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Felix Schulze
2025-12-13 17:29:56 +01:00
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# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
.venv/
venv/
ENV/
.eggs/
*.egg-info/
.mypy_cache/
.pytest_cache/
# Generated reports
rapport_*.pdf
rapport/
# Unlighthouse
.unlighthouse/
node_modules/
# IDE & editors
.idea/
.vscode/
*.swp
*.swo
*~
.DS_Store
webbanalys.json

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GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
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lib/__init__.py Normal file
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# Webbplatsanalys Report Generator Library

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lib/analyzer.py Normal file
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"""
Data analysis module for Unlighthouse reports.
"""
import json
from pathlib import Path
from collections import defaultdict
def load_ci_result(ci_result_path: Path) -> dict:
"""Load the main CI result JSON."""
with open(ci_result_path, "r", encoding="utf-8") as f:
return json.load(f)
def load_all_audits(reports_dir: Path, site_url: str = ""):
"""
Load ALL detailed audit data from lighthouse.json files.
Returns:
tuple: (all_audits, resource_issues, element_issues, page_count)
"""
all_audits = defaultdict(lambda: {
"count": 0, "pages": [], "score_sum": 0, "title": "", "description": "",
"total_wasted_bytes": 0, "display_values": []
})
resource_issues = defaultdict(lambda: {
"count": 0, "wasted_bytes": 0, "total_bytes": 0, "pages": []
})
element_issues = defaultdict(lambda: {
"count": 0, "audit": "", "pages": []
})
page_count = 0
site_domain = site_url.replace("https://", "").replace("http://", "").split("/")[0]
for report_dir in reports_dir.iterdir():
if not report_dir.is_dir():
continue
lighthouse_json = report_dir / "lighthouse.json"
if not lighthouse_json.exists():
continue
try:
with open(lighthouse_json, "r", encoding="utf-8") as f:
report = json.load(f)
page_path = report.get("finalDisplayedUrl", report_dir.name)
page_path_short = page_path.replace(site_url, "") or "/"
audits = report.get("audits", {})
page_count += 1
for audit_id, audit_data in audits.items():
score = audit_data.get("score")
if score is None:
continue
if score < 1:
all_audits[audit_id]["count"] += 1
all_audits[audit_id]["title"] = audit_data.get("title", audit_id)
all_audits[audit_id]["description"] = audit_data.get("description", "")
all_audits[audit_id]["score_sum"] += score
if len(all_audits[audit_id]["pages"]) < 20:
all_audits[audit_id]["pages"].append(page_path_short)
display_val = audit_data.get("displayValue", "")
if display_val:
all_audits[audit_id]["display_values"].append(display_val)
details = audit_data.get("details", {})
items = details.get("items", [])
for item in items:
if not isinstance(item, dict):
continue
url = item.get("url", "")
if url and site_domain and site_domain in url:
url_short = url.replace(site_url, "")
key = (audit_id, url_short)
resource_issues[key]["count"] += 1
resource_issues[key]["wasted_bytes"] += item.get("wastedBytes", 0)
resource_issues[key]["total_bytes"] += item.get("totalBytes", 0)
if len(resource_issues[key]["pages"]) < 5:
resource_issues[key]["pages"].append(page_path_short)
node = item.get("node", {})
snippet = node.get("snippet", "") if isinstance(node, dict) else str(node) if node else ""
if snippet:
key = (audit_id, snippet[:80])
element_issues[key]["count"] += 1
element_issues[key]["audit"] = audit_id
if len(element_issues[key]["pages"]) < 5:
element_issues[key]["pages"].append(page_path_short)
wasted = item.get("wastedBytes", 0)
if wasted:
all_audits[audit_id]["total_wasted_bytes"] += wasted
except Exception:
continue
return dict(all_audits), dict(resource_issues), dict(element_issues), page_count
def format_bytes(bytes_val: int) -> str:
"""Format bytes to human readable."""
if bytes_val >= 1024 * 1024:
return f"{bytes_val / (1024*1024):.1f} MB"
elif bytes_val >= 1024:
return f"{bytes_val / 1024:.0f} KB"
return f"{bytes_val} B"

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"""
Chart generation module for website analysis reports.
Uses consultancy brand colors: lime-500 and lime-900.
"""
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import io
# Consultancy brand colors (lime palette)
BRAND = {
"primary": "#84cc16", # lime-500 (oklch 76.8% 0.233 130.85)
"primary_dark": "#365314", # lime-900 (oklch 40.5% 0.101 131.063)
"accent": "#a3e635", # lime-400
"dark": "#1a2e05", # lime-950
}
# Score colors (traffic light)
SCORE_COLORS = {
"excellent": "#22c55e", # green-500
"needs_work": "#f59e0b", # amber-500
"poor": "#ef4444", # red-500
}
# UI colors
UI = {
"text": "#1f2937", # gray-800
"muted": "#6b7280", # gray-500
"light_bg": "#f9fafb", # gray-50
"border": "#e5e7eb", # gray-200
"white": "#ffffff",
}
# Swedish category names
CATEGORY_NAMES_SV = {
"performance": "Prestanda",
"accessibility": "Tillgänglighet",
"best-practices": "Bästa praxis",
"seo": "Sökmotoroptimering (SEO)",
}
def get_score_color(score: float) -> str:
"""Get color based on score (0-100)."""
if score >= 90:
return SCORE_COLORS["excellent"]
elif score >= 50:
return SCORE_COLORS["needs_work"]
return SCORE_COLORS["poor"]
def create_gauge_chart(score: float, label: str) -> io.BytesIO:
"""Create a semi-circular gauge chart with brand colors."""
fig, ax = plt.subplots(figsize=(6, 6), dpi=150)
color = get_score_color(score)
# Background arc
theta_bg = np.linspace(np.pi, 0, 100)
r = 0.75
ax.plot(r * np.cos(theta_bg), r * np.sin(theta_bg),
color=UI["border"], linewidth=25, solid_capstyle="round", zorder=1)
# Score arc
score_ratio = score / 100
theta_score = np.linspace(np.pi, np.pi - (np.pi * score_ratio), 100)
ax.plot(r * np.cos(theta_score), r * np.sin(theta_score),
color=color, linewidth=25, solid_capstyle="round", zorder=2)
# Score text
ax.text(0, 0.05, f"{int(score)}", fontsize=56, fontweight="bold",
ha="center", va="center", color=color)
ax.text(0, -0.4, label, fontsize=16, ha="center", va="center",
color=UI["muted"])
# Scale markers
for val in [0, 50, 100]:
angle = np.pi - (np.pi * val / 100)
x = (r + 0.15) * np.cos(angle)
y = (r + 0.15) * np.sin(angle)
ax.text(x, y, str(val), fontsize=12, ha="center", va="center",
color=UI["muted"])
ax.set_xlim(-1.1, 1.1)
ax.set_ylim(-0.6, 1.1)
ax.axis("off")
ax.set_aspect("equal")
buf = io.BytesIO()
plt.savefig(buf, format="png", bbox_inches="tight", transparent=True, dpi=150)
plt.close()
buf.seek(0)
return buf
def create_overview_chart(categories: dict, category_names: dict = None) -> io.BytesIO:
"""Create main overview chart with 0-100 scale."""
if category_names is None:
category_names = CATEGORY_NAMES_SV
fig, ax = plt.subplots(figsize=(10, 4), dpi=150)
names = [category_names.get(k, k) for k in categories.keys()]
scores = [v["averageScore"] * 100 for v in categories.values()]
y_pos = np.arange(len(names))
# Background bars (100%)
ax.barh(y_pos, [100] * len(names), color=UI["border"], height=0.5, zorder=1)
# Score bars with traffic light colors
colors_list = [get_score_color(s) for s in scores]
bars = ax.barh(y_pos, scores, color=colors_list, height=0.5, zorder=2)
ax.set_yticks(y_pos)
ax.set_yticklabels(names, fontsize=11, color=UI["text"])
ax.set_xlim(0, 100)
ax.set_xlabel("Poäng (0-100)", fontsize=11, color=UI["muted"])
for i, (bar, score) in enumerate(zip(bars, scores)):
ax.text(score + 2, i, f"{score:.0f}", va="center", fontsize=11,
fontweight="bold", color=UI["text"])
# Threshold lines
ax.axvline(x=90, color=SCORE_COLORS["excellent"], linestyle="--", alpha=0.5, linewidth=1)
ax.axvline(x=50, color=SCORE_COLORS["needs_work"], linestyle="--", alpha=0.5, linewidth=1)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_color(UI["border"])
ax.spines["bottom"].set_color(UI["border"])
plt.tight_layout()
buf = io.BytesIO()
plt.savefig(buf, format="png", bbox_inches="tight", facecolor="white", dpi=150)
plt.close()
buf.seek(0)
return buf
def create_issues_chart(all_audits: dict, total_pages: int, top_n: int = 12) -> io.BytesIO:
"""Create horizontal bar chart of most common issues."""
fig, ax = plt.subplots(figsize=(10, 6), dpi=150)
sorted_audits = sorted(all_audits.items(), key=lambda x: x[1]["count"], reverse=True)[:top_n]
def wrap_title(t, max_len=35):
if len(t) <= max_len:
return t
words = t.split()
lines, current = [], ""
for w in words:
if len(current + " " + w) <= max_len:
current = (current + " " + w).strip()
else:
if current:
lines.append(current)
current = w
if current:
lines.append(current)
return "\n".join(lines[:2])
names = [wrap_title(a[1]["title"]) for a in sorted_audits]
counts = [a[1]["count"] for a in sorted_audits]
percentages = [(c / total_pages) * 100 for c in counts]
y_pos = np.arange(len(names))
# Background bars
ax.barh(y_pos, [100] * len(names), color=UI["border"], height=0.6, zorder=1)
# Issue bars with severity colors
colors_list = [SCORE_COLORS["poor"] if p > 80 else SCORE_COLORS["needs_work"] if p > 50
else BRAND["primary"] for p in percentages]
bars = ax.barh(y_pos, percentages, color=colors_list, height=0.6, zorder=2)
ax.set_yticks(y_pos)
ax.set_yticklabels(names, fontsize=9, color=UI["text"])
ax.set_xlim(0, 100)
ax.set_xlabel("Andel av sidor med problem (%)", fontsize=10, color=UI["muted"])
# Add labels - bars and data are in same order
for i, (pct, cnt) in enumerate(zip(percentages, counts)):
ax.text(pct + 2, i, f"{pct:.0f}% ({cnt})", va="center", fontsize=9, color=UI["text"])
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_color(UI["border"])
ax.spines["bottom"].set_color(UI["border"])
plt.tight_layout()
buf = io.BytesIO()
plt.savefig(buf, format="png", bbox_inches="tight", facecolor="white", dpi=150)
plt.close()
buf.seek(0)
return buf
def create_savings_chart(all_audits: dict) -> io.BytesIO:
"""Create chart showing potential savings in MB."""
audits_with_savings = [(k, v) for k, v in all_audits.items() if v["total_wasted_bytes"] > 10000]
audits_with_savings.sort(key=lambda x: x[1]["total_wasted_bytes"], reverse=True)
audits_with_savings = audits_with_savings[:8]
if not audits_with_savings:
return None
fig, ax = plt.subplots(figsize=(9, 4), dpi=150)
def wrap_title(t, max_len=30):
return t[:max_len] + "..." if len(t) > max_len else t
names = [wrap_title(a[1]["title"]) for a in audits_with_savings]
savings_mb = [a[1]["total_wasted_bytes"] / (1024 * 1024) for a in audits_with_savings]
y_pos = np.arange(len(names))
bars = ax.barh(y_pos, savings_mb, color=SCORE_COLORS["needs_work"], height=0.6)
ax.set_yticks(y_pos)
ax.set_yticklabels(names, fontsize=9, color=UI["text"])
ax.set_xlabel("Potentiell besparing (MB)", fontsize=10, color=UI["muted"])
# Place labels at end of each bar
max_val = max(savings_mb) if savings_mb else 1
for i, s in enumerate(savings_mb):
ax.text(s + max_val * 0.02, i, f"{s:.1f} MB", va="center", fontsize=9, color=UI["text"])
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_color(UI["border"])
ax.spines["bottom"].set_color(UI["border"])
plt.tight_layout()
buf = io.BytesIO()
plt.savefig(buf, format="png", bbox_inches="tight", facecolor="white", dpi=150)
plt.close()
buf.seek(0)
return buf
def create_score_histogram(routes: list, category_names: dict = None) -> io.BytesIO:
"""Create 4 histograms showing score distribution (0-100 scale)."""
if category_names is None:
category_names = CATEGORY_NAMES_SV
fig, axes = plt.subplots(2, 2, figsize=(10, 7), dpi=150)
categories = ["performance", "accessibility", "best-practices", "seo"]
titles = [category_names.get(c, c) for c in categories]
for ax, cat, title in zip(axes.flatten(), categories, titles):
scores = [r.get("categories", {}).get(cat, {}).get("score", 0) * 100 for r in routes]
bins = np.arange(0, 105, 10)
n, bins_out, patches = ax.hist(scores, bins=bins, edgecolor="white", alpha=0.9)
for patch in patches:
bin_center = patch.get_x() + patch.get_width() / 2
patch.set_facecolor(get_score_color(bin_center))
avg = np.mean(scores)
ax.axvline(x=avg, color=BRAND["primary_dark"], linestyle="--", linewidth=2)
ax.set_xlim(0, 100)
ax.set_xlabel("Poäng", fontsize=9, color=UI["muted"])
ax.set_ylabel("Antal sidor", fontsize=9, color=UI["muted"])
ax.set_title(f"{title} (medel: {avg:.0f})", fontsize=11, fontweight="bold", color=UI["text"])
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_color(UI["border"])
ax.spines["bottom"].set_color(UI["border"])
plt.tight_layout()
buf = io.BytesIO()
plt.savefig(buf, format="png", bbox_inches="tight", facecolor="white", dpi=150)
plt.close()
buf.seek(0)
return buf
def create_core_web_vitals_chart(metrics: dict) -> io.BytesIO:
"""Create Core Web Vitals visualization."""
fig, axes = plt.subplots(1, 3, figsize=(10, 3), dpi=150)
vitals = [
("LCP", metrics.get("largest-contentful-paint", {}).get("averageNumericValue", 0) / 1000,
"s", 2.5, 4.0),
("CLS", metrics.get("cumulative-layout-shift", {}).get("averageNumericValue", 0),
"", 0.1, 0.25),
("FCP", metrics.get("first-contentful-paint", {}).get("averageNumericValue", 0) / 1000,
"s", 1.8, 3.0),
]
for ax, (abbr, value, unit, good, bad) in zip(axes, vitals):
if value <= good:
color = SCORE_COLORS["excellent"]
status = "BRA"
elif value <= bad:
color = SCORE_COLORS["needs_work"]
status = "BEHÖVER ARBETE"
else:
color = SCORE_COLORS["poor"]
status = "DÅLIGT"
ax.text(0.5, 0.65, f"{value:.2f}{unit}", fontsize=28, fontweight="bold",
ha="center", va="center", transform=ax.transAxes, color=color)
ax.text(0.5, 0.35, abbr, fontsize=14, ha="center", va="center",
transform=ax.transAxes, color=UI["muted"])
ax.text(0.5, 0.15, status, fontsize=10, ha="center", va="center",
transform=ax.transAxes, color=color, fontweight="bold")
ax.text(0.5, 0.02, f"(bra: ≤{good}{unit})", fontsize=8, ha="center", va="center",
transform=ax.transAxes, color=UI["muted"])
ax.axis("off")
plt.tight_layout()
buf = io.BytesIO()
plt.savefig(buf, format="png", bbox_inches="tight", facecolor="white", dpi=150)
plt.close()
buf.seek(0)
return buf

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"""
PDF report generation module with brand styling.
"""
from datetime import datetime
from pathlib import Path
from xml.sax.saxutils import escape as xml_escape
from collections import defaultdict
from reportlab.lib import colors
from reportlab.lib.pagesizes import A4
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm
from reportlab.platypus import (
SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
PageBreak, Image
)
from reportlab.lib.enums import TA_CENTER, TA_JUSTIFY, TA_RIGHT
from .charts import (
BRAND, SCORE_COLORS, UI, CATEGORY_NAMES_SV,
create_gauge_chart, create_overview_chart, create_issues_chart,
create_savings_chart, create_score_histogram, create_core_web_vitals_chart
)
from .analyzer import format_bytes
# Audit groups for categorization
AUDIT_GROUPS = {
"images": {
"title": "Bildoptimering",
"audits": ["modern-image-formats", "uses-responsive-images", "unsized-images",
"offscreen-images", "uses-optimized-images", "lcp-lazy-loaded",
"image-size-responsive"],
"onetime": True,
"description": "Bildrelaterade problem – åtgärdas med optimeringsverktyg och temaändringar."
},
"css_js": {
"title": "CSS & JavaScript",
"audits": ["unused-css-rules", "unused-javascript", "unminified-css",
"unminified-javascript", "render-blocking-resources", "legacy-javascript",
"duplicated-javascript", "bootup-time", "mainthread-work-breakdown"],
"onetime": True,
"description": "Oanvänd och blockerande kod – optimeras på temanivå med cachingplugins."
},
"caching": {
"title": "Caching & Leverans",
"audits": ["uses-long-cache-ttl", "uses-text-compression", "uses-http2",
"server-response-time", "total-byte-weight"],
"onetime": True,
"description": "Server- och cachingkonfiguration – kräver serverinställningar och CDN."
},
"accessibility": {
"title": "Tillgänglighetsproblem",
"audits": ["color-contrast", "link-name", "button-name", "image-alt",
"meta-viewport", "bypass", "html-has-lang", "document-title",
"heading-order", "label", "aria-"],
"onetime": False,
"description": "Tillgänglighetsproblem som hindrar användare med funktionsnedsättning."
},
"seo": {
"title": "SEO-problem",
"audits": ["meta-description", "link-text", "crawlable-anchors",
"robots-txt", "canonical", "hreflang", "structured-data"],
"onetime": False,
"description": "Sökmotoroptimeringsproblem som påverkar ranking och synlighet."
},
"errors": {
"title": "Fel & Varningar",
"audits": ["errors-in-console", "deprecations", "inspector-issues"],
"onetime": True,
"description": "JavaScript-fel och föråldrade API:er som indikerar teknisk skuld."
},
"layout": {
"title": "Layout & CLS",
"audits": ["cumulative-layout-shift", "dom-size", "largest-contentful-paint-element",
"layout-shift-elements", "long-tasks"],
"onetime": False,
"description": "Layout-stabilitet och renderingsprestanda – kritiskt för UX."
}
}
def get_styles():
"""Get PDF styles with brand colors."""
styles = getSampleStyleSheet()
brand_primary = colors.HexColor(BRAND["primary"])
brand_dark = colors.HexColor(BRAND["primary_dark"])
text_color = colors.HexColor(UI["text"])
muted_color = colors.HexColor(UI["muted"])
return {
"title": ParagraphStyle(
"Title", parent=styles["Heading1"], fontSize=28, spaceAfter=15,
alignment=TA_CENTER, textColor=brand_dark
),
"h1": ParagraphStyle(
"H1", parent=styles["Heading1"], fontSize=16, spaceBefore=12,
spaceAfter=8, textColor=brand_dark
),
"h2": ParagraphStyle(
"H2", parent=styles["Heading2"], fontSize=12, spaceBefore=10,
spaceAfter=5, textColor=text_color
),
"h3": ParagraphStyle(
"H3", parent=styles["Heading3"], fontSize=10, spaceBefore=6,
spaceAfter=3, textColor=text_color
),
"body": ParagraphStyle(
"Body", parent=styles["Normal"], fontSize=9, spaceAfter=5,
alignment=TA_JUSTIFY, leading=12, textColor=text_color
),
"body_bold": ParagraphStyle(
"BodyBold", parent=styles["Normal"], fontSize=9, spaceAfter=5,
alignment=TA_JUSTIFY, leading=12, fontName="Helvetica-Bold",
textColor=text_color
),
"small": ParagraphStyle(
"Small", parent=styles["Normal"], fontSize=8,
textColor=muted_color, leading=10, spaceAfter=3
),
"alert": ParagraphStyle(
"Alert", parent=styles["Normal"], fontSize=10,
textColor=colors.HexColor(SCORE_COLORS["poor"]), fontName="Helvetica-Bold"
),
"warning": ParagraphStyle(
"Warning", parent=styles["Normal"], fontSize=10,
textColor=colors.HexColor(SCORE_COLORS["needs_work"]), fontName="Helvetica-Bold"
),
"footer": ParagraphStyle(
"Footer", parent=styles["Normal"], fontSize=8,
textColor=muted_color, alignment=TA_RIGHT
),
}
def generate_pdf(
output_path: Path,
data: dict,
all_audits: dict,
resource_issues: dict,
element_issues: dict,
page_count: int,
customer_name: str = "",
site_url: str = "",
logo_path: Path = None,
consultant_name: str = "",
category_names: dict = None
):
"""Generate comprehensive PDF report with branding."""
if category_names is None:
category_names = CATEGORY_NAMES_SV
site_display = site_url.replace("https://", "").replace("http://", "").rstrip("/")
doc = SimpleDocTemplate(
str(output_path), pagesize=A4,
rightMargin=1.8*cm, leftMargin=1.8*cm, topMargin=1.8*cm, bottomMargin=2*cm,
)
s = get_styles()
story = []
brand_primary = colors.HexColor(BRAND["primary"])
brand_dark = colors.HexColor(BRAND["primary_dark"])
border_color = colors.HexColor(UI["border"])
light_bg = colors.HexColor(UI["light_bg"])
# ===== TITLE PAGE =====
story.append(Spacer(1, 1*cm))
# Logo
if logo_path and logo_path.exists():
try:
story.append(Image(str(logo_path), width=3*cm, height=3*cm))
story.append(Spacer(1, 0.5*cm))
except Exception:
pass
story.append(Paragraph("Webbplatsanalys", s["title"]))
story.append(Paragraph(site_display, ParagraphStyle(
"Sub", parent=s["title"], fontSize=20, textColor=colors.HexColor(UI["muted"])
)))
if customer_name:
story.append(Spacer(1, 0.3*cm))
story.append(Paragraph(f"Kund: {customer_name}", ParagraphStyle(
"Customer", parent=s["body"], fontSize=11, alignment=TA_CENTER
)))
story.append(Spacer(1, 1*cm))
# Overall score gauge
overall = data["summary"]["score"] * 100
gauge = create_gauge_chart(overall, "Övergripande betyg")
story.append(Image(gauge, width=10*cm, height=8*cm))
story.append(Spacer(1, 0.5*cm))
# Status assessment
if overall < 50:
story.append(Paragraph(
"Webbplatsen har betydande prestandaproblem som kräver omedelbar åtgärd.",
s["alert"]))
elif overall < 75:
story.append(Paragraph(
"Webbplatsen har flera förbättringsområden som påverkar användarupplevelse och SEO.",
s["warning"]))
story.append(Spacer(1, 0.5*cm))
# Key stats table
stats_data = [
["Analyserade sidor", str(page_count)],
["Identifierade problemtyper", str(len(all_audits))],
["Resursproblem", str(len(resource_issues))],
["Elementproblem", str(len(element_issues))],
["Rapport genererad", datetime.now().strftime("%Y-%m-%d")],
]
stats_table = Table(stats_data, colWidths=[8*cm, 5*cm])
stats_table.setStyle(TableStyle([
("FONTSIZE", (0, 0), (-1, -1), 10),
("ALIGN", (1, 0), (1, -1), "RIGHT"),
("TEXTCOLOR", (0, 0), (0, -1), colors.HexColor(UI["muted"])),
("TEXTCOLOR", (1, 0), (1, -1), brand_dark),
("FONTNAME", (1, 0), (1, -1), "Helvetica-Bold"),
("LINEBELOW", (0, -1), (-1, -1), 1, brand_primary),
]))
story.append(stats_table)
if consultant_name:
story.append(Spacer(1, 0.5*cm))
story.append(Paragraph(f"Utförd av: {consultant_name}", s["footer"]))
story.append(PageBreak())
# ===== EXECUTIVE SUMMARY =====
story.append(Paragraph("Sammanfattning", s["h1"]))
summary_text = f"""
Denna rapport presenterar resultatet av en teknisk analys av webbplatsen {site_display}.
Totalt har <b>{page_count} sidor</b> granskats med avseende på prestanda, tillgänglighet,
bästa praxis och sökmotoroptimering.
"""
story.append(Paragraph(summary_text, s["body"]))
# Performance summary
perf_score = data["summary"]["categories"]["performance"]["averageScore"] * 100
a11y_score = data["summary"]["categories"]["accessibility"]["averageScore"] * 100
bp_score = data["summary"]["categories"]["best-practices"]["averageScore"] * 100
seo_score = data["summary"]["categories"]["seo"]["averageScore"] * 100
findings = []
if perf_score < 70:
findings.append(f"<b>Prestanda ({perf_score:.0f}%)</b>: Problem med laddningstider och rendering.")
if a11y_score < 90:
findings.append(f"<b>Tillgänglighet ({a11y_score:.0f}%)</b>: Brister som påverkar användare.")
if bp_score < 80:
findings.append(f"<b>Bästa praxis ({bp_score:.0f}%)</b>: Tekniska brister.")
if seo_score < 90:
findings.append(f"<b>SEO ({seo_score:.0f}%)</b>: Optimeringsmöjligheter.")
if findings:
story.append(Paragraph("<b>Huvudsakliga fynd:</b>", s["body"]))
for f in findings:
story.append(Paragraph(f"• {f}", s["body"]))
story.append(Spacer(1, 0.3*cm))
# Category chart
cat_chart = create_overview_chart(data["summary"]["categories"], category_names)
story.append(Image(cat_chart, width=16*cm, height=6.5*cm))
story.append(Spacer(1, 0.3*cm))
# Category table
cat_table_data = [["Kategori", "Medel", "Lägsta", "Högsta", "Bedömning"]]
for cat_id, cat_info in data["summary"]["categories"].items():
avg = cat_info["averageScore"] * 100
scores = cat_info["scores"]
min_s, max_s = min(scores) * 100, max(scores) * 100
if avg >= 90:
status = "✓ Godkänt"
elif avg >= 50:
status = "⚠ Kräver åtgärd"
else:
status = "✗ Kritiskt"
cat_table_data.append([
category_names.get(cat_id, cat_id),
f"{avg:.0f}%", f"{min_s:.0f}%", f"{max_s:.0f}%", status
])
t = Table(cat_table_data, colWidths=[5*cm, 2*cm, 2*cm, 2*cm, 3*cm])
t.setStyle(TableStyle([
("BACKGROUND", (0, 0), (-1, 0), brand_dark),
("TEXTCOLOR", (0, 0), (-1, 0), colors.white),
("ALIGN", (1, 0), (-1, -1), "CENTER"),
("FONTSIZE", (0, 0), (-1, -1), 9),
("GRID", (0, 0), (-1, -1), 0.5, border_color),
("ROWBACKGROUNDS", (0, 1), (-1, -1), [colors.white, light_bg])
]))
story.append(t)
story.append(PageBreak())
# ===== CORE WEB VITALS =====
story.append(Paragraph("Core Web Vitals", s["h1"]))
story.append(Paragraph(
"Core Web Vitals är Googles officiella mätvärden för användarupplevelse. "
"Dessa påverkar direkt webbplatsens ranking i sökresultat.", s["body"]))
cwv_chart = create_core_web_vitals_chart(data["summary"]["metrics"])
story.append(Image(cwv_chart, width=16*cm, height=5*cm))
# Metrics table
metrics_data = [["Mätvärde", "Värde", "Bra", "Dåligt", "Status"]]
metric_info = [
("LCP (Largest Contentful Paint)", "largest-contentful-paint", 1000, "s", 2.5, 4.0),
("FCP (First Contentful Paint)", "first-contentful-paint", 1000, "s", 1.8, 3.0),
("CLS (Cumulative Layout Shift)", "cumulative-layout-shift", 1, "", 0.1, 0.25),
("TBT (Total Blocking Time)", "total-blocking-time", 1, "ms", 200, 600),
("TTI (Time to Interactive)", "interactive", 1000, "s", 3.8, 7.3),
]
for name, key, divisor, unit, good, bad in metric_info:
val = data["summary"]["metrics"].get(key, {}).get("averageNumericValue", 0) / divisor
status = "✓" if val <= good else "⚠" if val <= bad else "✗"
metrics_data.append([name, f"{val:.2f}{unit}", f"≤{good}{unit}", f">{bad}{unit}", status])
mt = Table(metrics_data, colWidths=[6.5*cm, 2.5*cm, 2*cm, 2*cm, 1.5*cm])
mt.setStyle(TableStyle([
("BACKGROUND", (0, 0), (-1, 0), brand_dark),
("TEXTCOLOR", (0, 0), (-1, 0), colors.white),
("ALIGN", (1, 0), (-1, -1), "CENTER"),
("FONTSIZE", (0, 0), (-1, -1), 8),
("GRID", (0, 0), (-1, -1), 0.5, border_color),
("ROWBACKGROUNDS", (0, 1), (-1, -1), [colors.white, light_bg])
]))
story.append(mt)
story.append(PageBreak())
# ===== SCORE DISTRIBUTION =====
story.append(Paragraph("Poängfördelning per kategori", s["h1"]))
story.append(Paragraph(
f"Följande diagram visar hur de {page_count} sidorna presterar. "
"Majoriteten bör ligga i det gröna området (≥90).", s["body"]))
hist_chart = create_score_histogram(data.get("routes", []), category_names)
story.append(Image(hist_chart, width=16*cm, height=11*cm))
story.append(PageBreak())
# ===== MOST COMMON ISSUES =====
story.append(Paragraph("Vanligaste problemen", s["h1"]))
story.append(Paragraph(
"Problem som påverkar >80% av sidorna indikerar systemiska brister.", s["body"]))
issues_chart = create_issues_chart(all_audits, page_count, 12)
story.append(Image(issues_chart, width=16*cm, height=10*cm))
story.append(PageBreak())
# ===== POTENTIAL SAVINGS =====
story.append(Paragraph("Potentiella besparingar", s["h1"]))
total_savings = sum(a["total_wasted_bytes"] for a in all_audits.values())
story.append(Paragraph(
f"<b>Total potentiell besparing: {format_bytes(total_savings)}</b>", s["body_bold"]))
savings_chart = create_savings_chart(all_audits)
if savings_chart:
story.append(Image(savings_chart, width=15*cm, height=6*cm))
story.append(PageBreak())
# ===== DETAILED ISSUES =====
story.append(Paragraph("Detaljerade problem", s["h1"]))
for group_id, group_info in AUDIT_GROUPS.items():
group_audits = []
for audit_id, audit_data in all_audits.items():
for pattern in group_info["audits"]:
if pattern.endswith("-"):
if audit_id.startswith(pattern):
group_audits.append((audit_id, audit_data))
break
elif audit_id == pattern:
group_audits.append((audit_id, audit_data))
break
if not group_audits:
continue
group_audits.sort(key=lambda x: x[1]["count"], reverse=True)
total_in_group = sum(a[1]["count"] for a in group_audits)
if total_in_group == 0:
continue
label = "ENGÅNGSÅTGÄRD" if group_info["onetime"] else "SIDSPECIFIK"
story.append(Paragraph(
f"{group_info['title']} <font color='#{UI['muted'][1:]}'>[{label}]</font>", s["h2"]))
story.append(Paragraph(group_info["description"], s["small"]))
for audit_id, audit_data in group_audits[:8]:
if audit_data["count"] == 0:
continue
title = xml_escape(audit_data["title"][:65])
pct = (audit_data["count"] / page_count) * 100
story.append(Paragraph(f"<b>• {title}</b>", s["body"]))
details = [f"{audit_data['count']}/{page_count} sidor ({pct:.0f}%)"]
if audit_data["total_wasted_bytes"] > 1024:
details.append(f"Besparing: {format_bytes(audit_data['total_wasted_bytes'])}")
story.append(Paragraph(" " + " | ".join(details), s["small"]))
story.append(Spacer(1, 0.2*cm))
story.append(PageBreak())
# ===== RESOURCE ISSUES =====
story.append(Paragraph("Resurser som kräver åtgärd", s["h1"]))
resource_by_audit = defaultdict(list)
for (audit_id, url), rdata in resource_issues.items():
if rdata["count"] >= 10 and rdata["wasted_bytes"] > 5000:
resource_by_audit[audit_id].append((url, rdata))
for audit_id in ["unused-css-rules", "unused-javascript", "render-blocking-resources",
"modern-image-formats", "uses-long-cache-ttl"]:
resources = resource_by_audit.get(audit_id, [])
if not resources:
continue
audit_title = all_audits.get(audit_id, {}).get("title", audit_id)
story.append(Paragraph(f"<b>{xml_escape(audit_title[:50])}</b>", s["h3"]))
resources.sort(key=lambda x: x[1]["wasted_bytes"], reverse=True)
resource_table = [["Resurs", "Sidor", "Bortkastade data"]]
for url, rdata in resources[:8]:
url_display = "..." + url[-42:] if len(url) > 45 else url
resource_table.append([
url_display, str(rdata["count"]), format_bytes(rdata["wasted_bytes"])
])
if len(resource_table) > 1:
rt = Table(resource_table, colWidths=[9*cm, 2*cm, 3*cm])
rt.setStyle(TableStyle([
("BACKGROUND", (0, 0), (-1, 0), brand_dark),
("TEXTCOLOR", (0, 0), (-1, 0), colors.white),
("FONTSIZE", (0, 0), (-1, -1), 7),
("FONTNAME", (0, 1), (0, -1), "Courier"),
("ALIGN", (1, 0), (-1, -1), "CENTER"),
("GRID", (0, 0), (-1, -1), 0.5, border_color),
("ROWBACKGROUNDS", (0, 1), (-1, -1), [colors.white, light_bg])
]))
story.append(rt)
story.append(Spacer(1, 0.2*cm))
story.append(PageBreak())
# ===== WORST PAGES =====
story.append(Paragraph("Sidor som kräver mest arbete", s["h1"]))
routes = data.get("routes", [])
for cat_id, cat_name in [("performance", "Prestanda"), ("accessibility", "Tillgänglighet")]:
story.append(Paragraph(f"{cat_name} – sämsta 10", s["h2"]))
sorted_routes = sorted(
routes, key=lambda r: r.get("categories", {}).get(cat_id, {}).get("score", 1)
)[:10]
worst_table = [["Sida", "Poäng"]]
for route in sorted_routes:
score = route.get("categories", {}).get(cat_id, {}).get("score", 1) * 100
path = route.get("path", "/")
if len(path) > 50:
path = path[:47] + "..."
worst_table.append([path, f"{score:.0f}%"])
wt = Table(worst_table, colWidths=[12*cm, 2.5*cm])
wt.setStyle(TableStyle([
("BACKGROUND", (0, 0), (-1, 0), brand_dark),
("TEXTCOLOR", (0, 0), (-1, 0), colors.white),
("FONTSIZE", (0, 0), (-1, -1), 8),
("ALIGN", (1, 0), (1, -1), "CENTER"),
("GRID", (0, 0), (-1, -1), 0.5, border_color),
("ROWBACKGROUNDS", (0, 1), (-1, -1), [colors.white, light_bg])
]))
story.append(wt)
story.append(Spacer(1, 0.3*cm))
story.append(PageBreak())
# ===== RECOMMENDATIONS =====
story.append(Paragraph("Rekommenderade åtgärder", s["h1"]))
story.append(Paragraph("Fas 1: Engångsåtgärder", s["h2"]))
story.append(Paragraph("Åtgärdas en gång, förbättrar alla sidor automatiskt.", s["small"]))
phase1 = [
("Bildoptimering",
f"WebP-konvertering och komprimering. Besparing: {format_bytes(all_audits.get('modern-image-formats', {}).get('total_wasted_bytes', 0))}.",
"2-4 tim"),
("Caching och minifiering",
"Aktivera minifiering av CSS/JS, lazy loading och browser caching.",
"4-6 tim"),
("Render-blocking resurser",
"Kritisk CSS-inlining och uppskjuten JavaScript-laddning.",
"2-4 tim"),
("Console-fel",
f"Åtgärda JavaScript-fel på {all_audits.get('errors-in-console', {}).get('count', 0)} sidor.",
"2-4 tim"),
]
for title, desc, time in phase1:
story.append(Paragraph(f"<b>{title}</b> <font color='#{UI['muted'][1:]}'>[{time}]</font>", s["body"]))
story.append(Paragraph(desc, s["small"]))
story.append(Spacer(1, 0.3*cm))
story.append(Paragraph("Fas 2: Sidspecifika förbättringar", s["h2"]))
phase2 = [
("Färgkontrast",
f"{all_audits.get('color-contrast', {}).get('count', 0)} sidor har otillräcklig kontrast.",
f"{all_audits.get('color-contrast', {}).get('count', 0) * 15} min"),
("Länktexter",
f"{all_audits.get('link-name', {}).get('count', 0)} sidor har länkar utan beskrivande text.",
f"{all_audits.get('link-name', {}).get('count', 0) * 10} min"),
("Layout Shift (CLS)",
"Definiera dimensioner på bilder, undvik dynamiskt innehåll.",
"4-8 tim"),
]
for title, desc, time in phase2:
story.append(Paragraph(f"<b>{title}</b> <font color='#{UI['muted'][1:]}'>[{time}]</font>", s["body"]))
story.append(Paragraph(desc, s["small"]))
story.append(Spacer(1, 0.5*cm))
# Time estimate
story.append(Paragraph("Sammanfattning arbetsinsats", s["h2"]))
total_time_low = int(15 + page_count * 0.25)
total_time_high = int(28 + page_count * 0.3)
time_data = [
["Fas", "Åtgärd", "Tid", "Påverkan"],
["1", "Bildoptimering + CDN", "4-8 tim", f"{page_count} sidor"],
["1", "Caching & minifiering", "4-6 tim", f"{page_count} sidor"],
["1", "Grundläggande a11y", "1-2 tim", f"{page_count} sidor"],
["1", "Felsökning", "2-4 tim", f"{page_count} sidor"],
["2", "Färgkontrast & länkar", f"{int(page_count * 0.2)}-{int(page_count * 0.3)} tim", "Specifika sidor"],
["2", "CLS-optimering", "4-8 tim", "Berörda mallar"],
["", "TOTALT ESTIMAT", f"{total_time_low}-{total_time_high} tim", ""],
]
tt = Table(time_data, colWidths=[1.2*cm, 7*cm, 3*cm, 3*cm])
tt.setStyle(TableStyle([
("BACKGROUND", (0, 0), (-1, 0), brand_dark),
("TEXTCOLOR", (0, 0), (-1, 0), colors.white),
("FONTSIZE", (0, 0), (-1, -1), 9),
("ALIGN", (0, 0), (0, -1), "CENTER"),
("ALIGN", (2, 0), (3, -1), "CENTER"),
("GRID", (0, 0), (-1, -1), 0.5, border_color),
("ROWBACKGROUNDS", (0, 1), (-1, -2), [colors.white, light_bg]),
("BACKGROUND", (0, -1), (-1, -1), brand_primary),
("TEXTCOLOR", (0, -1), (-1, -1), colors.white),
("FONTNAME", (0, -1), (-1, -1), "Helvetica-Bold"),
("FONTSIZE", (0, -1), (-1, -1), 10),
]))
story.append(tt)
# Build PDF
doc.build(story)

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# Webbplatsanalys Report Generator
# Install: pip install -r requirements.txt
reportlab>=4.0
matplotlib>=3.7
numpy>=1.24
pillow>=9.0

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{ pkgs ? import <nixpkgs> {} }:
pkgs.mkShell {
buildInputs = with pkgs; [
python313
python313Packages.reportlab
python313Packages.matplotlib
python313Packages.numpy
python313Packages.pillow # For logo handling
python313Packages.cairosvg # For SVG logo conversion
];
shellHook = ''
echo "Webbplatsanalys redo. Kör: python webbanalys.py"
'';
}

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export default defineUnlighthouseConfig({
server: {
open: false,
},
})

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webbanalys.py Normal file
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#!/usr/bin/env python3
"""
╔═══════════════════════════════════════════════════════════════════════╗
║ WEBBPLATSANALYS RAPPORTGENERATOR ║
║ ║
║ Analysera webbplatser och generera professionella PDF-rapporter ║
╚═══════════════════════════════════════════════════════════════════════╝
Usage:
python webbanalys.py scan <url> # Run analysis
python webbanalys.py generate # Generate PDF report
python webbanalys.py run <url> # Scan + generate in one step
Requirements (NixOS):
nix-shell shell.nix
Requirements (pip):
pip install -r requirements.txt
"""
import argparse
import json
import subprocess
import shutil
import sys
from pathlib import Path
from datetime import datetime
# Ensure lib is importable
sys.path.insert(0, str(Path(__file__).parent))
from lib.analyzer import load_ci_result, load_all_audits
from lib.pdf_report import generate_pdf
BANNER = """
╔═══════════════════════════════════════════════════════════════════════╗
║ WEBBPLATSANALYS RAPPORTGENERATOR ║
╚═══════════════════════════════════════════════════════════════════════╝
"""
def load_config(config_path: Path) -> dict:
"""Load project configuration from JSON file."""
if config_path.exists():
with open(config_path, "r", encoding="utf-8") as f:
return json.load(f)
return {}
def save_config(config_path: Path, config: dict):
"""Save project configuration to JSON file."""
with open(config_path, "w", encoding="utf-8") as f:
json.dump(config, f, indent=2, ensure_ascii=False)
def find_data_files(base_path: Path) -> tuple[Path, Path]:
"""Find ci-result.json and reports directory."""
search_paths = [
base_path / "rapport",
base_path / ".unlighthouse",
base_path,
]
ci_result = None
reports_dir = None
for path in search_paths:
if not path.exists():
continue
candidate = path / "ci-result.json"
if candidate.exists():
ci_result = candidate
candidate = path / "reports"
if candidate.exists() and candidate.is_dir():
reports_dir = candidate
if ci_result and reports_dir:
break
return ci_result, reports_dir
def find_logo(base_path: Path) -> Path:
"""Find logo file in project."""
# Prefer PNG/JPG, then SVG
for pattern in ["*logo*.png", "*logo*.jpg", "*.png", "*.jpg"]:
for f in base_path.glob(pattern):
if f.is_file() and f.suffix.lower() in [".png", ".jpg", ".jpeg"]:
return f
# Check for SVG (will be converted)
for pattern in ["*logo*.svg", "*.svg"]:
for f in base_path.glob(pattern):
if f.is_file():
return f
return None
def convert_svg_to_png(svg_path: Path) -> Path:
"""Convert SVG to PNG for PDF embedding."""
try:
import cairosvg
png_path = svg_path.with_suffix(".png")
if not png_path.exists() or png_path.stat().st_mtime < svg_path.stat().st_mtime:
cairosvg.svg2png(url=str(svg_path), write_to=str(png_path), output_width=200)
return png_path
except ImportError:
return None
except Exception:
return None
def cmd_generate(args, base_path: Path):
"""Generate PDF report."""
config_path = base_path / "webbanalys.json"
config = load_config(config_path)
# Find data files
print("\n🔍 Söker efter analysdata...")
ci_result_path, reports_dir = find_data_files(base_path)
if not ci_result_path:
print("❌ Kunde inte hitta ci-result.json")
print(" Kör först: unlighthouse-ci --site <url>")
print(f" Sökte i: {base_path}")
sys.exit(1)
if not reports_dir:
print("❌ Kunde inte hitta reports/ katalog")
sys.exit(1)
print(f" ✓ Hittade data i: {ci_result_path.parent}")
# Determine parameters (CLI > config > default)
customer = args.customer or config.get("customer", "")
site_url = args.url or config.get("site_url", "")
consultant = args.consultant or config.get("consultant", "")
# Find logo
logo_path = None
if args.logo:
logo_path = Path(args.logo)
elif config.get("logo"):
logo_path = base_path / config["logo"]
else:
logo_path = find_logo(base_path)
# Convert SVG to PNG if needed
if logo_path and logo_path.exists():
if logo_path.suffix.lower() == ".svg":
png_path = convert_svg_to_png(logo_path)
if png_path:
logo_path = png_path
print(f" ✓ Logotyp: {logo_path.name} (konverterad från SVG)")
else:
print(f" ⚠ Kunde inte konvertera SVG-logotyp")
logo_path = None
else:
print(f" ✓ Logotyp: {logo_path.name}")
# Load data
print("\n📊 Laddar analysdata...")
data = load_ci_result(ci_result_path)
# Extract site URL from data if not provided
if not site_url:
routes = data.get("routes", [])
if routes:
first_path = routes[0].get("path", "")
# Try to construct from common patterns
for route in routes[:5]:
path = route.get("path", "")
if path.startswith("http"):
site_url = "/".join(path.split("/")[:3])
break
print(f" Webbplats: {site_url or '(okänd)'}")
if customer:
print(f" Kund: {customer}")
# Analyze
print("\n🔬 Analyserar sidor...")
all_audits, resource_issues, element_issues, page_count = load_all_audits(
reports_dir, site_url
)
print(f" Sidor analyserade: {page_count}")
print(f" Problemtyper: {len(all_audits)}")
print(f" Resursproblem: {len(resource_issues)}")
print(f" Elementproblem: {len(element_issues)}")
# Overall score
overall = data["summary"]["score"] * 100
if overall >= 90:
score_emoji = "🟢"
elif overall >= 50:
score_emoji = "🟡"
else:
score_emoji = "🔴"
print(f"\n Övergripande betyg: {score_emoji} {overall:.0f}/100")
# Determine output path
if args.output:
output_path = Path(args.output)
else:
date_str = datetime.now().strftime("%Y%m%d")
site_slug = site_url.replace("https://", "").replace("http://", "").replace("/", "_").replace(".", "_")
output_path = base_path / f"rapport_{site_slug}_{date_str}.pdf"
# Generate report
print(f"\n📝 Genererar rapport...")
generate_pdf(
output_path=output_path,
data=data,
all_audits=all_audits,
resource_issues=resource_issues,
element_issues=element_issues,
page_count=page_count,
customer_name=customer,
site_url=site_url,
logo_path=logo_path if logo_path and logo_path.exists() else None,
consultant_name=consultant
)
file_size = output_path.stat().st_size / 1024
print(f"\n✅ Rapport genererad!")
print(f" 📄 {output_path}")
print(f" 📦 {file_size:.0f} KB")
# Offer to save config
if not config and (customer or site_url):
print("\n💾 Spara inställningar för framtida rapporter? (y/n): ", end="")
try:
if input().lower() == "y":
new_config = {}
if customer:
new_config["customer"] = customer
if site_url:
new_config["site_url"] = site_url
if consultant:
new_config["consultant"] = consultant
if logo_path:
new_config["logo"] = str(logo_path.relative_to(base_path))
save_config(config_path, new_config)
print(f" ✓ Sparad till {config_path.name}")
except (EOFError, KeyboardInterrupt):
pass
def cmd_init(args, base_path: Path):
"""Initialize project configuration."""
config_path = base_path / "webbanalys.json"
print("\n🔧 Konfigurera projekt")
print(" (tryck Enter för att hoppa över)\n")
config = load_config(config_path)
try:
customer = input(f" Kundnamn [{config.get('customer', '')}]: ").strip()
if customer:
config["customer"] = customer
site_url = input(f" Webbplats URL [{config.get('site_url', '')}]: ").strip()
if site_url:
config["site_url"] = site_url
consultant = input(f" Konsultnamn [{config.get('consultant', '')}]: ").strip()
if consultant:
config["consultant"] = consultant
save_config(config_path, config)
print(f"\n✅ Konfiguration sparad till {config_path.name}")
except (EOFError, KeyboardInterrupt):
print("\n Avbrutet.")
def cmd_info(args, base_path: Path):
"""Show project information."""
config_path = base_path / "webbanalys.json"
config = load_config(config_path)
print("\n📋 Projektinformation")
print(f" Mapp: {base_path}")
if config:
print(f"\n Konfiguration ({config_path.name}):")
for key, value in config.items():
print(f" {key}: {value}")
else:
print(f"\n ⚠ Ingen konfiguration hittad")
print(f" Kör: python webbanalys.py init")
# Check for data
ci_result, reports_dir = find_data_files(base_path)
print(f"\n Analysdata:")
if ci_result:
print(f" ✓ ci-result.json: {ci_result}")
else:
print(f" ✗ ci-result.json saknas")
if reports_dir:
report_count = len(list(reports_dir.glob("*/lighthouse.json")))
print(f" ✓ reports/: {report_count} sidor")
else:
print(f" ✗ reports/ saknas")
# Check for logo
logo = find_logo(base_path)
if logo:
print(f" ✓ Logotyp: {logo.name}")
def cmd_scan(args, base_path: Path):
"""Run Unlighthouse scan."""
config_path = base_path / "webbanalys.json"
config = load_config(config_path)
# Determine URL
site_url = args.url or config.get("site_url", "")
if not site_url:
print("❌ Ingen URL angiven.")
print(" Använd: python webbanalys.py scan https://example.com")
print(" Eller kör 'python webbanalys.py init' först")
sys.exit(1)
# Ensure URL has protocol
if not site_url.startswith("http"):
site_url = "https://" + site_url
samples = args.samples or config.get("samples", 3)
output_path = base_path / "rapport"
print(f"\n🔍 Startar webbanalys...")
print(f" URL: {site_url}")
print(f" Samples: {samples}")
print(f" Output: {output_path}")
# Check for pnpm
pnpm_path = shutil.which("pnpm")
npx_path = shutil.which("npx")
if pnpm_path:
cmd = [
"pnpm", "dlx", "-p", "@unlighthouse/cli",
"unlighthouse-ci",
"--site", site_url,
"--samples", str(samples),
"--output-path", str(output_path),
"--reporter", "jsonExpanded"
]
elif npx_path:
cmd = [
"npx", "-y", "@unlighthouse/cli",
"unlighthouse-ci",
"--site", site_url,
"--samples", str(samples),
"--output-path", str(output_path),
"--reporter", "jsonExpanded"
]
else:
print("❌ Varken pnpm eller npx hittades.")
print(" Installera Node.js och pnpm/npm först.")
sys.exit(1)
print(f"\n Kör: {' '.join(cmd)}\n")
print("─" * 70)
try:
result = subprocess.run(cmd, cwd=base_path)
print("─" * 70)
if result.returncode == 0:
print("\n✅ Analys klar!")
# Update config with URL
if site_url and site_url != config.get("site_url"):
config["site_url"] = site_url
config["samples"] = samples
save_config(config_path, config)
return True
else:
print(f"\n❌ Analys misslyckades (kod {result.returncode})")
return False
except KeyboardInterrupt:
print("\n\n⚠ Avbruten av användaren")
return False
except Exception as e:
print(f"\n❌ Fel: {e}")
return False
def cmd_run(args, base_path: Path):
"""Run scan + generate in one step."""
# First scan
if cmd_scan(args, base_path):
print("\n" + "═" * 70 + "\n")
# Then generate
cmd_generate(args, base_path)
def main():
parser = argparse.ArgumentParser(
description="Analysera webbplatser och generera PDF-rapporter",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Kommandon:
scan Kör Unlighthouse-analys
generate Generera PDF-rapport från befintlig data
run Kör analys + generera rapport (allt-i-ett)
init Konfigurera projekt
info Visa projektinformation
Exempel:
python webbanalys.py scan https://example.com
python webbanalys.py scan -u https://example.com -s 5
python webbanalys.py generate -c "Företag AB"
python webbanalys.py run https://example.com -c "Företag AB"
"""
)
subparsers = parser.add_subparsers(dest="command", help="Kommando")
# Scan command
scan_parser = subparsers.add_parser("scan", help="Kör Unlighthouse-analys")
scan_parser.add_argument("url", nargs="?", help="Webbplats-URL")
scan_parser.add_argument("--samples", "-s", type=int, default=3, help="Antal samples per sida (default: 3)")
# Generate command
gen_parser = subparsers.add_parser("generate", help="Generera PDF-rapport")
gen_parser.add_argument("--customer", "-c", help="Kundnamn")
gen_parser.add_argument("--url", "-u", help="Webbplats-URL")
gen_parser.add_argument("--consultant", help="Konsultnamn")
gen_parser.add_argument("--logo", "-l", help="Sökväg till logotyp (PNG/SVG)")
gen_parser.add_argument("--output", "-o", help="Utdatafil")
# Run command (scan + generate)
run_parser = subparsers.add_parser("run", help="Analysera + generera rapport")
run_parser.add_argument("url", nargs="?", help="Webbplats-URL")
run_parser.add_argument("--samples", "-s", type=int, default=3, help="Antal samples per sida (default: 3)")
run_parser.add_argument("--customer", "-c", help="Kundnamn")
run_parser.add_argument("--consultant", help="Konsultnamn")
run_parser.add_argument("--logo", "-l", help="Sökväg till logotyp (PNG/SVG)")
run_parser.add_argument("--output", "-o", help="Utdatafil")
# Init command
subparsers.add_parser("init", help="Konfigurera projekt")
# Info command
subparsers.add_parser("info", help="Visa projektinformation")
args = parser.parse_args()
base_path = Path.cwd()
print(BANNER)
if args.command == "scan":
cmd_scan(args, base_path)
elif args.command == "generate":
cmd_generate(args, base_path)
elif args.command == "run":
cmd_run(args, base_path)
elif args.command == "init":
cmd_init(args, base_path)
elif args.command == "info":
cmd_info(args, base_path)
else:
# Default: show help
parser.print_help()
print("\n💡 Snabbstart:")
print(" python webbanalys.py run https://example.com -c 'Företag AB'")
if __name__ == "__main__":
main()