Upload app.py

app.py

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+import cv2
+import numpy as np
+import gradio as gr
+from PIL import Image
+import tensorflow as tf
+import keras
+from huggingface_hub import snapshot_download
+import pytesseract
+import io
+import math
+import os
+import re
+import tempfile
+from collections import defaultdict
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+from reportlab.lib.pagesizes import letter
+from reportlab.lib.units import inch
+from reportlab.lib.styles import getSampleStyleSheet
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image as RLImage
+
+_msi_model = None
+LOGO_PATH = "logo.png"
+
+
+def add_watermark(pil_img, logo_path=LOGO_PATH, scale=0.20, opacity=0.95):
+    if not os.path.exists(logo_path):
+        return pil_img
+    try:
+        base = pil_img.convert("RGBA")
+        logo = Image.open(logo_path).convert("RGBA")
+        bbox = logo.getbbox()
+        if bbox:
+            logo = logo.crop(bbox)
+        img_w, img_h = base.size
+        logo_w = max(50, int(img_w * scale))
+        logo_ratio = logo.height / logo.width
+        logo_h = int(logo_w * logo_ratio)
+        logo_resized = logo.resize((logo_w, logo_h), Image.LANCZOS)
+
+        r, g, b, a = logo_resized.split()
+        a = a.point(lambda p: int(p * opacity))
+        logo_resized = Image.merge("RGBA", (r, g, b, a))
+
+        margin = max(10, int(img_w * 0.015))
+        pos = (img_w - logo_w - margin, img_h - logo_h - margin)
+
+        base.paste(logo_resized, pos, logo_resized)
+        return base.convert("RGB")
+    except Exception:
+        return pil_img
+
+
+def load_msi_net():
+    global _msi_model
+    if _msi_model is None:
+        hf_dir = snapshot_download(repo_id="alexanderkroner/MSI-Net")
+        _msi_model = keras.layers.TFSMLayer(hf_dir, call_endpoint="serving_default")
+    return _msi_model
+
+
+def get_target_shape(original_shape):
+    ar = original_shape[0] / original_shape[1]
+    square_mode = abs(ar - 1.0)
+    landscape_mode = abs(ar - 240 / 320)
+    portrait_mode = abs(ar - 320 / 240)
+    best = min(square_mode, landscape_mode, portrait_mode)
+    if best == square_mode:
+        return (320, 320)
+    elif best == landscape_mode:
+        return (240, 320)
+    else:
+        return (320, 240)
+
+
+def preprocess_input(input_image, target_shape):
+    t = tf.expand_dims(input_image, axis=0)
+    t = tf.image.resize(t, target_shape, preserve_aspect_ratio=True)
+    vp = target_shape[0] - t.shape[1]
+    hp = target_shape[1] - t.shape[2]
+    v1, v2 = vp // 2, vp - vp // 2
+    h1, h2 = hp // 2, hp - hp // 2
+    t = tf.pad(t, [[0, 0], [v1, v2], [h1, h2], [0, 0]])
+    return t, [v1, v2], [h1, h2]
+
+
+def postprocess_output(output_tensor, vp, hp, original_shape):
+    output_tensor = output_tensor[:, vp[0]:output_tensor.shape[1] - vp[1], hp[0]:output_tensor.shape[2] - hp[1], :]
+    output_tensor = tf.image.resize(output_tensor, original_shape)
+    return output_tensor.numpy().squeeze()
+
+
+def compute_text_boost_map(pil_img):
+    img_arr = np.array(pil_img.convert("RGB"))
+    h, w = img_arr.shape[:2]
+    data = pytesseract.image_to_data(img_arr, config="--psm 11", output_type=pytesseract.Output.DICT)
+    text_map = np.zeros((h, w), dtype=np.float32)
+    for i in range(len(data['text'])):
+        word = data['text'][i].strip()
+        tw_, th_ = data['width'][i], data['height'][i]
+        if not word or th_ < 8 or tw_ < 5:
+            continue
+        x, y = data['left'][i], data['top'][i]
+        weight = min(1.0, (th_ / h) * 6)
+        pad = int(th_ * 0.15)
+        y0, y1 = max(0, y - pad), min(h, y + th_ + pad)
+        x0, x1 = max(0, x - pad), min(w, x + tw_ + pad)
+        text_map[y0:y1, x0:x1] = np.maximum(text_map[y0:y1, x0:x1], weight)
+    return text_map
+
+
+def compute_saliency_map(pil_image):
+    model = load_msi_net()
+    input_image = np.array(pil_image.convert("RGB"), dtype=np.float32)
+    original_shape = input_image.shape[:2]
+    target_shape = get_target_shape(original_shape)
+    input_tensor, v_pad, h_pad = preprocess_input(input_image, target_shape)
+    raw_output = model(input_tensor)
+    output_tensor = list(raw_output.values())[0] if isinstance(raw_output, dict) else raw_output
+    msi_saliency = postprocess_output(output_tensor, v_pad, h_pad, original_shape)
+    msi_saliency = msi_saliency.astype(np.float32)
+    msi_saliency -= msi_saliency.min()
+    if msi_saliency.max() > 0:
+        msi_saliency /= msi_saliency.max()
+
+    text_boost = compute_text_boost_map(pil_image)
+    combined = 0.7 * msi_saliency + 0.3 * text_boost
+    return combined
+
+
+def render_heatmap_overlay(img_bgr, saliency_map, alpha=0.45):
+    heat_u8 = (saliency_map * 255).astype(np.uint8)
+    heat_color = cv2.applyColorMap(heat_u8, cv2.COLORMAP_JET)
+    return cv2.addWeighted(heat_color, alpha, img_bgr, 1 - alpha, 0)
+
+
+def compute_attention_scores(saliency_map):
+    flat = np.sort(saliency_map.flatten())
+    n = len(flat)
+    cum = np.cumsum(flat)
+    gini = (n + 1 - 2 * np.sum(cum) / (cum[-1] + 1e-8)) / n
+    focus_score = float(gini) * 100
+    spread_score = float((saliency_map >= 0.5).mean()) * 100
+    return {"focus_score": round(focus_score, 1), "spread_score": round(spread_score, 1)}
+
+
+def compute_readability_score(read):
+    if read['words_checked'] == 0:
+        return None
+    penalty = 12 * math.sqrt(read['contrast_issues']) + 8 * math.sqrt(read['small_text_issues'])
+    return round(max(0, 100 - penalty), 1)
+
+
+def coverage_grade_score(spread_score):
+    if spread_score < 40:
+        return 100.0
+    return max(0.0, 100.0 - (spread_score - 40) * 2)
+
+
+def compute_overall_grade(focus_score, spread_score, readability_score):
+    coverage_score = coverage_grade_score(spread_score)
+    if readability_score is None:
+        total = round((0.55 / 0.75) * focus_score + (0.20 / 0.75) * coverage_score)
+    else:
+        total = round(0.55 * focus_score + 0.20 * coverage_score + 0.25 * readability_score)
+    total = max(0, min(100, total))
+    if total >= 75:
+        grade = "Strong"
+    elif total >= 50:
+        grade = "Good Start"
+    else:
+        grade = "Room to Grow"
+    return total, grade
+
+
+def explain_grade(focus_score, spread_score, readability_score, read):
+    coverage_score = coverage_grade_score(spread_score)
+    weighted_coverage_deficit = 0.20 * max(0, 100 - coverage_score)
+    if readability_score is None:
+        if weighted_coverage_deficit > 0.55 * max(0, 100 - focus_score):
+            return "based on attention alone (this design's text couldn't be reliably read, so readability wasn't scored) - mainly because attention is spread too widely without settling anywhere"
+        elif focus_score < 50:
+            return "based on attention alone (this design's text couldn't be reliably read, so readability wasn't scored) - mainly because attention is spread out without one clear focal point"
+        elif focus_score > 90:
+            return "based on attention alone (this design's text couldn't be reliably read, so readability wasn't scored) - attention is very tightly focused on one spot"
+        else:
+            return "based on attention alone - this design's text couldn't be reliably read, so readability wasn't included"
+    total_issues = read['contrast_issues'] + read['small_text_issues']
+    weighted_focus_deficit = 0.55 * max(0, 100 - focus_score)
+    weighted_read_deficit = 0.25 * max(0, 100 - readability_score)
+
+    if weighted_coverage_deficit > weighted_focus_deficit and weighted_coverage_deficit > weighted_read_deficit:
+        return "mainly because attention is spread too widely across the design without settling anywhere"
+    elif weighted_read_deficit >= weighted_focus_deficit and total_issues > 0:
+        return "mainly due to text readability - some text is harder to read than ideal"
+    elif focus_score < 50:
+        return "mainly because attention is spread out, without one clear focal point"
+    elif focus_score > 90:
+        return "attention is very tightly focused on one spot - worth checking other key elements (logo, CTA) aren't being missed"
+    else:
+        return "a mix of small readability and focus factors - see details below"
+
+
+def compute_hierarchy(text_sizes):
+    if len(text_sizes) < 2:
+        return None
+    largest = max(text_sizes)
+    smallest = min(text_sizes)
+    ratio = largest / max(smallest, 0.1)
+    score = round(min(100, ratio * 20), 1)
+    return score, ratio
+
+
+def hierarchy_description(ratio):
+    if ratio < 1.5:
+        return "text sizes are very similar - there's no single element that clearly leads the eye"
+    elif ratio < 2.5:
+        return "there's some size variation, but the hierarchy could be stronger"
+    else:
+        return "clear size hierarchy - one element leads, the rest support it"
+
+
+def compute_balance(saliency_map):
+    h, w = saliency_map.shape
+    left = saliency_map[:, :w // 2].mean()
+    right = saliency_map[:, w // 2:].mean()
+    top = saliency_map[:h // 2, :].mean()
+    bottom = saliency_map[h // 2:, :].mean()
+    horiz_diff = abs(left - right) / max(left + right, 1e-6)
+    vert_diff = abs(top - bottom) / max(top + bottom, 1e-6)
+    imbalance = (horiz_diff + vert_diff) / 2
+    score = round(max(0, 100 - imbalance * 200), 1)
+    return score
+
+
+def balance_description(score):
+    if score >= 75:
+        return "well balanced - visual weight is evenly distributed"
+    elif score >= 50:
+        return "somewhat uneven - one side carries noticeably more visual weight"
+    else:
+        return "heavily lopsided - attention is pulled hard toward one side or corner"
+
+
+def detect_price_info(all_words):
+    joined = " ".join(all_words)
+    price_pattern = r'[\$£€¥]\s?\d[\d,]*(\.\d{1,2})?|\b\d[\d,]*(\.\d{1,2})?\s?(SGD|USD|GBP|EUR|dollars?|cents?)\b'
+    return bool(re.search(price_pattern, joined, re.IGNORECASE))
+
+
+def detect_location_info(all_words):
+    joined = " ".join(all_words)
+    location_pattern = (
+        r'\b(venue|address|location|street|st\.|road|rd\.|avenue|ave\.|'
+        r'blvd|drive|dr\.|www\.|\.com|\.sg|\.net)\b'
+    )
+    postal_pattern = r'\bS?\d{6}\b'
+    return bool(re.search(location_pattern, joined, re.IGNORECASE)) or bool(re.search(postal_pattern, joined))
+
+
+def coverage_description(spread_score):
+    if spread_score < 15:
+        return "concentrated on one clear focal point - that's usually a good sign, not a problem"
+    elif spread_score < 40:
+        return "landing mostly in a couple of areas, which is fairly typical"
+    else:
+        return "spread fairly widely across the design"
+
+
+def create_score_chart(focus_score, spread_score, readability_score, hierarchy_score=None, balance_score=None):
+    labels = ["Clear Focal\nPoint", "Attention\nCoverage"]
+    values = [focus_score, spread_score]
+    if readability_score is not None:
+        labels.append("Readability")
+        values.append(readability_score)
+    if hierarchy_score is not None:
+        labels.append("Visual\nHierarchy")
+        values.append(hierarchy_score)
+    if balance_score is not None:
+        labels.append("Balance")
+        values.append(balance_score)
+    colors = []
+    for v in values:
+        if v >= 75:
+            colors.append("#4CAF50")
+        elif v >= 50:
+            colors.append("#FFC107")
+        else:
+            colors.append("#F44336")
+    fig, ax = plt.subplots(figsize=(5, 3), dpi=100)
+    bars = ax.barh(labels, values, color=colors)
+    ax.set_xlim(0, 100)
+    ax.set_xlabel("Score out of 100")
+    ax.invert_yaxis()
+    for bar, v in zip(bars, values):
+        ax.text(min(v + 2, 92), bar.get_y() + bar.get_height() / 2, f"{v:.0f}", va="center", fontsize=10)
+    ax.set_title("Scores at a Glance")
+    fig.tight_layout()
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png")
+    plt.close(fig)
+    buf.seek(0)
+    return Image.open(buf).convert("RGB")
+
+
+def generate_recommendations(saliency_map, scores, read, img_h, img_w):
+    tips = []
+    bottom_band = saliency_map[int(img_h * 0.85):, :]
+    bottom_avg = bottom_band.mean()
+    if bottom_avg < 0.25:
+        tips.append("Your bottom section (often where CTAs or contact info sit) is getting low attention. Consider bolder color contrast or larger text there.")
+
+    if scores['focus_score'] < 40:
+        tips.append("Attention is scattered with no clear focal point. Consider making one element (headline, product, or offer) visually dominant.")
+    elif scores['focus_score'] > 90:
+        tips.append("Attention is very narrowly focused on one spot - double check other important elements (logo, CTA) are not being ignored.")
+
+    if read['contrast_issues'] > 0:
+        if read['contrast_issues'] > 5:
+            tips.append(f"A quick win: {read['contrast_issues']} text elements could read more clearly. The good news - the pattern (see white boxes marked \"Low contrast\" on the image) suggests one color choice affects most of them, so a single tweak will likely fix most at once.")
+        else:
+            tips.append(f"{read['contrast_issues']} text element(s) could be easier to read - see the white boxes marked \"Low contrast\" on the image above, then darken the text or lighten its background.")
+    if read['small_text_issues'] > 0:
+        if read['small_text_issues'] > 5:
+            tips.append(f"Another quick win: {read['small_text_issues']} text elements are a bit small. Likely one font-size setting affects most of them, so this is usually a fast fix.")
+        else:
+            tips.append(f"{read['small_text_issues']} text element(s) are a bit small - see the white boxes marked \"Small text\" on the image above.")
+
+    if not tips:
+        tips.append("No major issues detected - this design is in solid shape.")
+
+    return tips
+
+
+def identify_hotspots(saliency_map, img_h, img_w, top_n=4):
+    threshold = np.percentile(saliency_map, 85)
+    binary = (saliency_map >= threshold).astype(np.uint8)
+    kernel = np.ones((25, 25), np.uint8)
+    binary = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)
+    num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(binary, connectivity=8)
+    zones = []
+    for i in range(1, num_labels):
+        area = stats[i, cv2.CC_STAT_AREA]
+        if area < (img_h * img_w * 0.005):
+            continue
+        cx, cy = centroids[i]
+        h_pos = "top" if cy < img_h * 0.33 else ("bottom" if cy > img_h * 0.66 else "middle")
+        w_pos = "left" if cx < img_w * 0.33 else ("right" if cx > img_w * 0.66 else "center")
+        avg_intensity = float(saliency_map[labels == i].mean())
+        zones.append({"position": f"{h_pos}-{w_pos}", "area_pct": round(float(area / (img_h * img_w)) * 100, 1),
+                       "intensity": round(avg_intensity * 100, 1), "cx": int(cx), "cy": int(cy)})
+    zones.sort(key=lambda z: -z["intensity"])
+    top_zones = zones[:top_n]
+    for idx, z in enumerate(top_zones):
+        z["intensity_rank"] = idx + 1
+    top_zones.sort(key=lambda z: (z["cy"], z["cx"]))
+    return top_zones
+
+
+def relative_luminance(rgb):
+    def chan(c):
+        c = c / 255.0
+        return c / 12.92 if c <= 0.03928 else ((c + 0.055) / 1.055) ** 2.4
+    r, g, b = rgb
+    return 0.2126 * chan(r) + 0.7152 * chan(g) + 0.0722 * chan(b)
+
+
+def contrast_ratio(rgb1, rgb2):
+    l1 = relative_luminance(rgb1)
+    l2 = relative_luminance(rgb2)
+    lighter, darker = max(l1, l2), min(l1, l2)
+    return (lighter + 0.05) / (darker + 0.05)
+
+
+def required_contrast_ratio(size_pct):
+    return 3.0 if size_pct >= 3.0 else 4.5
+
+
+def contrast_severity_label(ratio, required_ratio):
+    deficit = (required_ratio - ratio) / required_ratio
+    if deficit > 0.6:
+        return "could be much clearer"
+    elif deficit > 0.35:
+        return "could be clearer"
+    elif deficit > 0.15:
+        return "slightly less clear than ideal"
+    else:
+        return "close to the recommended level"
+
+
+def sample_text_and_bg_color(img_arr, x, y, w, h):
+    pad = max(2, int(h * 0.3))
+    y0, y1 = max(0, y - pad), min(img_arr.shape[0], y + h + pad)
+    x0, x1 = max(0, x - pad), min(img_arr.shape[1], x + w + pad)
+    region = img_arr[y0:y1, x0:x1].reshape(-1, 3).astype(np.float32)
+    if len(region) < 10:
+        return None, None
+    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
+    try:
+        _, _, centers = cv2.kmeans(region, 2, None, criteria, 3, cv2.KMEANS_PP_CENTERS)
+    except cv2.error:
+        return None, None
+    color1 = tuple(centers[0].astype(int))
+    color2 = tuple(centers[1].astype(int))
+    return color1, color2
+
+
+def analyze_readability(pil_img):
+    img_arr = np.array(pil_img.convert("RGB"))
+    img_h = img_arr.shape[0]
+
+    gray = cv2.cvtColor(img_arr, cv2.COLOR_RGB2GRAY)
+
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+    gray_enhanced = clahe.apply(gray)
+
+    gray_denoised = cv2.medianBlur(gray_enhanced, 3)
+
+    bw = cv2.adaptiveThreshold(gray_denoised, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 10)
+
+    cleanup_kernel = np.ones((2, 2), np.uint8)
+    bw = cv2.morphologyEx(bw, cv2.MORPH_OPEN, cleanup_kernel)
+
+    ocr_data = pytesseract.image_to_data(bw, output_type=pytesseract.Output.DICT)
+    all_words_loose = [t.strip() for t in ocr_data['text'] if t.strip()]
+
+    words_checked = 0
+    contrast_issues = 0
+    small_text_issues = 0
+    issue_lines = []
+    issue_boxes = []
+    text_sizes = []
+    for i in range(len(ocr_data['text'])):
+        word = ocr_data['text'][i].strip()
+        conf = int(ocr_data['conf'][i])
+        if not word or conf < 60 or len(word) < 2:
+            continue
+        x, y, w, h = ocr_data['left'][i], ocr_data['top'][i], ocr_data['width'][i], ocr_data['height'][i]
+        if w < 3 or h < 3:
+            continue
+        words_checked += 1
+        size_pct = (h / img_h) * 100
+        text_sizes.append(size_pct)
+        text_color, bg_color = sample_text_and_bg_color(img_arr, x, y, w, h)
+        if text_color is None:
+            continue
+        ratio = contrast_ratio(text_color, bg_color)
+        required_ratio = required_contrast_ratio(size_pct)
+        is_contrast_issue = ratio < required_ratio
+        is_small_issue = size_pct < 1.2
+
+        if is_contrast_issue:
+            contrast_issues += 1
+            if len(issue_lines) < 5:
+                severity = contrast_severity_label(ratio, required_ratio)
+                issue_lines.append(f"- \"{word}\" {severity} against its background")
+        if is_small_issue:
+            small_text_issues += 1
+            if len(issue_lines) < 5:
+                issue_lines.append(f"- \"{word}\" is a bit small to read comfortably")
+
+        if is_contrast_issue or is_small_issue:
+            combined_label = " + ".join(
+                l for l, flag in [("Low contrast", is_contrast_issue), ("Small text", is_small_issue)] if flag
+            )
+            issue_boxes.append({"box": (x, y, w, h), "label": combined_label})
+    return {"words_checked": words_checked, "contrast_issues": contrast_issues,
+            "small_text_issues": small_text_issues, "issue_lines": issue_lines, "issue_boxes": issue_boxes,
+            "text_sizes": text_sizes, "all_words": all_words_loose}
+
+
+def merge_issue_boxes(issue_boxes, gap=15):
+    by_label = defaultdict(list)
+    for item in issue_boxes:
+        by_label[item["label"]].append(item["box"])
+    merged = []
+    for label, boxes in by_label.items():
+        boxes = sorted(boxes, key=lambda b: (b[1], b[0]))
+        used = [False] * len(boxes)
+        for i in range(len(boxes)):
+            if used[i]:
+                continue
+            x, y, w, h = boxes[i]
+            mx0, my0, mx1, my1 = x, y, x + w, y + h
+            used[i] = True
+            changed = True
+            while changed:
+                changed = False
+                for j in range(len(boxes)):
+                    if used[j]:
+                        continue
+                    bx, by, bw, bh = boxes[j]
+                    bx0, by0, bx1, by1 = bx, by, bx + bw, by + bh
+                    vertical_overlap = min(my1, by1) - max(my0, by0)
+                    if vertical_overlap > 0 and bx0 - mx1 <= gap and bx1 >= mx0 - gap:
+                        mx0, my0 = min(mx0, bx0), min(my0, by0)
+                        mx1, my1 = max(mx1, bx1), max(my1, by1)
+                        used[j] = True
+                        changed = True
+            merged.append({"box": (mx0, my0, mx1 - mx0, my1 - my0), "label": label})
+    return merged
+
+
+def draw_issue_markers(overlay_bgr, issue_boxes, img_h, img_w):
+    img = overlay_bgr.copy()
+    scale = max(1.0, min(img_h, img_w) / 800.0)
+    merged_issues = merge_issue_boxes(issue_boxes)
+    placed_label_rects = []
+    pad = int(4 * scale)
+    box_border = max(2, int(3 * scale))
+    thin_border = max(1, int(scale))
+    font_scale = 0.5 * scale
+    text_thickness = max(1, int(round(scale)))
+    for issue in merged_issues:
+        x, y, w, h = issue["box"]
+        cv2.rectangle(img, (x - pad, y - pad), (x + w + pad, y + h + pad), (255, 255, 255), box_border)
+        cv2.rectangle(img, (x - pad, y - pad), (x + w + pad, y + h + pad), (0, 0, 0), thin_border)
+        label = issue["label"]
+        (tw, th), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, font_scale, text_thickness)
+        label_x0 = x - pad
+        label_x1 = label_x0 + tw + int(8 * scale)
+        label_y1 = max(th + int(6 * scale), y - pad)
+        label_y0 = label_y1 - th - int(6 * scale)
+
+        collision = True
+        attempts = 0
+        while collision and attempts < 10:
+            collision = False
+            for (px0, py0, px1, py1) in placed_label_rects:
+                if not (label_x1 < px0 or label_x0 > px1 or label_y1 < py0 or label_y0 > py1):
+                    collision = True
+                    label_y1 -= (th + int(10 * scale))
+                    label_y0 = label_y1 - th - int(6 * scale)
+                    break
+            attempts += 1
+
+        placed_label_rects.append((label_x0, label_y0, label_x1, label_y1))
+        cv2.rectangle(img, (label_x0, label_y0), (label_x1, label_y1), (255, 255, 255), -1)
+        cv2.rectangle(img, (label_x0, label_y0), (label_x1, label_y1), (0, 0, 0), thin_border)
+        cv2.putText(img, label, (label_x0 + int(4 * scale), label_y1 - int(4 * scale)),
+                    cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 0), text_thickness, cv2.LINE_AA)
+    return img
+
+
+def markdown_to_paragraphs(md_text, styles):
+    story = []
+    for raw_line in md_text.split("\n"):
+        line = raw_line.strip()
+        if not line:
+            story.append(Spacer(1, 6))
+            continue
+        if line.startswith("## "):
+            story.append(Paragraph(line[3:], styles['Heading1']))
+        elif line.startswith("### "):
+            story.append(Paragraph(line[4:], styles['Heading2']))
+        elif line.startswith("---"):
+            story.append(Spacer(1, 10))
+        elif line.startswith("- ") or line.startswith("* "):
+            text = line[2:]
+            text = re.sub(r'\*\*(.*?)\*\*', r'<b>\1</b>', text)
+            text = re.sub(r'\*(.*?)\*', r'<i>\1</i>', text)
+            story.append(Paragraph("&bull; " + text, styles['Normal']))
+        elif re.match(r'^\d+\.\s', line):
+            num, text = line.split('.', 1)
+            text = re.sub(r'\*\*(.*?)\*\*', r'<b>\1</b>', text.strip())
+            story.append(Paragraph(f"{num}. {text}", styles['Normal']))
+        else:
+            text = re.sub(r'\*\*(.*?)\*\*', r'<b>\1</b>', line)
+            text = re.sub(r'\*(.*?)\*', r'<i>\1</i>', text)
+            text = re.sub(r'\[([^\]]+)\]\(([^)]+)\)', r'<link href="\2"><u>\1</u></link>', text)
+            story.append(Paragraph(text, styles['Normal']))
+    return story
+
+
+def stamp_pdf_watermark(canvas_obj, doc):
+    if not os.path.exists(LOGO_PATH):
+        return
+    try:
+        from reportlab.lib.utils import ImageReader
+        logo = Image.open(LOGO_PATH).convert("RGBA")
+        bbox = logo.getbbox()
+        if bbox:
+            logo = logo.crop(bbox)
+        logo_h = 0.8 * inch
+        logo_w = logo_h * (logo.width / logo.height)
+        margin = 15
+        canvas_obj.saveState()
+        canvas_obj.setFillAlpha(0.95)
+        canvas_obj.drawImage(ImageReader(logo), doc.pagesize[0] - logo_w - margin, margin,
+                              width=logo_w, height=logo_h, mask='auto', preserveAspectRatio=True)
+        canvas_obj.restoreState()
+    except Exception:
+        pass
+
+
+def export_pdf(heatmap_img, chart_img, summary_md):
+    if heatmap_img is None or not summary_md:
+        return None
+    if isinstance(heatmap_img, np.ndarray):
+        heatmap_img = Image.fromarray(heatmap_img)
+    if chart_img is not None and isinstance(chart_img, np.ndarray):
+        chart_img = Image.fromarray(chart_img)
+    styles = getSampleStyleSheet()
+    tmp_dir = tempfile.gettempdir()
+    pdf_path = os.path.join(tmp_dir, "design_analysis_report.pdf")
+    doc = SimpleDocTemplate(pdf_path, pagesize=letter, topMargin=0.6 * inch, bottomMargin=0.6 * inch,
+                             leftMargin=0.7 * inch, rightMargin=0.7 * inch)
+    story = [Paragraph("Design Analyzer Report", styles['Title']), Spacer(1, 12)]
+
+    max_width = 6.1 * inch
+    heatmap_path = os.path.join(tmp_dir, "heatmap_export_temp.png")
+    heatmap_img.save(heatmap_path)
+    ratio = heatmap_img.height / heatmap_img.width
+    story.append(RLImage(heatmap_path, width=max_width, height=max_width * ratio))
+    story.append(Spacer(1, 14))
+
+    if chart_img is not None:
+        chart_path = os.path.join(tmp_dir, "chart_export_temp.png")
+        chart_img.save(chart_path)
+        chart_ratio = chart_img.height / chart_img.width
+        story.append(RLImage(chart_path, width=max_width, height=max_width * chart_ratio))
+        story.append(Spacer(1, 14))
+
+    story.extend(markdown_to_paragraphs(summary_md, styles))
+    doc.build(story, onFirstPage=stamp_pdf_watermark, onLaterPages=stamp_pdf_watermark)
+    return pdf_path
+
+
+def draw_scan_path(overlay_bgr, zones, img_h, img_w):
+    img = overlay_bgr.copy()
+    scale = max(1.0, min(img_h, img_w) / 800.0)
+    outer_thickness = int(8 * scale)
+    inner_thickness = int(4 * scale)
+    ordered = sorted(zones, key=lambda z: z["intensity_rank"])
+    for i in range(len(ordered) - 1):
+        pt1 = (ordered[i]["cx"], ordered[i]["cy"])
+        pt2 = (ordered[i + 1]["cx"], ordered[i + 1]["cy"])
+        cv2.arrowedLine(img, pt1, pt2, (0, 0, 0), outer_thickness, tipLength=0.08, line_type=cv2.LINE_AA)
+        cv2.arrowedLine(img, pt1, pt2, (0, 255, 255), inner_thickness, tipLength=0.08, line_type=cv2.LINE_AA)
+    return img
+
+
+def draw_zone_labels(overlay_bgr, zones, img_h, img_w):
+    img = overlay_bgr.copy()
+    scale = max(1.0, min(img_h, img_w) / 800.0)
+    radius = int(22 * scale)
+    font_scale = 0.9 * scale
+    thickness = max(2, int(3 * scale))
+    for i, z in enumerate(zones, 1):
+        cx, cy = z["cx"], z["cy"]
+        cv2.circle(img, (cx, cy), radius, (255, 255, 255), -1)
+        cv2.circle(img, (cx, cy), radius, (0, 0, 0), thickness)
+        text = str(i)
+        (tw, th), _ = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, font_scale, thickness)
+        cv2.putText(img, text, (cx - tw // 2, cy + th // 2), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 0), thickness, cv2.LINE_AA)
+    return img
+
+
+def flatten_transparency(pil_image):
+    if pil_image.mode in ("RGBA", "LA") or (pil_image.mode == "P" and "transparency" in pil_image.info):
+        rgba = pil_image.convert("RGBA")
+        background = Image.new("RGB", rgba.size, (255, 255, 255))
+        background.paste(rgba, mask=rgba.split()[-1])
+        return background
+    return pil_image.convert("RGB")
+
+
+def analyze(pil_image, overlay_strength):
+    if pil_image is None:
+        return None, None, "Upload an image first."
+    pil_rgb = flatten_transparency(pil_image)
+    img_bgr = cv2.cvtColor(np.array(pil_rgb), cv2.COLOR_RGB2BGR)
+    h, w = img_bgr.shape[:2]
+
+    saliency_map = compute_saliency_map(pil_rgb)
+    overlay_bgr = render_heatmap_overlay(img_bgr, saliency_map, alpha=overlay_strength)
+
+    scores = compute_attention_scores(saliency_map)
+    zones = identify_hotspots(saliency_map, h, w)
+    read = analyze_readability(pil_rgb)
+    readability_score = compute_readability_score(read)
+    total_score, grade = compute_overall_grade(scores['focus_score'], scores['spread_score'], readability_score)
+    grade_reason = explain_grade(scores['focus_score'], scores['spread_score'], readability_score, read)
+    tips = generate_recommendations(saliency_map, scores, read, h, w)
+    hierarchy_result = compute_hierarchy(read.get('text_sizes', []))
+    balance_score = compute_balance(saliency_map)
+    hierarchy_score = hierarchy_result[0] if hierarchy_result else None
+    chart_image = create_score_chart(scores['focus_score'], scores['spread_score'], readability_score,
+                                      hierarchy_score, balance_score)
+
+    overlay_bgr = draw_scan_path(overlay_bgr, zones, h, w)
+    overlay_bgr = draw_zone_labels(overlay_bgr, zones, h, w)
+    overlay_bgr = draw_issue_markers(overlay_bgr, read.get("issue_boxes", []), h, w)
+    overlay_rgb = cv2.cvtColor(overlay_bgr, cv2.COLOR_BGR2RGB)
+
+    summary = f"## Grade: {grade} — {total_score}/100\n"
+    summary += f"*Why: {grade_reason}*\n\n"
+
+    summary += "**Where Attention Goes First:** *(numbers match the image above)*\n"
+    if zones:
+        for i, z in enumerate(zones, 1):
+            summary += f"{i}. {z['position'].replace('-', ' ').title()} ({z['intensity']}/100 intensity, {z['area_pct']}% of design)\n"
+    else:
+        summary += "- Attention is fairly even across the design, no strong single hotspot.\n"
+
+    summary += (
+        f"\n**Scores:**\n"
+        f"- Clear Focal Point: {scores['focus_score']}/100 (how strongly attention lands on one main spot, vs scattered everywhere)\n"
+        f"- Attention Coverage: {scores['spread_score']}/100 - {coverage_description(scores['spread_score'])}\n"
+        f"- Readability: "
+    )
+    if read['words_checked'] == 0:
+        summary += "couldn't be reliably read on this design (common with heavily stylized or hand-lettered fonts) - not included in the score above\n"
+    elif read['contrast_issues'] == 0 and read['small_text_issues'] == 0:
+        summary += "clean, no changes needed\n"
+    else:
+        total_issues = read['contrast_issues'] + read['small_text_issues']
+        summary += f"{total_issues} easy improvement(s) spotted - see below\n"
+
+    if read['issue_lines']:
+        summary += "\n**A few examples:**\n" + "\n".join(read['issue_lines']) + "\n"
+        summary += (
+            "\n*Note: this is automated and can occasionally flag text that's actually fine, "
+            "especially on busy or gradient backgrounds - if something flagged looks perfectly "
+            "readable to your eye, trust your eye.*\n"
+        )
+
+    summary += "\n**How to Improve:** *(common and usually quick to fix)*\n"
+    for tip in tips:
+        summary += f"- {tip}\n"
+
+    summary += "\n**Layout & Composition:** *(based on classic layout principles - hierarchy and balance)*\n"
+    if hierarchy_result:
+        summary += f"- Visual Hierarchy: {hierarchy_description(hierarchy_result[1])}\n"
+    else:
+        summary += "- Visual Hierarchy: not enough distinct text elements to judge\n"
+    summary += f"- Balance: {balance_description(balance_score)}\n"
+
+    has_price = detect_price_info(read.get('all_words', []))
+    has_location = detect_location_info(read.get('all_words', []))
+    summary += (
+        "\n**Content Checklist:** *(Mike Stevens' classic Who/What/Where/Why/How Much - "
+        "not every design needs all five)*\n"
+        f"- How Much (price): {'found' if has_price else 'not detected - if this design should show a price, double check it'}\n"
+        f"- Where (location/contact): {'found' if has_location else 'not detected - if this design should point somewhere, double check it'}\n"
+        "- Who / What / Why: these need a human read, not automation - ask yourself: is it "
+        "clear who's offering this, exactly what they're offering, and why someone should care?\n"
+    )
+
+    summary += (
+        "\n---\n"
+        "**Methodology:** Attention prediction combines MSI-Net, a peer-reviewed saliency model "
+        "(Kroner et al., *Neural Networks*, 2020, "
+        "[DOI: 10.1016/j.neunet.2020.05.004](https://doi.org/10.1016/j.neunet.2020.05.004)), "
+        "with a size-proportional boost for legible text - since eye-tracking research confirms "
+        "large headline text reliably draws attention, which pure bottom-up saliency models can "
+        "underweight relative to small high-contrast graphics. This is a data-informed prediction, "
+        "not a substitute for live user testing."
+    )
+
+    summary += (
+        "\n\n---\n"
+        "**This tool flags what's off. A designer knows what's right.** "
+        "Haan is a brand identity and visual designer with 12+ years turning "
+        "insights like these into designs that actually convert, stay true to "
+        "your brand, and look intentional (not just \"fixed\").\n\n"
+        "[Visit haanilango.com](https://www.haanilango.com) · Tel: +65 9027 2070"
+    )
+
+    result_image = add_watermark(Image.fromarray(overlay_rgb))
+    chart_image = add_watermark(chart_image, scale=0.26)
+
+    return result_image, chart_image, summary
+
+
+with gr.Blocks(title="Design Analyzer") as demo:
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Upload design")
+            strength_slider = gr.Slider(0.1, 0.8, value=0.45, step=0.05, label="Heatmap overlay strength")
+            analyze_btn = gr.Button("Analyze", variant="primary")
+        with gr.Column():
+            image_output = gr.Image(label="Heatmap result", interactive=False)
+            chart_output = gr.Image(label="Scores at a glance", interactive=False)
+    with gr.Row():
+        text_output = gr.Markdown()
+    with gr.Row():
+        pdf_btn = gr.Button("Download PDF Report")
+        pdf_output = gr.File(label="PDF Report")
+    analyze_btn.click(fn=analyze, inputs=[image_input, strength_slider], outputs=[image_output, chart_output, text_output])
+    pdf_btn.click(fn=export_pdf, inputs=[image_output, chart_output, text_output], outputs=pdf_output)
+    gr.Markdown("---\n© 2026 Haan Ilango. All rights reserved. This tool's methodology and design are original work.")
+
+demo.launch()