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 
from matplotlib.colors import LinearSegmentedColormap
import io 
from io import BytesIO # Import io then BytesIO 
from PIL import Image, ImageDraw, ImageFont
from Bio.Graphics import GenomeDiagram
from Bio.SeqFeature import SeqFeature, FeatureLocation
from reportlab.lib import colors
import pandas as pd
import tempfile
import os
from typing import List, Dict, Tuple, Optional, Any
import seaborn as sns

###############################################################################
# 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", num_bins=30):
    fig, ax = plt.subplots(figsize=(6, 4))
    ax.hist(shap_array, bins=num_bins, 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, None)
    else:
        return ("Please provide a FASTA sequence.", None, None, None, None, None)

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

    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    try:
        # IMPORTANT: adjust how you load your model as needed
        state_dict = torch.load('model.pt', map_location=device)
        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, 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)

    # You might want to provide a CSV or other data for the 6th return item
    # Here, we'll simply return None for the file download:
    state_dict_out = {"seq": seq, "shap_means": shap_means}

    return (results_text, bar_img, heatmap_img, state_dict_out, header, None)

###############################################################################
# 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, 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, 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)
    
    # For demonstration, returning None for the file download as well
    return (region_info, heatmap_img, hist_img, None)

###############################################################################
# 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, title="SHAP Difference Heatmap"):
    """
    Plot heatmap using relative positions (0-100%)
    """
    heatmap_data = shap_diff.reshape(1, -1)
    extent = max(abs(np.min(shap_diff)), abs(np.max(shap_diff)))
    
    fig, ax = plt.subplots(figsize=(12, 1.8))
    cmap = get_zero_centered_cmap()
    cax = ax.imshow(heatmap_data, aspect='auto', cmap=cmap, vmin=-extent, vmax=extent)
    
    # Create percentage-based x-axis ticks
    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)
    
    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)
    
    ax.set_yticks([])
    ax.set_xlabel('Relative Position in Sequence', fontsize=10)
    ax.set_title(title, pad=10)
    plt.subplots_adjust(bottom=0.25, left=0.05, right=0.95)
    
    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, 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, 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 4 outputs (text, image, image, and a file or None for the last)
        return (comparison_text, heatmap_img, hist_img, None)
        
    except Exception as e:
        error_msg = f"Error during sequence comparison: {str(e)}"
        return (error_msg, None, None, None)

###############################################################################
# 11. GENE FEATURE ANALYSIS
###############################################################################

import io
from io import BytesIO
from PIL import Image, ImageDraw, ImageFont
import numpy as np
import pandas as pd
import tempfile
import os
from typing import List, Dict, Tuple, Optional, Any
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
import seaborn as sns

def parse_gene_features(text: str) -> List[Dict[str, Any]]:
    """Parse gene features from text file in FASTA-like format"""
    genes = []
    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:
                genes.append({
                    'header': current_header,
                    'sequence': ''.join(current_sequence),
                    'metadata': parse_gene_metadata(current_header)
                })
            current_header = line[1:]
            current_sequence = []
        else:
            current_sequence.append(line.upper())
            
    if current_header:
        genes.append({
            'header': current_header,
            'sequence': ''.join(current_sequence),
            'metadata': parse_gene_metadata(current_header)
        })
    
    return genes

def parse_gene_metadata(header: str) -> Dict[str, str]:
    """Extract metadata from gene header"""
    metadata = {}
    parts = header.split()
    
    for part in parts:
        if '[' in part and ']' in part:
            key_value = part[1:-1].split('=', 1)
            if len(key_value) == 2:
                metadata[key_value[0]] = key_value[1]
                
    return metadata

def parse_location(location_str: str) -> Tuple[Optional[int], Optional[int]]:
    """Parse gene location string, handling both forward and complement strands"""
    try:
        # Remove 'complement(' and ')' if present
        clean_loc = location_str.replace('complement(', '').replace(')', '')
        
        # Split on '..' and convert to integers
        if '..' in clean_loc:
            start, end = map(int, clean_loc.split('..'))
            return start, end
        else:
            return None, None
    except Exception as e:
        print(f"Error parsing location {location_str}: {str(e)}")
        return None, None

def compute_gene_statistics(gene_shap: np.ndarray) -> Dict[str, float]:
    """Compute statistical measures for gene SHAP values"""
    return {
        'avg_shap': float(np.mean(gene_shap)),
        'median_shap': float(np.median(gene_shap)),
        'std_shap': float(np.std(gene_shap)),
        'max_shap': float(np.max(gene_shap)),
        'min_shap': float(np.min(gene_shap)),
        'pos_fraction': float(np.mean(gene_shap > 0))
    }

def create_simple_genome_diagram(gene_results: List[Dict[str, Any]], genome_length: int) -> Image.Image:
    """
    Create a simple genome diagram using PIL, forcing a minimum color intensity
    so that small SHAP values don't appear white.
    """
    from PIL import Image, ImageDraw, ImageFont
    
    # Validate inputs
    if not gene_results or genome_length <= 0:
        img = Image.new('RGB', (800, 100), color='white')
        draw = ImageDraw.Draw(img)
        draw.text((10, 40), "Error: Invalid input data", fill='black')
        return img
        
    # Ensure all gene coordinates are valid integers
    for gene in gene_results:
        gene['start'] = max(0, int(gene['start']))
        gene['end'] = min(genome_length, int(gene['end']))
        if gene['start'] >= gene['end']:
            print(f"Warning: Invalid coordinates for gene {gene.get('gene_name','?')}: {gene['start']}-{gene['end']}")
    
    # Image dimensions
    width = 1500
    height = 600
    margin = 50
    track_height = 40
    
    # Create image with white background
    img = Image.new('RGB', (width, height), 'white')
    draw = ImageDraw.Draw(img)
    
    # Try to load font, fall back to default if unavailable
    try:
        font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 12)
        title_font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 16)
    except:
        font = ImageFont.load_default()
        title_font = ImageFont.load_default()
    
    # Draw title
    draw.text((margin, margin // 2), "Genome SHAP Analysis", fill='black', font=title_font or font)
    
    # Draw genome line
    line_y = height // 2
    draw.line([(int(margin), int(line_y)), (int(width - margin), int(line_y))], fill='black', width=2)
    
    # Calculate scale factor
    scale = float(width - 2 * margin) / float(genome_length)
    
    # Determine a reasonable step for scale markers
    num_ticks = 10
    if genome_length < num_ticks:
        step = 1
    else:
        step = genome_length // num_ticks
    
    # Draw scale markers
    for i in range(0, genome_length + 1, step):
        x_coord = margin + i * scale
        draw.line([
            (int(x_coord), int(line_y - 5)), 
            (int(x_coord), int(line_y + 5))
        ], fill='black', width=1)
        draw.text((int(x_coord - 20), int(line_y + 10)), f"{i:,}", fill='black', font=font)
    
    # Sort genes by absolute SHAP value for drawing
    sorted_genes = sorted(gene_results, key=lambda x: abs(x['avg_shap']))
    
    # Draw genes
    for idx, gene in enumerate(sorted_genes):
        # Calculate position and ensure integers
        start_x = margin + int(gene['start'] * scale)
        end_x   = margin + int(gene['end'] * scale)
        
        # Calculate color based on SHAP value
        avg_shap = gene['avg_shap']
        
        # Convert shap -> color intensity (0 to 255)
        # Then clamp to a minimum intensity so it never ends up plain white
        intensity = int(abs(avg_shap) * 500)
        intensity = max(50, min(255, intensity))  # clamp between 50 and 255

        if avg_shap > 0:
            # Red-ish for positive
            color = (255, 255 - intensity, 255 - intensity)
        else:
            # Blue-ish for negative or zero
            color = (255 - intensity, 255 - intensity, 255)
        
        # Draw gene rectangle
        draw.rectangle([
            (int(start_x), int(line_y - track_height // 2)),
            (int(end_x),   int(line_y + track_height // 2))
        ], fill=color, outline='black')
        
        # Prepare gene name label
        label = str(gene.get('gene_name','?'))
        
        # Fallback for label size
        label_mask = font.getmask(label)
        label_width, label_height = label_mask.size
        
        # Alternate label positions
        if idx % 2 == 0:
            text_y = line_y - track_height - 15
        else:
            text_y = line_y + track_height + 5
        
        # Decide whether to rotate text based on space
        gene_width = end_x - start_x
        if gene_width > label_width:
            text_x = start_x + (gene_width - label_width) // 2
            draw.text((int(text_x), int(text_y)), label, fill='black', font=font)
        elif gene_width > 20:
            txt_img = Image.new('RGBA', (label_width, label_height), (255, 255, 255, 0))
            txt_draw = ImageDraw.Draw(txt_img)
            txt_draw.text((0, 0), label, font=font, fill='black')
            rotated_img = txt_img.rotate(90, expand=True)
            img.paste(rotated_img, (int(start_x), int(text_y)), rotated_img)
    
    # Draw legend
    legend_x = margin
    legend_y = height - margin
    draw.text((int(legend_x), int(legend_y - 60)), "SHAP Values:", fill='black', font=font)
    
    # Draw legend boxes
    box_width = 20
    box_height = 20
    spacing = 15
    
    # Strong human-like
    draw.rectangle([
        (int(legend_x), int(legend_y - 45)), 
        (int(legend_x + box_width), int(legend_y - 45 + box_height))
    ], fill=(255, 0, 0), outline='black')
    draw.text((int(legend_x + box_width + spacing), int(legend_y - 45)), 
              "Strong human-like signal", fill='black', font=font)
    
    # Weak human-like
    draw.rectangle([
        (int(legend_x), int(legend_y - 20)), 
        (int(legend_x + box_width), int(legend_y - 20 + box_height))
    ], fill=(255, 200, 200), outline='black')
    draw.text((int(legend_x + box_width + spacing), int(legend_y - 20)), 
              "Weak human-like signal", fill='black', font=font)
    
    # Weak non-human-like
    draw.rectangle([
        (int(legend_x + 250), int(legend_y - 45)), 
        (int(legend_x + 250 + box_width), int(legend_y - 45 + box_height))
    ], fill=(200, 200, 255), outline='black')
    draw.text((int(legend_x + 250 + box_width + spacing), int(legend_y - 45)), 
              "Weak non-human-like signal", fill='black', font=font)
    
    # Strong non-human-like
    draw.rectangle([
        (int(legend_x + 250), int(legend_y - 20)), 
        (int(legend_x + 250 + box_width), int(legend_y - 20 + box_height))
    ], fill=(0, 0, 255), outline='black')
    draw.text((int(legend_x + 250 + box_width + spacing), int(legend_y - 20)), 
              "Strong non-human-like signal", fill='black', font=font)
    
    return img

def analyze_gene_features(sequence_file: str, 
                          features_file: str, 
                          fasta_text: str = "", 
                          features_text: str = "") -> Tuple[str, Optional[str], Optional[Image.Image]]:
    """Analyze SHAP values for each gene feature"""
    # First analyze whole sequence
    sequence_results = analyze_sequence(sequence_file, top_kmers=10, fasta_text=fasta_text)
    if isinstance(sequence_results[0], str) and "Error" in sequence_results[0]:
        return f"Error in sequence analysis: {sequence_results[0]}", None, None
        
    # Get SHAP values
    shap_means = sequence_results[3]["shap_means"]
    
    # Parse gene features
    try:
        if features_text.strip():
            genes = parse_gene_features(features_text)
        else:
            with open(features_file, 'r') as f:
                genes = parse_gene_features(f.read())
    except Exception as e:
        return f"Error reading features file: {str(e)}", None, None
            
    # Analyze each gene
    gene_results = []
    for gene in genes:
        try:
            location = gene['metadata'].get('location', '')
            if not location:
                continue
                
            start, end = parse_location(location)
            if start is None or end is None:
                continue
                
            # Get SHAP values for this region
            gene_shap = shap_means[start:end]
            stats = compute_gene_statistics(gene_shap)
            
            gene_results.append({
                'gene_name': gene['metadata'].get('gene', 'Unknown'),
                'location': location,
                'start': start,
                'end': end,
                'locus_tag': gene['metadata'].get('locus_tag', ''),
                'avg_shap': stats['avg_shap'],
                'median_shap': stats['median_shap'],
                'std_shap': stats['std_shap'],
                'max_shap': stats['max_shap'],
                'min_shap': stats['min_shap'],
                'pos_fraction': stats['pos_fraction'],
                'classification': 'Human' if stats['avg_shap'] > 0 else 'Non-human',
                'confidence': abs(stats['avg_shap'])
            })
            
        except Exception as e:
            print(f"Error processing gene {gene['metadata'].get('gene', 'Unknown')}: {str(e)}")
            continue
    
    if not gene_results:
        return "No valid genes could be processed", None, None
            
    # Sort genes by absolute SHAP value
    sorted_genes = sorted(gene_results, key=lambda x: abs(x['avg_shap']), reverse=True)
    
    # Create results text
    results_text = "Gene Analysis Results:\n\n"
    results_text += f"Total genes analyzed: {len(gene_results)}\n"
    results_text += f"Human-like genes: {sum(1 for g in gene_results if g['classification'] == 'Human')}\n"
    results_text += f"Non-human-like genes: {sum(1 for g in gene_results if g['classification'] == 'Non-human')}\n\n"
    
    results_text += "Top 10 most distinctive genes:\n"
    for gene in sorted_genes[:10]:
        results_text += (
            f"Gene: {gene['gene_name']}\n"
            f"Location: {gene['location']}\n"
            f"Classification: {gene['classification']} "
            f"(confidence: {gene['confidence']:.4f})\n"
            f"Average SHAP: {gene['avg_shap']:.4f}\n\n"
        )
    
    # Create CSV content
    csv_content = "gene_name,location,avg_shap,median_shap,std_shap,max_shap,min_shap,"
    csv_content += "pos_fraction,classification,confidence,locus_tag\n"
    
    for gene in gene_results:
        csv_content += (
            f"{gene['gene_name']},{gene['location']},{gene['avg_shap']:.4f},"
            f"{gene['median_shap']:.4f},{gene['std_shap']:.4f},{gene['max_shap']:.4f},"
            f"{gene['min_shap']:.4f},{gene['pos_fraction']:.4f},{gene['classification']},"
            f"{gene['confidence']:.4f},{gene['locus_tag']}\n"
        )
    
    # Save CSV to temp file
    try:
        temp_dir = tempfile.gettempdir()
        temp_path = os.path.join(temp_dir, f"gene_analysis_{os.urandom(4).hex()}.csv")
        
        with open(temp_path, 'w') as f:
            f.write(csv_content)
    except Exception as e:
        print(f"Error saving CSV: {str(e)}")
        temp_path = None
    
    # Create visualization
    try:
        diagram_img = create_simple_genome_diagram(gene_results, len(shap_means))
    except Exception as e:
        print(f"Error creating visualization: {str(e)}")
        # Create error image
        diagram_img = Image.new('RGB', (800, 100), color='white')
        draw = ImageDraw.Draw(diagram_img)
        draw.text((10, 40), f"Error creating visualization: {str(e)}", fill='black')
    
    return results_text, temp_path, diagram_img

###############################################################################
# 12. DOWNLOAD FUNCTIONS
###############################################################################

def prepare_csv_download(data, filename="analysis_results.csv"):
    """Prepare CSV data for download"""
    if isinstance(data, str):
        return data.encode(), filename
    elif isinstance(data, (list, dict)):
        import csv
        from io import StringIO
        
        output = StringIO()
        writer = csv.DictWriter(output, fieldnames=data[0].keys())
        writer.writeheader()
        writer.writerows(data)
        return output.getvalue().encode(), filename
    else:
        raise ValueError("Unsupported data type for CSV download")
        
###############################################################################
# 13. BUILD GRADIO INTERFACE
###############################################################################

css = """
.gradio-container {
    font-family: 'IBM Plex Sans', sans-serif;
}
.download-button {
    margin-top: 10px;
}
"""

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.  
    **Step 3**: Analyze gene features and their contributions.  
    **Step 4**: Compare sequences and analyze differences.
    
    **Color Scale**: Negative SHAP = Blue, Zero = White, Positive SHAP = 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)")
                download_results = gr.File(label="Download Results", visible=False, elem_classes="download-button")
        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, download_results]
        )

    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 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)")
        download_subregion = gr.File(label="Download Subregion Analysis", visible=False, elem_classes="download-button")
        
        region_btn.click(
            analyze_subregion,
            inputs=[seq_state, header_state, region_start, region_end],
            outputs=[subregion_info, subregion_img, subregion_hist_img, download_subregion]
        )

    with gr.Tab("3) Gene Features Analysis"):
        gr.Markdown("""
        **Analyze Gene Features**  
        Upload a FASTA file and corresponding gene features file to analyze SHAP values per gene.
        Gene features should be in the format:
        ```
        >gene_name [gene=X] [locus_tag=Y] [location=start..end] or [location=complement(start..end)]
        SEQUENCE
        ```
        The genome viewer will show genes color-coded by their contribution:
        - Red: Genes pushing toward human origin
        - Blue: Genes pushing toward non-human origin
        - Color intensity indicates strength of signal
        """)
        with gr.Row():
            with gr.Column(scale=1):
                gene_fasta_file = gr.File(label="Upload FASTA file", file_types=[".fasta", ".fa", ".txt"], type="filepath")
                gene_fasta_text = gr.Textbox(label="Or paste FASTA sequence", placeholder=">sequence_name\nACGTACGT...", lines=5)
            with gr.Column(scale=1):
                features_file = gr.File(label="Upload gene features file", file_types=[".txt"], type="filepath")
                features_text = gr.Textbox(label="Or paste gene features", placeholder=">gene_1 [gene=U12]...\nACGT...", lines=5)
        
        analyze_genes_btn = gr.Button("Analyze Gene Features", variant="primary")
        gene_results = gr.Textbox(label="Gene Analysis Results", lines=12, interactive=False)
        gene_diagram = gr.Image(label="Genome Diagram with Gene Features")
        download_gene_results = gr.File(label="Download Gene Analysis (CSV)", visible=True)
        
        analyze_genes_btn.click(
            analyze_gene_features,
            inputs=[gene_fasta_file, features_file, gene_fasta_text, features_text],
            outputs=[gene_results, download_gene_results, gene_diagram]
        )
    
    with gr.Tab("4) 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 more human-like  
        - Blue: Sequence 1 more human-like  
        - 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")
        download_comparison = gr.File(label="Download Comparison Results", visible=False, elem_classes="download-button")

        compare_btn.click(
            analyze_sequence_comparison,
            inputs=[file_input1, file_input2, text_input1, text_input2],
            outputs=[comparison_text, diff_heatmap, diff_hist, download_comparison]
        )
    
    gr.Markdown("""
    ### Interface Features
    - **Overall Classification** (human vs non-human) using k-mer frequencies
    - **SHAP Analysis** shows which k-mers push classification toward or away from human
    - **White-Centered SHAP Gradient**: 
      - Negative (blue), 0 (white), Positive (red)
      - Symmetrical color range around 0
    - **Identify Subregions** with strongest push for human or non-human
    - **Gene Feature Analysis**:
      - Analyze individual genes' contributions
      - Interactive genome viewer
      - Gene-level statistics and classification
    - **Sequence Comparison**:
      - Compare two sequences to identify regions of difference
      - Normalized comparison to handle different lengths
      - Statistical summary of differences
    - **Data Export**:
      - Download results as CSV files
      - Save analysis outputs for further processing
    """)

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