"""
Visualization Module for CropDoctor-Semantic
=============================================

This module provides visualization functions for:
- Segmentation masks overlay
- Severity heatmaps
- Diagnostic dashboards
- Comparison views
"""

import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.colors import LinearSegmentedColormap
from typing import List, Optional, Tuple, Union, Dict
from pathlib import Path
import logging

logger = logging.getLogger(__name__)

# Color schemes
SEVERITY_COLORS = {
    'healthy': '#2ECC71',    # Green
    'mild': '#F1C40F',       # Yellow
    'moderate': '#E67E22',   # Orange
    'severe': '#E74C3C'      # Red
}

SYMPTOM_COLORS = [
    '#E74C3C',  # Red
    '#9B59B6',  # Purple
    '#3498DB',  # Blue
    '#E67E22',  # Orange
    '#1ABC9C',  # Teal
    '#F39C12',  # Yellow
    '#D35400',  # Dark Orange
    '#8E44AD',  # Dark Purple
]


def create_diagnostic_visualization(
    image: Union[str, Path, Image.Image, np.ndarray],
    masks: Optional[np.ndarray] = None,
    severity_label: str = "unknown",
    disease_name: str = "Unknown",
    affected_percent: float = 0.0,
    prompt_labels: Optional[List[str]] = None,
    figsize: Tuple[int, int] = (16, 6)
) -> plt.Figure:
    """
    Create a comprehensive diagnostic visualization.
    
    Args:
        image: Input image
        masks: Segmentation masks array (N, H, W)
        severity_label: Severity classification result
        disease_name: Identified disease name
        affected_percent: Percentage of affected area
        prompt_labels: Labels for each mask
        figsize: Figure size
        
    Returns:
        matplotlib Figure object
    """
    # Load image
    if isinstance(image, (str, Path)):
        image = Image.open(image).convert("RGB")
    elif isinstance(image, np.ndarray):
        image = Image.fromarray(image)
        
    img_array = np.array(image)
    
    # Create figure with subplots
    fig, axes = plt.subplots(1, 3, figsize=figsize)
    fig.suptitle(f'CropDoctor Diagnostic Report', fontsize=14, fontweight='bold')
    
    # Panel 1: Original Image
    axes[0].imshow(img_array)
    axes[0].set_title('Original Image', fontsize=12)
    axes[0].axis('off')
    
    # Panel 2: Segmentation Overlay
    if masks is not None and len(masks) > 0:
        overlay = create_mask_overlay(img_array, masks, alpha=0.5)
        axes[1].imshow(overlay)
        
        # Create legend
        if prompt_labels:
            patches = []
            for i, label in enumerate(prompt_labels[:len(SYMPTOM_COLORS)]):
                color = SYMPTOM_COLORS[i % len(SYMPTOM_COLORS)]
                patches.append(mpatches.Patch(color=color, label=label, alpha=0.7))
            axes[1].legend(handles=patches, loc='upper right', fontsize=8)
    else:
        axes[1].imshow(img_array)
        axes[1].text(0.5, 0.5, 'No disease regions detected',
                    transform=axes[1].transAxes, ha='center', va='center',
                    fontsize=12, color='green')
        
    axes[1].set_title('Disease Detection', fontsize=12)
    axes[1].axis('off')
    
    # Panel 3: Diagnostic Summary
    axes[2].axis('off')
    
    # Create summary text
    severity_color = SEVERITY_COLORS.get(severity_label.lower(), '#95A5A6')
    
    summary_text = f"""
    ╔════════════════════════════════╗
    ║     DIAGNOSTIC SUMMARY         ║
    ╚════════════════════════════════╝
    
    📋 Disease: {disease_name}
    
    ⚠️ Severity: {severity_label.upper()}
    
    📊 Affected Area: {affected_percent:.1f}%
    
    """
    
    # Add severity indicator
    axes[2].text(0.5, 0.65, summary_text, transform=axes[2].transAxes,
                fontsize=11, fontfamily='monospace',
                verticalalignment='top', horizontalalignment='center')
    
    # Add severity color bar
    severity_bar = plt.Rectangle((0.15, 0.25), 0.7, 0.1, 
                                  facecolor=severity_color, 
                                  edgecolor='black',
                                  transform=axes[2].transAxes)
    axes[2].add_patch(severity_bar)
    axes[2].text(0.5, 0.30, severity_label.upper(),
                transform=axes[2].transAxes, ha='center', va='center',
                fontsize=12, fontweight='bold', color='white')
    
    # Add affected area progress bar
    bar_width = 0.7 * (affected_percent / 100)
    bg_bar = plt.Rectangle((0.15, 0.12), 0.7, 0.06,
                           facecolor='#EEEEEE', edgecolor='black',
                           transform=axes[2].transAxes)
    progress_bar = plt.Rectangle((0.15, 0.12), max(0.01, bar_width), 0.06,
                                 facecolor='#E74C3C',
                                 transform=axes[2].transAxes)
    axes[2].add_patch(bg_bar)
    axes[2].add_patch(progress_bar)
    axes[2].text(0.5, 0.08, f'Affected Area: {affected_percent:.1f}%',
                transform=axes[2].transAxes, ha='center', fontsize=10)
    
    plt.tight_layout()
    
    return fig


def create_mask_overlay(
    image: np.ndarray,
    masks: np.ndarray,
    alpha: float = 0.5,
    colors: Optional[List[str]] = None
) -> np.ndarray:
    """
    Create an overlay of segmentation masks on an image.
    
    Args:
        image: RGB image array (H, W, 3)
        masks: Binary masks (N, H, W)
        alpha: Transparency of overlay
        colors: Optional list of colors for masks
        
    Returns:
        Image array with mask overlay
    """
    if colors is None:
        colors = SYMPTOM_COLORS
        
    # Start with the original image
    overlay = image.copy().astype(np.float32)
    
    for i, mask in enumerate(masks):
        if mask.any():
            # Get color for this mask
            color_hex = colors[i % len(colors)]
            color_rgb = hex_to_rgb(color_hex)
            
            # Create colored mask
            colored_mask = np.zeros_like(overlay)
            colored_mask[mask] = color_rgb
            
            # Blend with overlay
            mask_3d = np.stack([mask] * 3, axis=-1)
            overlay = np.where(
                mask_3d,
                overlay * (1 - alpha) + colored_mask * alpha,
                overlay
            )
            
    return overlay.astype(np.uint8)


def create_severity_heatmap(
    image: Union[str, Path, Image.Image, np.ndarray],
    severity_map: np.ndarray,
    figsize: Tuple[int, int] = (12, 5)
) -> plt.Figure:
    """
    Create a heatmap showing severity distribution across the image.
    
    Args:
        image: Input image
        severity_map: Array of severity values (H, W) with values 0-3
        figsize: Figure size
        
    Returns:
        matplotlib Figure object
    """
    # Load image
    if isinstance(image, (str, Path)):
        image = Image.open(image).convert("RGB")
    elif isinstance(image, np.ndarray):
        image = Image.fromarray(image)
        
    img_array = np.array(image)
    
    # Create custom colormap
    colors = ['#2ECC71', '#F1C40F', '#E67E22', '#E74C3C']  # Green to Red
    cmap = LinearSegmentedColormap.from_list('severity', colors, N=256)
    
    fig, axes = plt.subplots(1, 2, figsize=figsize)
    
    # Original image
    axes[0].imshow(img_array)
    axes[0].set_title('Original Image')
    axes[0].axis('off')
    
    # Heatmap overlay
    axes[1].imshow(img_array)
    heatmap = axes[1].imshow(severity_map, cmap=cmap, alpha=0.6, vmin=0, vmax=3)
    axes[1].set_title('Severity Heatmap')
    axes[1].axis('off')
    
    # Add colorbar
    cbar = plt.colorbar(heatmap, ax=axes[1], fraction=0.046, pad=0.04)
    cbar.set_ticks([0, 1, 2, 3])
    cbar.set_ticklabels(['Healthy', 'Mild', 'Moderate', 'Severe'])
    
    plt.tight_layout()
    
    return fig


def create_comparison_view(
    images: List[Union[str, Path, Image.Image]],
    results: List[Dict],
    cols: int = 4,
    figsize_per_image: Tuple[float, float] = (4, 5)
) -> plt.Figure:
    """
    Create a grid comparison view of multiple diagnoses.
    
    Args:
        images: List of images
        results: List of diagnostic results (dicts with 'severity_label', 'disease_name', etc.)
        cols: Number of columns in grid
        figsize_per_image: Size per image in the grid
        
    Returns:
        matplotlib Figure object
    """
    n_images = len(images)
    rows = (n_images + cols - 1) // cols
    
    fig, axes = plt.subplots(
        rows, cols,
        figsize=(figsize_per_image[0] * cols, figsize_per_image[1] * rows)
    )
    
    if rows == 1:
        axes = [axes]
    if cols == 1:
        axes = [[ax] for ax in axes]
        
    for i, (img, result) in enumerate(zip(images, results)):
        row = i // cols
        col = i % cols
        ax = axes[row][col] if rows > 1 else axes[col]
        
        # Load image
        if isinstance(img, (str, Path)):
            img = Image.open(img).convert("RGB")
            
        ax.imshow(img)
        ax.axis('off')
        
        # Add colored border based on severity
        severity = result.get('severity_label', 'unknown')
        color = SEVERITY_COLORS.get(severity.lower(), '#95A5A6')
        
        for spine in ax.spines.values():
            spine.set_edgecolor(color)
            spine.set_linewidth(4)
            spine.set_visible(True)
            
        # Add label
        ax.set_title(
            f"{result.get('disease_name', 'Unknown')}\n{severity.upper()}",
            fontsize=10,
            color=color
        )
        
    # Hide empty subplots
    for i in range(n_images, rows * cols):
        row = i // cols
        col = i % cols
        ax = axes[row][col] if rows > 1 else axes[col]
        ax.axis('off')
        ax.set_visible(False)
        
    plt.tight_layout()
    
    return fig


def create_treatment_card(
    result: Dict,
    figsize: Tuple[int, int] = (8, 10)
) -> plt.Figure:
    """
    Create a treatment recommendation card.
    
    Args:
        result: Diagnostic result dictionary
        figsize: Figure size
        
    Returns:
        matplotlib Figure object
    """
    fig, ax = plt.subplots(figsize=figsize)
    ax.axis('off')
    
    severity_color = SEVERITY_COLORS.get(
        result.get('severity_label', 'unknown').lower(),
        '#95A5A6'
    )
    
    # Title
    ax.text(0.5, 0.95, '🌿 TREATMENT CARD',
           ha='center', va='top', fontsize=16, fontweight='bold',
           transform=ax.transAxes)
    
    # Disease info
    disease_text = f"""
╔═══════════════════════════════════════════╗
║ Disease: {result.get('disease_name', 'Unknown'):<32}║
║ Type: {result.get('disease_type', 'unknown'):<35}║
║ Severity: {result.get('severity_label', 'unknown').upper():<31}║
║ Affected Area: {result.get('affected_area_percent', 0):.1f}%{' ' * 25}║
╚═══════════════════════════════════════════╝
"""
    ax.text(0.5, 0.85, disease_text,
           ha='center', va='top', fontfamily='monospace', fontsize=10,
           transform=ax.transAxes)
    
    # Treatments
    y_pos = 0.60
    
    # Organic treatments
    ax.text(0.1, y_pos, '🌱 ORGANIC TREATMENTS', fontweight='bold', fontsize=11,
           transform=ax.transAxes)
    y_pos -= 0.03
    
    for treatment in result.get('organic_treatments', [])[:4]:
        ax.text(0.12, y_pos, f'• {treatment[:50]}', fontsize=9,
               transform=ax.transAxes)
        y_pos -= 0.03
        
    y_pos -= 0.02
    
    # Chemical treatments
    if result.get('chemical_treatments'):
        ax.text(0.1, y_pos, '🧪 CHEMICAL TREATMENTS', fontweight='bold', fontsize=11,
               transform=ax.transAxes)
        y_pos -= 0.03
        
        for treatment in result.get('chemical_treatments', [])[:3]:
            ax.text(0.12, y_pos, f'• {treatment[:50]}', fontsize=9,
                   transform=ax.transAxes)
            y_pos -= 0.03
            
    y_pos -= 0.02
    
    # Prevention
    ax.text(0.1, y_pos, '🛡️ PREVENTION', fontweight='bold', fontsize=11,
           transform=ax.transAxes)
    y_pos -= 0.03
    
    for measure in result.get('preventive_measures', [])[:4]:
        ax.text(0.12, y_pos, f'• {measure[:50]}', fontsize=9,
               transform=ax.transAxes)
        y_pos -= 0.03
        
    # Timing
    y_pos -= 0.02
    ax.text(0.1, y_pos, f"⏰ TIMING: {result.get('treatment_timing', 'Consult expert')[:60]}",
           fontsize=9, transform=ax.transAxes)
    
    # Add border
    rect = plt.Rectangle((0.05, 0.05), 0.9, 0.92,
                         fill=False, edgecolor=severity_color, linewidth=3,
                         transform=ax.transAxes)
    ax.add_patch(rect)
    
    return fig


def hex_to_rgb(hex_color: str) -> List[int]:
    """Convert hex color to RGB."""
    hex_color = hex_color.lstrip('#')
    return [int(hex_color[i:i+2], 16) for i in (0, 2, 4)]


def save_visualization(
    fig: plt.Figure,
    output_path: Union[str, Path],
    dpi: int = 150
):
    """Save figure to file."""
    output_path = Path(output_path)
    output_path.parent.mkdir(parents=True, exist_ok=True)
    fig.savefig(output_path, dpi=dpi, bbox_inches='tight', facecolor='white')
    plt.close(fig)
    logger.info(f"Visualization saved to {output_path}")


if __name__ == "__main__":
    # Test visualizations
    import numpy as np
    
    # Create test image
    test_img = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
    test_img[:, :, 1] = 139  # Greenish tint
    
    # Create test masks
    test_masks = np.zeros((2, 480, 640), dtype=bool)
    test_masks[0, 100:200, 100:200] = True  # Square mask
    test_masks[1, 300:400, 400:500] = True  # Another square
    
    # Test diagnostic visualization
    fig = create_diagnostic_visualization(
        test_img,
        test_masks,
        severity_label="moderate",
        disease_name="Leaf Spot Disease",
        affected_percent=15.5,
        prompt_labels=["brown spots", "yellowing"]
    )
    
    save_visualization(fig, "/tmp/test_diagnostic.png")
    print("Test visualization saved to /tmp/test_diagnostic.png")