app.py

import gradio as gr
import torch
import joblib
import numpy as np
from itertools import product
import torch.nn as nn
import shap
import matplotlib.pyplot as plt
import io
from PIL import Image

class VirusClassifier(nn.Module):
    def __init__(self, input_shape: int):
        super(VirusClassifier, self).__init__()
        self.network = nn.Sequential(
            nn.Linear(input_shape, 64),
            nn.GELU(),
            nn.BatchNorm1d(64),
            nn.Dropout(0.3),
            nn.Linear(64, 32),
            nn.GELU(),
            nn.BatchNorm1d(32),
            nn.Dropout(0.3),
            nn.Linear(32, 32),
            nn.GELU(),
            nn.Linear(32, 2)
        )

    def forward(self, x):
        return self.network(x)
    
    def get_feature_importance(self, x):
        """Calculate feature importance using gradient-based method for the human class (index 1)"""
        x.requires_grad_(True)
        output = self.network(x)
        probs = torch.softmax(output, dim=1)
        
        # We focus on the human class (index 1) probability
        human_prob = probs[..., 1]
        human_prob.backward()
        
        # The gradient shows how each feature affects the human probability
        importance = x.grad
        
        return importance, float(human_prob)

def sequence_to_kmer_vector(sequence: str, k: int = 4) -> np.ndarray:
    """Convert sequence to k-mer frequency vector"""
    kmers = [''.join(p) for p in product("ACGT", repeat=k)]
    kmer_dict = {km: i for i, km in enumerate(kmers)}
    vec = np.zeros(len(kmers), dtype=np.float32)
    
    for i in range(len(sequence) - k + 1):
        kmer = sequence[i:i+k]
        if kmer in kmer_dict:
            vec[kmer_dict[kmer]] += 1

    total_kmers = len(sequence) - k + 1
    if total_kmers > 0:
        vec = vec / total_kmers

    return vec

def parse_fasta(text):
    sequences = []
    current_header = None
    current_sequence = []
    
    for line in text.split('\n'):
        line = line.strip()
        if not line:
            continue
        if line.startswith('>'):
            if current_header:
                sequences.append((current_header, ''.join(current_sequence)))
            current_header = line[1:]
            current_sequence = []
        else:
            current_sequence.append(line.upper())
    if current_header:
        sequences.append((current_header, ''.join(current_sequence)))
    return sequences

def predict(file_obj):
    if file_obj is None:
        return "Please upload a FASTA file", None
    
    try:
        if isinstance(file_obj, str):
            text = file_obj
        else:
            text = file_obj.decode('utf-8')
    except Exception as e:
        return f"Error reading file: {str(e)}", None

    k = 4
    kmers = [''.join(p) for p in product("ACGT", repeat=k)]
    kmer_dict = {km: i for i, km in enumerate(kmers)}
    
    try:
        device = 'cuda' if torch.cuda.is_available() else 'cpu'
        model = VirusClassifier(256).to(device)
        state_dict = torch.load('model.pt', map_location=device)
        model.load_state_dict(state_dict)
        scaler = joblib.load('scaler.pkl')
        model.eval()
    except Exception as e:
        return f"Error loading model: {str(e)}", None

    results_text = ""
    plot_image = None
    
    try:
        sequences = parse_fasta(text)
        header, seq = sequences[0]
        
        raw_freq_vector = sequence_to_kmer_vector(seq)
        kmer_vector = scaler.transform(raw_freq_vector.reshape(1, -1))
        X_tensor = torch.FloatTensor(kmer_vector).to(device)
        
        # Calculate final probabilities first
        with torch.no_grad():
            output = model(X_tensor)
            probs = torch.softmax(output, dim=1)
            human_prob = float(probs[0][1])
        
        # Get feature importance using integrated gradients
        baseline = torch.zeros_like(X_tensor)  # baseline of zeros
        steps = 50
        
        all_importance = []
        for i in range(steps + 1):
            alpha = i / steps
            interpolated = baseline + alpha * (X_tensor - baseline)
            interpolated.requires_grad_(True)
            
            output = model(interpolated)
            probs = torch.softmax(output, dim=1)
            human_class = probs[..., 1]
            
            if interpolated.grad is not None:
                interpolated.grad.zero_()
            human_class.backward()
            all_importance.append(interpolated.grad.cpu().numpy())
        
        # Average the gradients
        kmer_importance = np.mean(all_importance, axis=0)[0]
        # Scale to match probability difference
        target_diff = human_prob - 0.5  # difference from neutral prediction
        current_sum = np.sum(kmer_importance)
        if current_sum != 0:  # avoid division by zero
            kmer_importance = kmer_importance * (target_diff / current_sum)
        
        # Get top k-mers by absolute importance
        top_k = 10
        top_indices = np.argsort(np.abs(kmer_importance))[-top_k:][::-1]
        important_kmers = [
            {
                'kmer': list(kmer_dict.keys())[list(kmer_dict.values()).index(i)],
                'importance': float(kmer_importance[i]),
                'frequency': float(raw_freq_vector[i]),
                'scaled': float(kmer_vector[0][i])
            }
            for i in top_indices
        ]
        
        # Prepare data for SHAP waterfall plot
        top_features = [item['kmer'] for item in important_kmers]
        top_values = [item['importance'] for item in important_kmers]
        
        # Calculate the impact of remaining features
        others_mask = np.ones_like(kmer_importance, dtype=bool)
        others_mask[top_indices] = False
        others_sum = np.sum(kmer_importance[others_mask])
        
        top_features.append("Others")
        top_values.append(others_sum)
        
        # Calculate final probabilities first
        with torch.no_grad():
            output = model(X_tensor)
            probs = torch.softmax(output, dim=1)
            human_prob = float(probs[0][1])

        # Create SHAP explanation
        explanation = shap.Explanation(
            values=np.array(top_values),
            base_values=0.5,  # Start from neutral prediction
            data=np.array([
                raw_freq_vector[kmer_dict[feat]] if feat != "Others" 
                else np.sum(raw_freq_vector[others_mask]) 
                for feat in top_features
            ]),
            feature_names=top_features
        )
        explanation.expected_value = 0.5  # Start from neutral prediction

        # Create waterfall plot
        plt.figure(figsize=(10, 6))
        fig = shap.plots._waterfall.waterfall_legacy(
            explanation,
            show=False,
            max_display=11  # Show all features including "Others"
        )
        plt.title(f"Feature contributions to human probability (final prob: {human_prob:.3f})")
        
        # Save plot
        buf = io.BytesIO()
        plt.savefig(buf, format='png', bbox_inches='tight', dpi=300)
        buf.seek(0)
        plot_image = Image.open(buf)
        plt.close()

        # Calculate final probabilities
        with torch.no_grad():
            output = model(X_tensor)
            probs = torch.softmax(output, dim=1)

        pred_class = 1 if probs[0][1] > probs[0][0] else 0
        pred_label = 'human' if pred_class == 1 else 'non-human'
        
        # Generate results text
        results_text += f"""Sequence: {header}
Prediction: {pred_label}
Confidence: {float(max(probs[0])):0.4f}
Human probability: {float(probs[0][1]):0.4f}
Non-human probability: {float(probs[0][0]):0.4f}
Most influential k-mers (ranked by importance):"""
        
        for kmer in important_kmers:
            direction = "human" if kmer['importance'] > 0 else "non-human"
            results_text += f"\n  {kmer['kmer']}: "
            results_text += f"pushes toward {direction} (impact={abs(kmer['importance']):.4f}), "
            results_text += f"occurrence={kmer['frequency']*100:.2f}% of sequence "
            if kmer['scaled'] > 0:
                results_text += f"(appears {abs(kmer['scaled']):.2f}σ more than average)"
            else:
                results_text += f"(appears {abs(kmer['scaled']):.2f}σ less than average)"
        
    except Exception as e:
        return f"Error processing sequences: {str(e)}", None

    return results_text, plot_image

iface = gr.Interface(
    fn=predict,
    inputs=gr.File(label="Upload FASTA file", type="binary"),
    outputs=[gr.Textbox(label="Results"), gr.Image(label="SHAP Waterfall Plot")],
    title="Virus Host Classifier"
)

if __name__ == "__main__":
    iface.launch(share=True)