app.py

import gradio as gr
import torch
import joblib
import numpy as np
from itertools import product
import torch.nn as nn
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import io
from PIL import Image
from scipy.interpolate import interp1d

###############################################################################
# 1. MODEL DEFINITION
###############################################################################

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)

###############################################################################
# 2. FASTA PARSING & K-MER FEATURE ENGINEERING
###############################################################################

def parse_fasta(text):
    sequences = []
    current_header = None
    current_sequence = []
    for line in text.strip().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 sequence_to_kmer_vector(sequence: str, k: int = 4) -> np.ndarray:
    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 /= total_kmers
    return vec

###############################################################################
# 3. SHAP-VALUE (ABLATION) CALCULATION
###############################################################################

def calculate_shap_values(model, x_tensor):
    model.eval()
    with torch.no_grad():
        baseline_output = model(x_tensor)
        baseline_probs = torch.softmax(baseline_output, dim=1)
        baseline_prob = baseline_probs[0, 1].item()  # Prob of 'human'
        shap_values = []
        x_zeroed = x_tensor.clone()
        for i in range(x_tensor.shape[1]):
            original_val = x_zeroed[0, i].item()
            x_zeroed[0, i] = 0.0
            output = model(x_zeroed)
            probs = torch.softmax(output, dim=1)
            prob = probs[0, 1].item()
            shap_values.append(baseline_prob - prob)
            x_zeroed[0, i] = original_val
    return np.array(shap_values), baseline_prob

###############################################################################
# 4. PER-BASE SHAP AGGREGATION
###############################################################################

def compute_positionwise_scores(sequence, shap_values, k=4):
    kmers = [''.join(p) for p in product("ACGT", repeat=k)]
    kmer_dict = {km: i for i, km in enumerate(kmers)}
    seq_len = len(sequence)
    shap_sums = np.zeros(seq_len, dtype=np.float32)
    coverage = np.zeros(seq_len, dtype=np.float32)
    for i in range(seq_len - k + 1):
        kmer = sequence[i:i+k]
        if kmer in kmer_dict:
            val = shap_values[kmer_dict[kmer]]
            shap_sums[i:i+k] += val
            coverage[i:i+k] += 1
    with np.errstate(divide='ignore', invalid='ignore'):
        shap_means = np.where(coverage > 0, shap_sums / coverage, 0.0)
    return shap_means

###############################################################################
# 5. FIND EXTREME SHAP REGIONS
###############################################################################

def find_extreme_subregion(shap_means, window_size=500, mode="max"):
    n = len(shap_means)
    if n == 0: return (0, 0, 0.0)
    if window_size >= n:
        return (0, n, float(np.mean(shap_means)))
    csum = np.zeros(n + 1, dtype=np.float32)
    csum[1:] = np.cumsum(shap_means)
    best_start = 0
    best_sum = csum[window_size] - csum[0]
    best_avg = best_sum / window_size
    for start in range(1, n - window_size + 1):
        wsum = csum[start + window_size] - csum[start]
        wavg = wsum / window_size
        if mode == "max" and wavg > best_avg:
            best_avg = wavg; best_start = start
        elif mode == "min" and wavg < best_avg:
            best_avg = wavg; best_start = start
    return (best_start, best_start + window_size, float(best_avg))

###############################################################################
# 6. PLOTTING / UTILITIES
###############################################################################

def fig_to_image(fig):
    buf = io.BytesIO()
    fig.savefig(buf, format='png', bbox_inches='tight', dpi=150)
    buf.seek(0)
    img = Image.open(buf)
    plt.close(fig)
    return img

def get_zero_centered_cmap():
    colors = [(0.0, 'blue'), (0.5, 'white'), (1.0, 'red')]
    return mcolors.LinearSegmentedColormap.from_list("blue_white_red", colors)

def plot_linear_heatmap(shap_means, title="Per-base SHAP Heatmap", start=None, end=None):
    if start is not None and end is not None:
        local_shap = shap_means[start:end]
        subtitle = f" (positions {start}-{end})"
    else:
        local_shap = shap_means
        subtitle = ""
    if len(local_shap) == 0:
        local_shap = np.array([0.0])
    heatmap_data = local_shap.reshape(1, -1)
    min_val = np.min(local_shap)
    max_val = np.max(local_shap)
    extent = max(abs(min_val), abs(max_val))
    cmap = get_zero_centered_cmap()
    fig, ax = plt.subplots(figsize=(12, 1.8))
    cax = ax.imshow(heatmap_data, aspect='auto', cmap=cmap, vmin=-extent, vmax=extent)
    cbar = plt.colorbar(cax, orientation='horizontal', pad=0.25, aspect=40, shrink=0.8)
    cbar.ax.tick_params(labelsize=8)
    cbar.set_label('SHAP Contribution', fontsize=9, labelpad=5)
    ax.set_yticks([])
    ax.set_xlabel('Position in Sequence', fontsize=10)
    ax.set_title(f"{title}{subtitle}", pad=10)
    plt.subplots_adjust(bottom=0.25, left=0.05, right=0.95)
    return fig

def create_importance_bar_plot(shap_values, kmers, top_k=10):
    plt.rcParams.update({'font.size': 10})
    fig = plt.figure(figsize=(10, 5))
    indices = np.argsort(np.abs(shap_values))[-top_k:]
    values = shap_values[indices]
    features = [kmers[i] for i in indices]
    colors = ['#99ccff' if v < 0 else '#ff9999' for v in values]
    plt.barh(range(len(values)), values, color=colors)
    plt.yticks(range(len(values)), features)
    plt.xlabel('SHAP Value (impact on model output)')
    plt.title(f'Top {top_k} Most Influential k-mers')
    plt.gca().invert_yaxis()
    plt.tight_layout()
    return fig

def plot_shap_histogram(shap_array, title="SHAP Distribution in Region"):
    fig, ax = plt.subplots(figsize=(6, 4))
    ax.hist(shap_array, bins=30, color='gray', edgecolor='black')
    ax.axvline(0, color='red', linestyle='--', label='0.0')
    ax.set_xlabel("SHAP Value")
    ax.set_ylabel("Count")
    ax.set_title(title)
    ax.legend()
    plt.tight_layout()
    return fig

def compute_gc_content(sequence):
    if not sequence: return 0
    gc_count = sequence.count('G') + sequence.count('C')
    return (gc_count / len(sequence)) * 100.0

###############################################################################
# 7. MAIN ANALYSIS STEP (Gradio Step 1)
###############################################################################

def analyze_sequence(file_obj, top_kmers=10, fasta_text="", window_size=500):
    if fasta_text.strip():
        text = fasta_text.strip()
    elif file_obj is not None:
        try:
            with open(file_obj, 'r') as f:
                text = f.read()
        except Exception as e:
            return (f"Error reading file: {str(e)}", None, None, None, None)
    else:
        return ("Please provide a FASTA sequence.", None, None, None, None)

    sequences = parse_fasta(text)
    if not sequences:
        return ("No valid FASTA sequences found.", None, None, None, None)
    header, seq = sequences[0]

    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    try:
        state_dict = torch.load('model.pt', map_location=device, weights_only=True)
        model = VirusClassifier(256).to(device)
        model.load_state_dict(state_dict)
        scaler = joblib.load('scaler.pkl')
    except Exception as e:
        return (f"Error loading model/scaler: {str(e)}", None, None, None, None)

    freq_vector = sequence_to_kmer_vector(seq)
    scaled_vector = scaler.transform(freq_vector.reshape(1, -1))
    x_tensor = torch.FloatTensor(scaled_vector).to(device)

    shap_values, prob_human = calculate_shap_values(model, x_tensor)
    prob_nonhuman = 1.0 - prob_human
    classification = "Human" if prob_human > 0.5 else "Non-human"
    confidence = max(prob_human, prob_nonhuman)

    shap_means = compute_positionwise_scores(seq, shap_values, k=4)
    max_start, max_end, max_avg = find_extreme_subregion(shap_means, window_size, mode="max")
    min_start, min_end, min_avg = find_extreme_subregion(shap_means, window_size, mode="min")

    results_text = (
        f"Sequence: {header}\n"
        f"Length: {len(seq):,} bases\n"
        f"Classification: {classification}\n"
        f"Confidence: {confidence:.3f}\n"
        f"(Human Probability: {prob_human:.3f}, Non-human Probability: {prob_nonhuman:.3f})\n\n"
        f"---\n"
        f"**Most Human-Pushing {window_size}-bp Subregion**:\n"
        f"Start: {max_start}, End: {max_end}, Avg SHAP: {max_avg:.4f}\n\n"
        f"**Most Non-Human–Pushing {window_size}-bp Subregion**:\n"
        f"Start: {min_start}, End: {min_end}, Avg SHAP: {min_avg:.4f}"
    )

    kmers = [''.join(p) for p in product("ACGT", repeat=4)]
    bar_fig = create_importance_bar_plot(shap_values, kmers, top_kmers)
    bar_img = fig_to_image(bar_fig)

    heatmap_fig = plot_linear_heatmap(shap_means, title="Genome-wide SHAP")
    heatmap_img = fig_to_image(heatmap_fig)

    state_dict_out = {"seq": seq, "shap_means": shap_means}

    return (results_text, bar_img, heatmap_img, state_dict_out, header)

###############################################################################
# 8. SUBREGION ANALYSIS (Gradio Step 2)
###############################################################################

def analyze_subregion(state, header, region_start, region_end):
    if not state or "seq" not in state or "shap_means" not in state:
        return ("No sequence data found. Please run Step 1 first.", None, None)
    seq = state["seq"]
    shap_means = state["shap_means"]
    region_start = int(region_start)
    region_end = int(region_end)
    region_start = max(0, min(region_start, len(seq)))
    region_end = max(0, min(region_end, len(seq)))
    if region_end <= region_start:
        return ("Invalid region range. End must be > Start.", None, None)
    region_seq = seq[region_start:region_end]
    region_shap = shap_means[region_start:region_end]
    gc_percent = compute_gc_content(region_seq)
    avg_shap = float(np.mean(region_shap))
    positive_fraction = np.mean(region_shap > 0)
    negative_fraction = np.mean(region_shap < 0)
    if avg_shap > 0.05:
        region_classification = "Likely pushing toward human"
    elif avg_shap < -0.05:
        region_classification = "Likely pushing toward non-human"
    else:
        region_classification = "Near neutral (no strong push)"
    region_info = (
        f"Analyzing subregion of {header} from {region_start} to {region_end}\n"
        f"Region length: {len(region_seq)} bases\n"
        f"GC content: {gc_percent:.2f}%\n"
        f"Average SHAP in region: {avg_shap:.4f}\n"
        f"Fraction with SHAP > 0 (toward human): {positive_fraction:.2f}\n"
        f"Fraction with SHAP < 0 (toward non-human): {negative_fraction:.2f}\n"
        f"Subregion interpretation: {region_classification}\n"
    )
    heatmap_fig = plot_linear_heatmap(shap_means, title="Subregion SHAP", start=region_start, end=region_end)
    heatmap_img = fig_to_image(heatmap_fig)
    hist_fig = plot_shap_histogram(region_shap, title="SHAP Distribution in Subregion")
    hist_img = fig_to_image(hist_fig)
    return (region_info, heatmap_img, hist_img)

###############################################################################
# 9. COMPARISON ANALYSIS FUNCTIONS
###############################################################################

def get_zero_centered_cmap():
    """Create a zero-centered blue-white-red colormap"""
    colors = [(0.0, 'blue'), (0.5, 'white'), (1.0, 'red')]
    return mcolors.LinearSegmentedColormap.from_list("blue_white_red", colors)

def compute_shap_difference(shap1_norm, shap2_norm):
    """Compute the SHAP difference between normalized sequences"""
    return shap2_norm - shap1_norm

def plot_comparative_heatmap(shap_diff, seq1_length, seq2_length, title="SHAP Difference Heatmap"):
    """
    Plot heatmap using both relative positions (0-100%) and actual sequence positions
    
    Parameters:
        shap_diff: numpy array of SHAP differences
        seq1_length: length of sequence 1
        seq2_length: length of sequence 2
        title: plot title
    """
    heatmap_data = shap_diff.reshape(1, -1)
    extent = max(abs(np.min(shap_diff)), abs(np.max(shap_diff)))
    
    # Create figure with additional space for the second x-axis
    fig, ax = plt.subplots(figsize=(12, 2.4))
    
    # Plot main heatmap
    cmap = get_zero_centered_cmap()
    cax = ax.imshow(heatmap_data, aspect='auto', cmap=cmap, vmin=-extent, vmax=extent)
    
    # Create percentage-based x-axis ticks (top)
    num_ticks = 5
    tick_positions = np.linspace(0, shap_diff.shape[0]-1, num_ticks)
    tick_labels = [f"{int(x*100)}%" for x in np.linspace(0, 1, num_ticks)]
    ax.set_xticks(tick_positions)
    ax.set_xticklabels(tick_labels)
    
    # Create second x-axis for actual positions (bottom)
    ax2 = ax.twiny()
    ax2.set_xlim(ax.get_xlim())
    
    # Calculate actual positions for both sequences
    seq1_positions = np.linspace(0, seq1_length, num_ticks)
    seq2_positions = np.linspace(0, seq2_length, num_ticks)
    
    # Format position labels with appropriate scaling
    def format_position(x):
        if x >= 1e6:
            return f"{x/1e6:.1f}M"
        elif x >= 1e3:
            return f"{x/1e3:.0f}K"
        else:
            return f"{int(x)}"
    
    seq1_labels = [format_position(x) for x in seq1_positions]
    seq2_labels = [format_position(x) for x in seq2_positions]
    
    # Set positions for bottom axis
    ax2.set_xticks(tick_positions)
    ax2.set_xticklabels([f"S1: {s1}\nS2: {s2}" for s1, s2 in zip(seq1_labels, seq2_labels)])
    
    # Add colorbar
    cbar = plt.colorbar(cax, orientation='horizontal', pad=0.25, aspect=40, shrink=0.8)
    cbar.ax.tick_params(labelsize=8)
    cbar.set_label('SHAP Difference (Seq2 - Seq1)', fontsize=9, labelpad=5)
    
    # Adjust labels and layout
    ax.set_yticks([])
    ax.set_xlabel('Relative Position (%)', fontsize=10)
    ax2.set_xlabel('Sequence Positions', fontsize=10)
    ax.set_title(title, pad=10)
    
    # Adjust layout to prevent label overlap
    plt.subplots_adjust(bottom=0.35, left=0.05, right=0.95, top=0.85)
    
    return fig

def plot_shap_histogram(shap_array, title="SHAP Distribution", num_bins=30):
    """
    Plot histogram of SHAP values with configurable number of bins
    """
    fig, ax = plt.subplots(figsize=(6, 4))
    ax.hist(shap_array, bins=num_bins, color='gray', edgecolor='black', alpha=0.7)
    ax.axvline(0, color='red', linestyle='--', label='0.0')
    ax.set_xlabel("SHAP Value")
    ax.set_ylabel("Count")
    ax.set_title(title)
    ax.legend()
    plt.tight_layout()
    return fig

def calculate_adaptive_parameters(len1, len2):
    """
    Calculate adaptive parameters based on sequence lengths and their difference.
    Returns: (num_points, smooth_window, resolution_factor)
    """
    length_diff = abs(len1 - len2)
    max_length = max(len1, len2)
    min_length = min(len1, len2)
    length_ratio = min_length / max_length
    
    # Base number of points scales with sequence length
    base_points = min(2000, max(500, max_length // 100))
    
    # Adjust parameters based on sequence properties
    if length_diff < 500:
        resolution_factor = 2.0
        num_points = min(3000, base_points * 2)
        smooth_window = max(10, length_diff // 50)
    elif length_diff < 5000:
        resolution_factor = 1.5
        num_points = min(2000, base_points * 1.5)
        smooth_window = max(20, length_diff // 100)
    elif length_diff < 50000:
        resolution_factor = 1.0
        num_points = base_points
        smooth_window = max(50, length_diff // 200)
    else:
        resolution_factor = 0.75
        num_points = max(500, base_points // 2)
        smooth_window = max(100, length_diff // 500)
    
    # Adjust window size based on length ratio
    smooth_window = int(smooth_window * (1 + (1 - length_ratio)))
    
    return int(num_points), int(smooth_window), resolution_factor

def sliding_window_smooth(values, window_size=50):
    """
    Apply sliding window smoothing with edge handling
    """
    if window_size < 3:
        return values
    
    # Create window with exponential decay at edges
    window = np.ones(window_size)
    decay = np.exp(-np.linspace(0, 3, window_size // 2))
    window[:window_size // 2] = decay
    window[-(window_size // 2):] = decay[::-1]
    window = window / window.sum()
    
    # Apply convolution
    smoothed = np.convolve(values, window, mode='valid')
    
    # Handle edges
    pad_size = len(values) - len(smoothed)
    pad_left = pad_size // 2
    pad_right = pad_size - pad_left
    
    result = np.zeros_like(values)
    result[pad_left:-pad_right] = smoothed
    result[:pad_left] = values[:pad_left]
    result[-pad_right:] = values[-pad_right:]
    
    return result

def normalize_shap_lengths(shap1, shap2):
    """
    Normalize and smooth SHAP values with dynamic adaptation
    """
    # Calculate adaptive parameters
    num_points, smooth_window, _ = calculate_adaptive_parameters(len(shap1), len(shap2))
    
    # Apply initial smoothing
    shap1_smooth = sliding_window_smooth(shap1, smooth_window)
    shap2_smooth = sliding_window_smooth(shap2, smooth_window)
    
    # Create relative positions and interpolate
    x1 = np.linspace(0, 1, len(shap1_smooth))
    x2 = np.linspace(0, 1, len(shap2_smooth))
    x_norm = np.linspace(0, 1, num_points)
    
    shap1_interp = np.interp(x_norm, x1, shap1_smooth)
    shap2_interp = np.interp(x_norm, x2, shap2_smooth)
    
    return shap1_interp, shap2_interp, smooth_window

def analyze_sequence_comparison(file1, file2, fasta1="", fasta2=""):
    """
    Compare two sequences with adaptive parameters and visualization
    """
    try:
        # Analyze first sequence
        res1 = analyze_sequence(file1, top_kmers=10, fasta_text=fasta1, window_size=500)
        if isinstance(res1[0], str) and "Error" in res1[0]:
            return (f"Error in sequence 1: {res1[0]}", None, None)
        
        # Analyze second sequence
        res2 = analyze_sequence(file2, top_kmers=10, fasta_text=fasta2, window_size=500)
        if isinstance(res2[0], str) and "Error" in res2[0]:
            return (f"Error in sequence 2: {res2[0]}", None, None)

        # Extract SHAP values and sequence info
        shap1 = res1[3]["shap_means"]
        shap2 = res2[3]["shap_means"]
        
        # Calculate sequence properties
        len1, len2 = len(shap1), len(shap2)
        length_diff = abs(len1 - len2)
        length_ratio = min(len1, len2) / max(len1, len2)
        
        # Normalize and compare sequences
        shap1_norm, shap2_norm, smooth_window = normalize_shap_lengths(shap1, shap2)
        shap_diff = compute_shap_difference(shap1_norm, shap2_norm)
        
        # Calculate adaptive threshold and statistics
        base_threshold = 0.05
        adaptive_threshold = base_threshold * (1 + (1 - length_ratio))
        if length_diff > 50000:
            adaptive_threshold *= 1.5
        
        # Calculate comparison statistics
        avg_diff = np.mean(shap_diff)
        std_diff = np.std(shap_diff)
        max_diff = np.max(shap_diff)
        min_diff = np.min(shap_diff)
        substantial_diffs = np.abs(shap_diff) > adaptive_threshold
        frac_different = np.mean(substantial_diffs)

        # Extract classifications
        try:
            classification1 = res1[0].split('Classification: ')[1].split('\n')[0].strip()
            classification2 = res2[0].split('Classification: ')[1].split('\n')[0].strip()
        except:
            classification1 = "Unknown"
            classification2 = "Unknown"
        
        # Format output text
        comparison_text = (
            "Sequence Comparison Results:\n"
            f"Sequence 1: {res1[4]}\n"
            f"Length: {len1:,} bases\n"
            f"Classification: {classification1}\n\n"
            f"Sequence 2: {res2[4]}\n"
            f"Length: {len2:,} bases\n"
            f"Classification: {classification2}\n\n"
            "Comparison Parameters:\n"
            f"Length Difference: {length_diff:,} bases\n"
            f"Length Ratio: {length_ratio:.3f}\n"
            f"Smoothing Window: {smooth_window} points\n"
            f"Adaptive Threshold: {adaptive_threshold:.3f}\n\n"
            "Statistics:\n"
            f"Average SHAP difference: {avg_diff:.4f}\n"
            f"Standard deviation: {std_diff:.4f}\n"
            f"Max difference: {max_diff:.4f} (Seq2 more human-like)\n"
            f"Min difference: {min_diff:.4f} (Seq1 more human-like)\n"
            f"Fraction with substantial differences: {frac_different:.2%}\n\n"
            "Note: All parameters automatically adjusted based on sequence properties\n\n"
            "Interpretation:\n"
            "- Red regions: Sequence 2 more human-like\n"
            "- Blue regions: Sequence 1 more human-like\n"
            "- White regions: Similar between sequences"
        )
        
        # Generate visualizations
        heatmap_fig = plot_comparative_heatmap(
            shap_diff,
            title=f"SHAP Difference Heatmap (window: {smooth_window})"
        )
        heatmap_img = fig_to_image(heatmap_fig)
        
        # Create histogram with adaptive bins
        num_bins = max(20, min(50, int(np.sqrt(len(shap_diff)))))
        hist_fig = plot_shap_histogram(
            shap_diff,
            title="Distribution of SHAP Differences",
            num_bins=num_bins
        )
        hist_img = fig_to_image(hist_fig)
        
        return comparison_text, heatmap_img, hist_img
        
    except Exception as e:
        error_msg = f"Error during sequence comparison: {str(e)}"
        return error_msg, None, None
        
###############################################################################
# 10. BUILD GRADIO INTERFACE
###############################################################################

css = """
.gradio-container {
    font-family: 'IBM Plex Sans', sans-serif;
}
"""

with gr.Blocks(css=css) as iface:
    gr.Markdown("""
    # Virus Host Classifier
    **Step 1**: Predict overall viral sequence origin (human vs non-human) and identify extreme regions.  
    **Step 2**: Explore subregions to see local SHAP signals, distribution, GC content, etc.
    
    **Color Scale**: Negative SHAP = Blue, Zero = White, Positive = Red.
    """)
    
    with gr.Tab("1) Full-Sequence Analysis"):
        with gr.Row():
            with gr.Column(scale=1):
                file_input = gr.File(label="Upload FASTA file", file_types=[".fasta", ".fa", ".txt"], type="filepath")
                text_input = gr.Textbox(label="Or paste FASTA sequence", placeholder=">sequence_name\nACGTACGT...", lines=5)
                top_k = gr.Slider(minimum=5, maximum=30, value=10, step=1, label="Number of top k-mers to display")
                win_size = gr.Slider(minimum=100, maximum=5000, value=500, step=100, label="Window size for 'most pushing' subregions")
                analyze_btn = gr.Button("Analyze Sequence", variant="primary")
            with gr.Column(scale=2):
                results_box = gr.Textbox(label="Classification Results", lines=12, interactive=False)
                kmer_img = gr.Image(label="Top k-mer SHAP")
                genome_img = gr.Image(label="Genome-wide SHAP Heatmap (Blue=neg, White=0, Red=pos)")
        seq_state = gr.State()
        header_state = gr.State()
        analyze_btn.click(
            analyze_sequence,
            inputs=[file_input, top_k, text_input, win_size],
            outputs=[results_box, kmer_img, genome_img, seq_state, header_state]
        )

    with gr.Tab("2) Subregion Exploration"):
        gr.Markdown("""
        **Subregion Analysis**  
        Select start/end positions to view local SHAP signals, distribution, GC content, etc.
        The heatmap also uses the same Blue-White-Red scale.
        """)
        with gr.Row():
            region_start = gr.Number(label="Region Start", value=0)
            region_end = gr.Number(label="Region End", value=500)
            region_btn = gr.Button("Analyze Subregion")
        subregion_info = gr.Textbox(label="Subregion Analysis", lines=7, interactive=False)
        with gr.Row():
            subregion_img = gr.Image(label="Subregion SHAP Heatmap (B-W-R)")
            subregion_hist_img = gr.Image(label="SHAP Distribution (Histogram)")
        region_btn.click(
            analyze_subregion,
            inputs=[seq_state, header_state, region_start, region_end],
            outputs=[subregion_info, subregion_img, subregion_hist_img]
        )
    
    with gr.Tab("3) Comparative Analysis"):
        gr.Markdown("""
        **Compare Two Sequences**  
        Upload or paste two FASTA sequences to compare their SHAP patterns.
        The sequences will be normalized to the same length for comparison.
        
        **Color Scale**:  
        - Red: Sequence 2 is more human-like in this region  
        - Blue: Sequence 1 is more human-like in this region  
        - White: No substantial difference
        """)
        with gr.Row():
            with gr.Column(scale=1):
                file_input1 = gr.File(label="Upload first FASTA file", file_types=[".fasta", ".fa", ".txt"], type="filepath")
                text_input1 = gr.Textbox(label="Or paste first FASTA sequence", placeholder=">sequence1\nACGTACGT...", lines=5)
            with gr.Column(scale=1):
                file_input2 = gr.File(label="Upload second FASTA file", file_types=[".fasta", ".fa", ".txt"], type="filepath")
                text_input2 = gr.Textbox(label="Or paste second FASTA sequence", placeholder=">sequence2\nACGTACGT...", lines=5)
        compare_btn = gr.Button("Compare Sequences", variant="primary")
        comparison_text = gr.Textbox(label="Comparison Results", lines=12, interactive=False)
        with gr.Row():
            diff_heatmap = gr.Image(label="SHAP Difference Heatmap")
            diff_hist = gr.Image(label="Distribution of SHAP Differences")
        compare_btn.click(
            analyze_sequence_comparison,
            inputs=[file_input1, file_input2, text_input1, text_input2],
            outputs=[comparison_text, diff_heatmap, diff_hist]
        )
    
    gr.Markdown("""
    ### Interface Features
    - **Overall Classification** (human vs non-human) using k-mer frequencies.
    - **SHAP Analysis** to see which k-mers push classification toward or away from human.
    - **White-Centered SHAP Gradient**: 
      - Negative (blue), 0 (white), Positive (red), with symmetrical color range around 0. 
    - **Identify Subregions** with the strongest push for human or non-human.
    - **Subregion Exploration**: 
      - Local SHAP heatmap & histogram 
      - GC content 
      - Fraction of positions pushing human vs. non-human 
      - Simple logic-based classification
    - **Sequence Comparison**:
      - Compare two sequences to identify regions of difference
      - Normalized comparison to handle different sequence lengths
      - Statistical summary of differences
    """)

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