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olivas/backend/app/services/report_generator.py
DJP c1b80eb9a7 Replace entropy score with composite Design Effectiveness Score 
The pure Shannon entropy score penalized well-designed ads with multiple
intentional visual elements (e.g. hero product + text + logo scored ~8/100).

New composite score (0-100) weights four components:
- Peak Dominance (30%): strength of #1 hotspot vs rest
- Hierarchy Clarity (25%): monotonic intensity ordering
- Gaze Coherence (25%): smooth spatial gaze path
- Entropy Concentration (20%): sqrt-softened entropy

The raw entropy score is preserved as entropy_score for users who want it,
visible in the ScoreCard hover tooltip and PDF report.

Also adds auto-create DB tables on startup for fresh Docker deploys.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-24 22:50:12 -05:00

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import io
import os
from datetime import datetime
from PIL import Image as PILImage
from reportlab.lib import colors
from reportlab.lib.pagesizes import A4
from reportlab.lib.styles import ParagraphStyle
from reportlab.lib.units import inch, mm
from reportlab.pdfbase import pdfmetrics
from reportlab.pdfbase.ttfonts import TTFont
from reportlab.platypus import (
Image,
PageBreak,
Paragraph,
SimpleDocTemplate,
Spacer,
Table,
TableStyle,
)
# ─── Colors ──────────────────────────────────────────
NAVY = colors.HexColor("#1a1a2e")
GOLD = colors.HexColor("#ffc407")
GOLD_LIGHT = colors.HexColor("#fff8e0")
LIGHT_GRAY = colors.HexColor("#f5f5f5")
MEDIUM_GRAY = colors.HexColor("#666666")
DARK_TEXT = colors.HexColor("#333333")
WHITE = colors.white
GREEN = colors.HexColor("#16a34a")
BLUE = colors.HexColor("#2563eb")
AMBER = colors.HexColor("#d97706")
PURPLE = colors.HexColor("#7c3aed")
# ─── Register Montserrat fonts ──────────────────────
FONT_DIR = os.path.join(os.path.dirname(__file__), "fonts")
pdfmetrics.registerFont(TTFont("Montserrat", os.path.join(FONT_DIR, "Montserrat-Regular.ttf")))
pdfmetrics.registerFont(TTFont("Montserrat-Bold", os.path.join(FONT_DIR, "Montserrat-Bold.ttf")))
pdfmetrics.registerFont(TTFont("Montserrat-SemiBold", os.path.join(FONT_DIR, "Montserrat-SemiBold.ttf")))
pdfmetrics.registerFontFamily(
"Montserrat",
normal="Montserrat",
bold="Montserrat-Bold",
)
def _bytes_to_image(data: bytes) -> io.BytesIO:
buf = io.BytesIO(data)
buf.seek(0)
return buf
def _make_styles():
title = ParagraphStyle(
"Title",
fontName="Montserrat-Bold",
fontSize=32,
textColor=NAVY,
spaceAfter=6,
leading=38,
)
subtitle = ParagraphStyle(
"Subtitle",
fontName="Montserrat",
fontSize=14,
textColor=GOLD,
spaceAfter=20,
leading=18,
)
heading = ParagraphStyle(
"Heading",
fontName="Montserrat-Bold",
fontSize=18,
textColor=NAVY,
spaceBefore=12,
spaceAfter=8,
leading=22,
)
subheading = ParagraphStyle(
"Subheading",
fontName="Montserrat-SemiBold",
fontSize=13,
textColor=NAVY,
spaceBefore=8,
spaceAfter=4,
leading=16,
)
body = ParagraphStyle(
"Body",
fontName="Montserrat",
fontSize=10,
textColor=DARK_TEXT,
spaceAfter=6,
leading=14,
)
body_small = ParagraphStyle(
"BodySmall",
fontName="Montserrat",
fontSize=9,
textColor=MEDIUM_GRAY,
spaceAfter=4,
leading=12,
)
meta_label = ParagraphStyle(
"MetaLabel",
fontName="Montserrat-SemiBold",
fontSize=10,
textColor=MEDIUM_GRAY,
spaceAfter=2,
)
meta_value = ParagraphStyle(
"MetaValue",
fontName="Montserrat-Bold",
fontSize=14,
textColor=NAVY,
spaceAfter=8,
)
footer = ParagraphStyle(
"Footer",
fontName="Montserrat",
fontSize=8,
textColor=MEDIUM_GRAY,
alignment=1, # center
)
return {
"title": title,
"subtitle": subtitle,
"heading": heading,
"subheading": subheading,
"body": body,
"body_small": body_small,
"meta_label": meta_label,
"meta_value": meta_value,
"footer": footer,
}
def _insight_type_label(t: str) -> tuple[str, colors.Color]:
return {
"success": ("STRENGTH", GREEN),
"warning": ("ATTENTION", AMBER),
"info": ("INSIGHT", BLUE),
}.get(t, ("INSIGHT", BLUE))
def _build_insight_table(insights: list[dict], styles: dict, is_ai: bool = False) -> list:
"""Build styled insight rows as ReportLab elements."""
elements = []
for insight in insights:
label_text, label_color = _insight_type_label(insight["type"])
accent = PURPLE if is_ai else label_color
# Build a mini table for each insight card
badge = f'<font color="#{accent.hexval()[2:]}" size="7"><b>{label_text}</b></font>'
title = f'<font name="Montserrat-SemiBold" size="10" color="#1a1a2e">{insight["title"]}</font>'
desc = f'<font name="Montserrat" size="9" color="#555555">{insight["description"]}</font>'
content = Paragraph(f"{badge}<br/>{title}<br/>{desc}", styles["body"])
t = Table(
[[content]],
colWidths=[170 * mm],
)
t.setStyle(TableStyle([
("BACKGROUND", (0, 0), (-1, -1), GOLD_LIGHT if is_ai else LIGHT_GRAY),
("LEFTPADDING", (0, 0), (-1, -1), 10),
("RIGHTPADDING", (0, 0), (-1, -1), 10),
("TOPPADDING", (0, 0), (-1, -1), 8),
("BOTTOMPADDING", (0, 0), (-1, -1), 8),
("ROUNDEDCORNERS", [4, 4, 4, 4]),
("LINEBEFOREDECORCOLOR", (0, 0), (0, -1), accent),
# Left accent border
("LINEBEFORE", (0, 0), (0, -1), 3, accent),
]))
elements.append(t)
elements.append(Spacer(1, 4))
return elements
def generate_report(
analysis,
original_image: bytes,
heatmap_image: bytes,
gaze_image: bytes,
aois: list,
rule_insights: list[dict] | None = None,
ai_insights: list[dict] | None = None,
ai_cost_usd: float | None = None,
) -> bytes:
"""Generate a professional PDF report for an analysis."""
buffer = io.BytesIO()
doc = SimpleDocTemplate(
buffer,
pagesize=A4,
topMargin=20 * mm,
bottomMargin=20 * mm,
leftMargin=15 * mm,
rightMargin=15 * mm,
)
styles = _make_styles()
elements = []
# ─── Cover Page ──────────────────────────────────────────
elements.append(Spacer(1, 50))
elements.append(Paragraph("OliVAS", styles["title"]))
elements.append(Paragraph("Visual Attention Analysis Report", styles["subtitle"]))
elements.append(Spacer(1, 8))
# Gold divider line
divider = Table([[""]], colWidths=[60 * mm], rowHeights=[2])
divider.setStyle(TableStyle([
("BACKGROUND", (0, 0), (-1, -1), GOLD),
("LEFTPADDING", (0, 0), (-1, -1), 0),
("RIGHTPADDING", (0, 0), (-1, -1), 0),
("TOPPADDING", (0, 0), (-1, -1), 0),
("BOTTOMPADDING", (0, 0), (-1, -1), 0),
]))
elements.append(divider)
elements.append(Spacer(1, 20))
# Metadata grid
meta_data = [
["ANALYSIS", "MODEL", "DATE", "IMAGE SIZE"],
[
analysis.name,
analysis.model_used.replace("_", " ").title(),
datetime.now().strftime("%B %d, %Y"),
f"{analysis.image_width} x {analysis.image_height}",
],
]
meta_table = Table(meta_data, colWidths=[45 * mm] * 4)
meta_table.setStyle(TableStyle([
("FONTNAME", (0, 0), (-1, 0), "Montserrat-SemiBold"),
("FONTSIZE", (0, 0), (-1, 0), 8),
("TEXTCOLOR", (0, 0), (-1, 0), MEDIUM_GRAY),
("FONTNAME", (0, 1), (-1, 1), "Montserrat-Bold"),
("FONTSIZE", (0, 1), (-1, 1), 11),
("TEXTCOLOR", (0, 1), (-1, 1), NAVY),
("TOPPADDING", (0, 0), (-1, -1), 4),
("BOTTOMPADDING", (0, 0), (-1, -1), 4),
("VALIGN", (0, 0), (-1, -1), "TOP"),
]))
elements.append(meta_table)
elements.append(Spacer(1, 10))
# Score badge
if analysis.overall_score is not None:
score = analysis.overall_score
score_color = GREEN if score >= 55 else (AMBER if score >= 35 else colors.HexColor("#ef4444"))
score_text = (
f'<font name="Montserrat-SemiBold" size="9" color="#666666">DESIGN EFFECTIVENESS</font><br/>'
f'<font name="Montserrat-Bold" size="28" color="#{score_color.hexval()[2:]}">{score:.0f}</font>'
f'<font name="Montserrat" size="12" color="#999999"> / 100</font>'
)
entropy = getattr(analysis, "entropy_score", None)
if entropy is not None:
score_text += (
f'<br/><font name="Montserrat" size="8" color="#999999">'
f'Entropy concentration: {entropy:.0f}/100</font>'
)
elements.append(Paragraph(score_text, styles["body"]))
elements.append(Spacer(1, 10))
# Thumbnail on cover
max_width = 160 * mm
img = PILImage.open(io.BytesIO(original_image))
aspect = img.width / img.height
img_width = min(max_width, 130 * mm)
img_height = img_width / aspect
if img_height > 90 * mm:
img_height = 90 * mm
img_width = img_height * aspect
elements.append(Image(_bytes_to_image(original_image), width=img_width, height=img_height))
elements.append(PageBreak())
# ─── Heatmap Page ────────────────────────────────────────
elements.append(Paragraph("Attention Heatmap", styles["heading"]))
elements.append(
Paragraph(
"Areas highlighted in warm colors (red/yellow) indicate high predicted "
"attention during the first 3-5 seconds of viewing. Cool colors (blue) "
"indicate lower attention probability.",
styles["body"],
)
)
elements.append(Spacer(1, 8))
elements.append(Image(_bytes_to_image(heatmap_image), width=img_width, height=img_height))
elements.append(PageBreak())
# ─── Gaze Sequence Page ──────────────────────────────────
elements.append(Paragraph("Predicted Gaze Sequence", styles["heading"]))
elements.append(
Paragraph(
"Numbered points show the predicted order in which viewers will "
"fixate on different areas of the design. Point #1 is where the eye "
"lands first.",
styles["body"],
)
)
elements.append(Spacer(1, 8))
elements.append(Image(_bytes_to_image(gaze_image), width=img_width, height=img_height))
if analysis.gaze_sequence:
elements.append(Spacer(1, 10))
elements.append(Paragraph("Fixation Details", styles["subheading"]))
table_data = [["#", "Position", "From Left", "From Top", "Probability"]]
for point in analysis.gaze_sequence:
table_data.append([
str(point["rank"]),
f"({point['x']}, {point['y']})",
f"{point['x_pct']:.1f}%",
f"{point['y_pct']:.1f}%",
f"{point['probability']:.1%}",
])
t = Table(table_data, colWidths=[25, 80, 60, 60, 70])
t.setStyle(TableStyle([
("BACKGROUND", (0, 0), (-1, 0), NAVY),
("TEXTCOLOR", (0, 0), (-1, 0), WHITE),
("FONTNAME", (0, 0), (-1, 0), "Montserrat-SemiBold"),
("FONTNAME", (0, 1), (-1, -1), "Montserrat"),
("FONTSIZE", (0, 0), (-1, -1), 9),
("GRID", (0, 0), (-1, -1), 0.5, colors.HexColor("#dddddd")),
("ROWBACKGROUNDS", (0, 1), (-1, -1), [WHITE, LIGHT_GRAY]),
("ALIGN", (0, 0), (-1, -1), "CENTER"),
("TOPPADDING", (0, 0), (-1, -1), 5),
("BOTTOMPADDING", (0, 0), (-1, -1), 5),
]))
elements.append(t)
# ─── Hotspots Page ───────────────────────────────────────
if analysis.hotspots:
elements.append(PageBreak())
elements.append(Paragraph("Attention Hotspots", styles["heading"]))
elements.append(
Paragraph(
"The top regions ranked by predicted attention intensity. Higher rank "
"means more predicted visual attention.",
styles["body"],
)
)
elements.append(Spacer(1, 8))
hs_data = [["Rank", "Position (x, y)", "Intensity"]]
for hs in analysis.hotspots:
cx = hs.get("center_x", hs["x"])
cy = hs.get("center_y", hs["y"])
hs_data.append([
f"#{hs['rank']}",
f"({cx}, {cy})",
f"{hs['intensity']:.2%}",
])
t = Table(hs_data, colWidths=[50, 120, 80])
t.setStyle(TableStyle([
("BACKGROUND", (0, 0), (-1, 0), NAVY),
("TEXTCOLOR", (0, 0), (-1, 0), WHITE),
("FONTNAME", (0, 0), (-1, 0), "Montserrat-SemiBold"),
("FONTNAME", (0, 1), (-1, -1), "Montserrat"),
("FONTSIZE", (0, 0), (-1, -1), 9),
("GRID", (0, 0), (-1, -1), 0.5, colors.HexColor("#dddddd")),
("ROWBACKGROUNDS", (0, 1), (-1, -1), [WHITE, LIGHT_GRAY]),
("ALIGN", (0, 0), (-1, -1), "CENTER"),
("TOPPADDING", (0, 0), (-1, -1), 5),
("BOTTOMPADDING", (0, 0), (-1, -1), 5),
]))
elements.append(t)
# ─── Insights Page ───────────────────────────────────────
has_rule = rule_insights and len(rule_insights) > 0
has_ai = ai_insights and len(ai_insights) > 0
if has_rule or has_ai:
elements.append(PageBreak())
elements.append(Paragraph("Analysis Insights", styles["heading"]))
if has_rule:
elements.append(Paragraph("Rule-Based Insights", styles["subheading"]))
elements.append(
Paragraph(
"Automatically generated observations based on the attention metrics.",
styles["body_small"],
)
)
elements.append(Spacer(1, 6))
elements.extend(_build_insight_table(rule_insights, styles, is_ai=False))
elements.append(Spacer(1, 12))
if has_ai:
elements.append(Paragraph("AI Design Analysis", styles["subheading"]))
ai_note = "Generated by Claude Sonnet 4.6 — context-aware design recommendations."
if ai_cost_usd is not None:
ai_note += f" (Cost: ${ai_cost_usd:.4f})"
elements.append(Paragraph(ai_note, styles["body_small"]))
elements.append(Spacer(1, 6))
elements.extend(_build_insight_table(ai_insights, styles, is_ai=True))
# ─── AOI Page (if any) ───────────────────────────────────
if aois:
elements.append(PageBreak())
elements.append(Paragraph("Areas of Interest", styles["heading"]))
elements.append(
Paragraph(
"User-defined regions analyzed for predicted attention capture. "
"Density > 1.0 means the region captures more attention than its "
"size would suggest.",
styles["body"],
)
)
elements.append(Spacer(1, 8))
aoi_data = [["Region", "Attention %", "Area %", "Density"]]
for aoi in aois:
aoi_data.append([
aoi.label,
f"{aoi.attention_pct:.1f}%",
f"{aoi.area_pct:.1f}%",
f"{aoi.attention_density:.2f}x",
])
t = Table(aoi_data, colWidths=[100, 70, 70, 70])
t.setStyle(TableStyle([
("BACKGROUND", (0, 0), (-1, 0), NAVY),
("TEXTCOLOR", (0, 0), (-1, 0), WHITE),
("FONTNAME", (0, 0), (-1, 0), "Montserrat-SemiBold"),
("FONTNAME", (0, 1), (-1, -1), "Montserrat"),
("FONTSIZE", (0, 0), (-1, -1), 9),
("GRID", (0, 0), (-1, -1), 0.5, colors.HexColor("#dddddd")),
("ROWBACKGROUNDS", (0, 1), (-1, -1), [WHITE, LIGHT_GRAY]),
("ALIGN", (1, 0), (-1, -1), "CENTER"),
("TOPPADDING", (0, 0), (-1, -1), 5),
("BOTTOMPADDING", (0, 0), (-1, -1), 5),
]))
elements.append(t)
# ─── Footer ──────────────────────────────────────────────
elements.append(Spacer(1, 30))
elements.append(
Paragraph(
"Generated by OliVAS — Open-Source Visual Attention Software by OLIVER",
styles["footer"],
)
)
doc.build(elements)
return buffer.getvalue()
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