"""
VERIDEX — Visualization Engine
================================
Generates heatmaps, module score graphs, ELA overlays,
and forensic annotation images for the frontend.
All outputs are returned as base64-encoded PNG strings.
"""
import io
import base64
import logging
import numpy as np
from PIL import Image, ImageDraw, ImageFont

logger = logging.getLogger(__name__)


def _to_b64(img: Image.Image, fmt: str = "PNG") -> str:
    """Convert PIL image to base64 data-URI string."""
    buf = io.BytesIO()
    img.save(buf, format=fmt, optimize=True)
    return base64.b64encode(buf.getvalue()).decode()


def _font(size: int = 12):
    """Load a font, falling back to default if not available."""
    try:
        return ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", size)
    except Exception:
        return ImageFont.load_default()


# ── ELA Heatmap ──────────────────────────────────────────────────

def ela_heatmap(content: bytes, quality: int = 75) -> str | None:
    """
    Error Level Analysis heatmap.
    Re-saves the image at `quality` and amplifies the difference.

    Returns base64 PNG string, or None on failure.
    """
    try:
        original = Image.open(io.BytesIO(content)).convert("RGB")
        arr_orig = np.array(original, dtype=np.float32)

        buf = io.BytesIO()
        original.save(buf, format="JPEG", quality=quality)
        reloaded = np.array(Image.open(buf).convert("RGB"), dtype=np.float32)

        ela  = np.abs(arr_orig - reloaded)
        ela  = (ela * 10.0).clip(0, 255).astype(np.uint8)   # amplify 10×

        # Apply a red-yellow-white colourmap
        heat = np.zeros((*ela.shape[:2], 3), dtype=np.uint8)
        mag  = ela.mean(axis=2)
        heat[:, :, 0] = np.clip(mag * 3,   0, 255).astype(np.uint8)   # R
        heat[:, :, 1] = np.clip(mag * 1.5, 0, 255).astype(np.uint8)   # G
        heat[:, :, 2] = np.zeros_like(mag, dtype=np.uint8)             # B

        out = Image.fromarray(heat)
        out = out.resize(original.size, Image.LANCZOS)
        return _to_b64(out)
    except Exception as e:
        logger.warning(f"[Viz] ela_heatmap failed: {e}")
        return None


# ── Noise Map ────────────────────────────────────────────────────

def noise_heatmap(content: bytes) -> str | None:
    """
    Laplacian noise variance heatmap.
    Highlights regions with inconsistent noise (splicing indicator).
    """
    try:
        from scipy.ndimage import laplace, uniform_filter

        img  = Image.open(io.BytesIO(content)).convert("RGB")
        arr  = np.array(img, dtype=np.float32)
        gray = arr.mean(axis=2)
        lap  = np.abs(laplace(gray))

        # Tile-wise variance
        tile = 16
        var_map = np.zeros_like(gray)
        for y in range(0, gray.shape[0], tile):
            for x in range(0, gray.shape[1], tile):
                block = lap[y:y + tile, x:x + tile]
                var_map[y:y + tile, x:x + tile] = np.var(block)

        # Normalise to [0, 255]
        vm = var_map
        vm = ((vm - vm.min()) / (vm.max() - vm.min() + 1e-9) * 255).astype(np.uint8)

        # Blue-cyan colourmap
        heat = np.zeros((*vm.shape, 3), dtype=np.uint8)
        heat[:, :, 0] = np.zeros_like(vm)
        heat[:, :, 1] = (vm * 0.8).astype(np.uint8)
        heat[:, :, 2] = vm

        out = Image.fromarray(heat).resize(img.size, Image.NEAREST)
        return _to_b64(out)
    except Exception as e:
        logger.warning(f"[Viz] noise_heatmap failed: {e}")
        return None


# ── Module Score Radar / Bar Chart ───────────────────────────────

def module_score_chart(scores: dict[int | str, float], width: int = 900, height: int = 420) -> str | None:
    """
    Horizontal bar chart of all module scores.
    Green = authentic (≥0.65), Red = suspicious (≤0.35), Grey = neutral.

    Returns base64 PNG.
    """
    try:
        from utils.module_runner import MODULE_NAMES

        sorted_items = sorted(scores.items(), key=lambda x: int(x[0]))
        n = len(sorted_items)
        if n == 0:
            return None

        bar_h   = max(8, min(14, height // n))
        h_actual = bar_h * n + 60
        img     = Image.new("RGB", (width, h_actual), (18, 18, 30))
        draw    = ImageDraw.Draw(img)
        font_sm = _font(9)
        font_ti = _font(11)

        draw.text((10, 8), "VERIDEX — Module Score Overview", fill=(200, 200, 220), font=font_ti)
        draw.text((10, 22), f"{n} modules analysed", fill=(120, 120, 140), font=font_sm)

        label_w = 220
        bar_max = width - label_w - 80

        for i, (mid, score) in enumerate(sorted_items):
            y     = 45 + i * bar_h
            sc    = float(score)
            mid_i = int(mid)
            name  = MODULE_NAMES.get(mid_i, f"Module {mid_i}")[:35]

            # Colour
            if sc >= 0.65:
                colour = (40, 200, 100)
            elif sc <= 0.35:
                colour = (220, 60, 60)
            else:
                colour = (120, 120, 160)

            bar_w = int(sc * bar_max)
            draw.rectangle([label_w, y, label_w + bar_w, y + bar_h - 2], fill=colour)

            draw.text((4, y + 1),
                      f"{mid_i:>2}. {name}", fill=(180, 180, 200), font=font_sm)
            draw.text((label_w + bar_w + 4, y + 1),
                      f"{sc:.2f}", fill=colour, font=font_sm)

        return _to_b64(img)
    except Exception as e:
        logger.warning(f"[Viz] module_score_chart failed: {e}")
        return None


# ── Verdict Summary Card ─────────────────────────────────────────

def verdict_card(
    verdict: str,
    confidence: float,
    risk_score: float,
    case_id: str,
    filename: str,
    width: int = 600,
    height: int = 200,
) -> str | None:
    """
    Compact verdict summary image — embeddable in PDF or frontend.
    """
    try:
        COLOURS = {
            "AUTHENTIC":         (30, 200, 90),
            "LIKELY AUTHENTIC":  (80, 180, 80),
            "INCONCLUSIVE":      (160, 160, 60),
            "LIKELY SYNTHETIC":  (220, 120, 30),
            "SYNTHETIC":         (220, 40, 40),
        }
        col    = COLOURS.get(verdict.upper(), (120, 120, 120))
        bg     = (18, 18, 30)
        img    = Image.new("RGB", (width, height), bg)
        draw   = ImageDraw.Draw(img)

        # Left colour bar
        draw.rectangle([0, 0, 8, height], fill=col)

        font_lg = _font(28)
        font_md = _font(14)
        font_sm = _font(10)

        draw.text((20, 15),  "VERIDEX VERDICT",     fill=(140, 140, 180), font=font_sm)
        draw.text((20, 32),  verdict,                fill=col,             font=font_lg)
        draw.text((20, 80),  f"Confidence : {confidence:.1%}",  fill=(200, 200, 210), font=font_md)
        draw.text((20, 102), f"Risk Score : {risk_score:.1f}/100", fill=(200, 200, 210), font=font_md)
        draw.text((20, 130), f"Case  : {case_id}",  fill=(120, 120, 150), font=font_sm)
        draw.text((20, 145), f"File  : {filename[:50]}", fill=(120, 120, 150), font=font_sm)

        # Risk bar
        bar_x, bar_y, bar_w, bar_h2 = 20, 168, width - 40, 12
        draw.rectangle([bar_x, bar_y, bar_x + bar_w, bar_y + bar_h2], fill=(40, 40, 55))
        risk_fill = int(risk_score / 100 * bar_w)
        draw.rectangle([bar_x, bar_y, bar_x + risk_fill, bar_y + bar_h2], fill=col)
        draw.text((bar_x, bar_y - 12), "Risk Level", fill=(100, 100, 130), font=font_sm)

        return _to_b64(img)
    except Exception as e:
        logger.warning(f"[Viz] verdict_card failed: {e}")
        return None


# ── Overlay: annotate image ──────────────────────────────────────

def annotate_image(content: bytes, findings: list[str], verdict: str) -> str | None:
    """
    Returns the original image with a forensic annotation overlay
    (verdict badge + key findings text strip).
    """
    try:
        COLOURS = {
            "AUTHENTIC":        (30, 200, 90),
            "LIKELY AUTHENTIC": (80, 180, 80),
            "INCONCLUSIVE":     (200, 180, 40),
            "LIKELY SYNTHETIC": (220, 120, 30),
            "SYNTHETIC":        (220, 40, 40),
        }
        col = COLOURS.get(verdict.upper(), (160, 160, 160))

        img  = Image.open(io.BytesIO(content)).convert("RGB")
        w, h = img.size

        strip_h = 28 + len(findings[:4]) * 16
        out     = Image.new("RGB", (w, h + strip_h), (18, 18, 30))
        out.paste(img, (0, 0))

        draw    = ImageDraw.Draw(out)
        font_vd = _font(13)
        font_sm = _font(10)

        # Verdict bar
        draw.rectangle([0, h, w, h + 24], fill=(28, 28, 45))
        draw.rectangle([0, h, 8, h + 24], fill=col)
        draw.text((14, h + 5), f"VERIDEX: {verdict}", fill=col, font=font_vd)

        # Findings
        for i, finding in enumerate(findings[:4]):
            fy = h + 28 + i * 16
            draw.text((10, fy), f"• {finding[:90]}", fill=(180, 180, 200), font=font_sm)

        return _to_b64(out)
    except Exception as e:
        logger.warning(f"[Viz] annotate_image failed: {e}")
        return None


# ── Public convenience function ──────────────────────────────────

def generate_all_visuals(
    content: bytes,
    content_type: str,
    scores: dict,
    verdict: str,
    confidence: float,
    risk_score: float,
    case_id: str,
    filename: str,
    findings: list[str],
) -> dict[str, str | None]:
    """
    Generate all visuals for a VERIDEX analysis result.

    Returns a dict of { visual_name: base64_png | None }.
    Only runs for image content types.
    """
    visuals: dict[str, str | None] = {}

    if content_type.startswith("image/"):
        visuals["ela_heatmap"]    = ela_heatmap(content)
        visuals["noise_heatmap"]  = noise_heatmap(content)
        visuals["annotated"]      = annotate_image(content, findings, verdict)

    visuals["score_chart"]  = module_score_chart(scores)
    visuals["verdict_card"] = verdict_card(
        verdict, confidence, risk_score, case_id, filename
    )

    generated = sum(1 for v in visuals.values() if v is not None)
    logger.info(f"[Viz] Generated {generated}/{len(visuals)} visuals for {case_id}")
    return visuals