        ```bibtex
            @article{krishnan2024reliable,
            title={Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning},
            author={Krishnan, Sowmya R and Roy, Arijit and Gromiha, M Michael},
            journal={Briefings in Bioinformatics},
            volume={25},
            number={2},
            pages={bbae002},
            year={2024},
            publisher={Oxford University Press}
            }
            ``` 
        """)
    with gr.Tab("ℹ️ About"):
        gr.Markdown("""
        ## About this application
        
        This application implements DLRNA-BERTa, a Dual Langauge RoBERTa Transformer model for predicting drug to RNA target interactions. The model architecture includes:
        
        - **Target encoder**: Processes RNA sequences using RNA-BERTa
        - **Drug encoder**: Processes molecular SMILES notation using ChemBERTa
        - **Cross-attention mechanism**: Captures interactions between drugs and targets
        - **Regression head**: Predicts binding affinity scores (pKd values)
        
        ### Input requirements:
        - **Target sequence**: RNA sequence of the target (nucleotide sequences: A, U, G, C)
        - **Drug SMILES**: Simplified Molecular Input Line Entry System notation
        
        ### Model features:
        - Cross-attention for drug-target interaction modeling
        - Dropout for regularization
        - Layer normalization for stable training
        - Interpretability mode for contribution and attention visualization
        
        ### Usage tips:
        1. Load a trained model using the Model Settings tab (optional)
        2. Enter a RNA sequence and drug SMILES in the Prediction & Analysis tab
        3. Click "Predict Interaction" for binding affinity prediction only
        4. Click "Generate Visualizations" to create detailed interaction analysis - results will appear in the Visualizations tab
        
        For best results, ensure your input sequences are properly formatted and within reasonable length limits (max 512 tokens).
        
        ### Visualization features:
        - **Cross-attention heatmap**: Shows cross-attention weights between drug and target tokens
        - **Unnormalized pKd contribution**: Shows unnormalized signed contributions from each target token (only when pKd > 0)
        - **Normalized pKd contribution**: Shows normalized non-negative contributions from each target token
        
        ### Performance metrics:
        - Training on diverse drug-target interaction datasets
        - Evaluated using RMSE, Pearson correlation, and Concordance Index
        - Optimized for both predictive accuracy and interpretability

        ### GitHub repository:
        - The full model GitHub repository can be found here: https://github.com/IlPakoZ/dlrnaberta-dti-prediction
        
        ### Contribution:
        - Special thanks to Umut Onur Özcan for help in developing this space:)

        ### Contact:
        - Ziaurrehman Tanoli ([email protected])
            Principal investigator at Institute for Molecular Medicine Finland
            HiLIFE, University of Helsinki, Finland.
            
        """)

# Launch the app
if __name__ == "__main__":
    # Try to load model on startup
    if os.path.exists("./config.json"):
        app.load_model("./")
    
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        show_error=True
    )