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
import numpy as np

###############################################################################
# 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):
    """Parse FASTA formatted text into a list of (header, sequence)."""
    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:
    """Convert a sequence to a k-mer frequency vector for classification."""
    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

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

def calculate_shap_values(model, x_tensor):
    """
    Calculate SHAP values using a simple ablation approach.
    Returns shap_values, prob_human
    """
    model.eval()
    with torch.no_grad():
        # Baseline
        baseline_output = model(x_tensor)
        baseline_probs = torch.softmax(baseline_output, dim=1)
        baseline_prob = baseline_probs[0, 1].item()  # Probability of 'human' class
        
        # Zeroing each feature to measure impact
        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()
            impact = baseline_prob - prob
            shap_values.append(impact)
            x_zeroed[0, i] = original_val  # restore
    return np.array(shap_values), baseline_prob

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

def compute_positionwise_scores(sequence, shap_values, k=4):
    """
    Returns an array of per-base SHAP contributions by averaging
    the k-mer SHAP values of all k-mers covering that base.
    """
    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"):
    """
    Finds the subregion of length `window_size` that has the maximum 
    (mode="max") or minimum (mode="min") average SHAP.
    Returns (best_start, best_end, best_avg).
    """
    n = len(shap_means)
    if n == 0:
        return (0, 0, 0.0)
    if window_size >= n:
        # entire sequence
        avg_val = float(np.mean(shap_means))
        return (0, n, avg_val)
    
    # We'll build csum of length n+1
    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":
            if wavg > best_avg:
                best_avg = wavg
                best_start = start
        else:  # mode == "min"
            if 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):
    """Convert a Matplotlib figure to a PIL Image for Gradio."""
    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():
    """
    Creates a custom diverging colormap that is:
    - Blue for negative
    - White for zero
    - Red for positive
    """
    colors = [
        (0.0, 'blue'),   # negative
        (0.5, 'white'),  # zero
        (1.0, 'red')     # positive
    ]
    cmap = mcolors.LinearSegmentedColormap.from_list("blue_white_red", colors)
    return cmap

def plot_linear_heatmap(shap_means, title="Per-base SHAP Heatmap", start=None, end=None):
    """
    Plots a 1D heatmap of per-base SHAP contributions with a custom colormap:
    - Negative = blue
    - 0 = white
    - Positive = red
    """
    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])
        
    # Build 2D array for imshow
    heatmap_data = local_shap.reshape(1, -1)
    
    # Force symmetrical range
    min_val = np.min(local_shap)
    max_val = np.max(local_shap)
    extent = max(abs(min_val), abs(max_val))
    
    # Create custom colormap
    custom_cmap = get_zero_centered_cmap()
    
    # Create figure with adjusted height ratio
    fig, ax = plt.subplots(figsize=(12, 1.8))  # Reduced height
    
    # Plot heatmap
    cax = ax.imshow(
        heatmap_data,
        aspect='auto',
        cmap=custom_cmap,
        vmin=-extent,
        vmax=+extent
    )
    
    # Configure colorbar with more subtle positioning
    cbar = plt.colorbar(
        cax,
        orientation='horizontal',
        pad=0.25,  # Reduced padding
        aspect=40,  # Make colorbar thinner
        shrink=0.8  # Make colorbar shorter than plot width
    )
    
    # Style the colorbar
    cbar.ax.tick_params(labelsize=8)  # Smaller tick labels
    cbar.set_label(
        'SHAP Contribution',
        fontsize=9,
        labelpad=5
    )
    
    # Configure main plot
    ax.set_yticks([])
    ax.set_xlabel('Position in Sequence', fontsize=10)
    ax.set_title(f"{title}{subtitle}", pad=10)
    
    # Fine-tune layout
    plt.subplots_adjust(
        bottom=0.25,  # Reduced bottom margin
        left=0.05,    # Tighter left margin
        right=0.95    # Tighter right margin
    )
    
    return fig

def create_importance_bar_plot(shap_values, kmers, top_k=10):
    """Create a bar plot of the most important k-mers."""
    plt.rcParams.update({'font.size': 10})
    fig = plt.figure(figsize=(10, 5))
    
    # Sort by absolute importance
    indices = np.argsort(np.abs(shap_values))[-top_k:]
    values = shap_values[indices]
    features = [kmers[i] for i in indices]
    
    # negative -> blue, positive -> red
    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"):
    """
    Simple histogram of SHAP values in the subregion.
    """
    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):
    """Compute %GC in the sequence (A, C, G, T)."""
    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_comparison(file1, file2, fasta1="", fasta2=""):
    """
    Compare two sequences by analyzing their SHAP differences.
    Returns comparison text and visualizations.
    """
    # Process first sequence
    results1 = analyze_sequence(file1, fasta_text=fasta1)
    if isinstance(results1[0], str) and "Error" in results1[0]:
        return (f"Error in sequence 1: {results1[0]}", None, None)
    
    # Process second sequence
    results2 = analyze_sequence(file2, fasta_text=fasta2)
    if isinstance(results2[0], str) and "Error" in results2[0]:
        return (f"Error in sequence 2: {results2[0]}", None, None)
    
    # Get SHAP means from state dictionaries
    shap1 = results1[3]["shap_means"]
    shap2 = results2[3]["shap_means"]
    
    # Normalize lengths
    shap1_norm, shap2_norm = normalize_shap_lengths(shap1, shap2)
    
    # Compute difference (positive = seq2 more human-like)
    shap_diff = compute_shap_difference(shap1_norm, shap2_norm)
    
    # Calculate some 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)
    
    # Calculate what fraction of positions show substantial differences
    threshold = 0.05  # Arbitrary threshold for "substantial" difference
    substantial_diffs = np.abs(shap_diff) > threshold
    frac_different = np.mean(substantial_diffs)
    
    # Generate comparison text
    # Extract classifications without using split on newline
    classification1 = results1[0].split('Classification: ')[1].split('(')[0].strip()
    classification2 = results2[0].split('Classification: ')[1].split('(')[0].strip()
    
    # Build the text using format method
    comparison_text = (
        "Sequence Comparison Results:\n"
        "Sequence 1: {}\n"
        "Length: {:,} bases\n"
        "Classification: {}\n\n"
        "Sequence 2: {}\n"
        "Length: {:,} bases\n"
        "Classification: {}\n\n"
        "Comparison Statistics:\n"
        "Average SHAP difference: {:.4f}\n"
        "Standard deviation: {:.4f}\n"
        "Max difference: {:.4f} (Seq2 more human-like)\n"
        "Min difference: {:.4f} (Seq1 more human-like)\n"
        "Fraction of positions with substantial differences: {:.2%}\n\n"
        "Interpretation:\n"
        "Positive values (red) indicate regions where Sequence 2 is more human-like\n"
        "Negative values (blue) indicate regions where Sequence 1 is more human-like"
    ).format(
        results1[4], len(shap1), classification1,
        results2[4], len(shap2), classification2,
        avg_diff, std_diff, max_diff, min_diff, frac_different
    )
    
    # Create comparison heatmap
    heatmap_fig = plot_comparative_heatmap(shap_diff)
    heatmap_img = fig_to_image(heatmap_fig)
    
    # Create histogram of differences
    hist_fig = plot_shap_histogram(
        shap_diff,
        title="Distribution of SHAP Differences"
    )
    hist_img = fig_to_image(hist_fig)
    
    return comparison_text, heatmap_img, hist_img


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

def analyze_subregion(state, header, region_start, region_end):
    """
    Takes stored data from step 1 and a user-chosen region.
    Returns a subregion heatmap, histogram, and some stats (GC, average SHAP).
    """
    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"]

    # Validate bounds
    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)

    # Subsequence
    region_seq = seq[region_start:region_end]
    region_shap = shap_means[region_start:region_end]

    # Some stats
    gc_percent = compute_gc_content(region_seq)
    avg_shap = float(np.mean(region_shap))

    # Fraction pushing toward human vs. non-human
    positive_fraction = np.mean(region_shap > 0)
    negative_fraction = np.mean(region_shap < 0)

    # Simple logic-based interpretation
    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"
    )

    # Plot region as small heatmap
    heatmap_fig = plot_linear_heatmap(
        shap_means, 
        title="Subregion SHAP", 
        start=region_start, 
        end=region_end
    )
    heatmap_img = fig_to_image(heatmap_fig)

    # Plot histogram of SHAP in region
    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)


###############################################################################
# NEW SECTION: COMPARATIVE ANALYSIS FUNCTIONS
###############################################################################

def normalize_shap_lengths(shap1, shap2, num_points=1000):
    """
    Normalize two SHAP arrays to the same length using interpolation.
    Returns (normalized_shap1, normalized_shap2)
    """
    # Create x coordinates for both sequences
    x1 = np.linspace(0, 1, len(shap1))
    x2 = np.linspace(0, 1, len(shap2))
    
    # Create interpolation functions
    f1 = interp1d(x1, shap1, kind='linear')
    f2 = interp1d(x2, shap2, kind='linear')
    
    # Create new x coordinates for interpolation
    x_new = np.linspace(0, 1, num_points)
    
    # Interpolate both sequences to new length
    shap1_norm = f1(x_new)
    shap2_norm = f2(x_new)
    
    return shap1_norm, shap2_norm

def compute_shap_difference(shap1_norm, shap2_norm):
    """
    Compute the difference between two normalized SHAP arrays.
    Positive values indicate seq2 is more "human-like" than seq1.
    """
    return shap2_norm - shap1_norm

def plot_comparative_heatmap(shap_diff, title="SHAP Difference Heatmap"):
    """
    Plot the difference between two sequences' SHAP values.
    Red indicates seq2 is more human-like, blue indicates seq1 is more human-like.
    """
    # Build 2D array for imshow
    heatmap_data = shap_diff.reshape(1, -1)
    
    # Force symmetrical range
    extent = max(abs(np.min(shap_diff)), abs(np.max(shap_diff)))
    
    # Create figure with adjusted height ratio
    fig, ax = plt.subplots(figsize=(12, 1.8))
    
    # Create custom colormap
    custom_cmap = get_zero_centered_cmap()
    
    # Plot heatmap
    cax = ax.imshow(
        heatmap_data,
        aspect='auto',
        cmap=custom_cmap,
        vmin=-extent,
        vmax=+extent
    )
    
    # Configure colorbar
    cbar = plt.colorbar(
        cax,
        orientation='horizontal',
        pad=0.25,
        aspect=40,
        shrink=0.8
    )
    
    # Style the colorbar
    cbar.ax.tick_params(labelsize=8)
    cbar.set_label(
        'SHAP Difference (Seq2 - Seq1)',
        fontsize=9,
        labelpad=5
    )
    
    # Configure main plot
    ax.set_yticks([])
    ax.set_xlabel('Normalized Position (0-100%)', fontsize=10)
    ax.set_title(title, pad=10)
    
    plt.subplots_adjust(
        bottom=0.25,
        left=0.05,
        right=0.95
    )
    
    return fig

def analyze_sequence_comparison(file1, file2, fasta1="", fasta2=""):
    """
    Compare two sequences by analyzing their SHAP differences.
    Returns comparison text and visualizations.
    """
    # Process first sequence
    results1 = analyze_sequence(file1, fasta_text=fasta1)
    if isinstance(results1[0], str) and "Error" in results1[0]:
        return (f"Error in sequence 1: {results1[0]}", None, None)
    
    # Process second sequence
    results2 = analyze_sequence(file2, fasta_text=fasta2)
    if isinstance(results2[0], str) and "Error" in results2[0]:
        return (f"Error in sequence 2: {results2[0]}", None, None)
    
    # Get SHAP means from state dictionaries
    shap1 = results1[3]["shap_means"]
    shap2 = results2[3]["shap_means"]
    
    # Normalize lengths
    shap1_norm, shap2_norm = normalize_shap_lengths(shap1, shap2)
    
    # Compute difference (positive = seq2 more human-like)
    shap_diff = compute_shap_difference(shap1_norm, shap2_norm)
    
    # Calculate some 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)
    
    # Calculate what fraction of positions show substantial differences
    threshold = 0.05  # Arbitrary threshold for "substantial" difference
    substantial_diffs = np.abs(shap_diff) > threshold
    frac_different = np.mean(substantial_diffs)
    
    # Generate comparison text
    # Format the numbers without using f-string with `:,`
    len1_formatted = "{:,}".format(len(shap1))
    len2_formatted = "{:,}".format(len(shap2))
    frac_formatted = "{:.2%}".format(frac_different)
    
    comparison_text = (
        f"Sequence Comparison Results:\n"
        f"Sequence 1: {results1[4]}\n"
        f"Length: {len1_formatted} bases\n"
        f"Classification: {results1[0].split('Classification: ')[1].split('\\n')[0]}\n\n"
        f"Sequence 2: {results2[4]}\n"
        f"Length: {len2_formatted} bases\n"
        f"Classification: {results2[0].split('Classification: ')[1].split('\\n')[0]}\n\n"
        f"Comparison 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 of positions with substantial differences: {frac_formatted}\n\n"
        f"Interpretation:\n"
        f"Positive values (red) indicate regions where Sequence 2 is more 'human-like'\n"
        f"Negative values (blue) indicate regions where Sequence 1 is more 'human-like'"
    )
    
    # Create comparison heatmap
    heatmap_fig = plot_comparative_heatmap(shap_diff)
    heatmap_img = fig_to_image(heatmap_fig)
    
    # Create histogram of differences
    hist_fig = plot_shap_histogram(
        shap_diff,
        title="Distribution of SHAP Differences"
    )
    hist_img = fig_to_image(hist_fig)
    
    return comparison_text, heatmap_img, hist_img
    
###############################################################################
# 9. 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_sequence(...) returns 5 items
        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, and GC content.  
        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
    """)

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