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scripts/reports/fetch_monthly_report_data.py

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+#!/usr/bin/env python3
+"""
+Pobiera dane z Google Ads API + GA4 za wskazany miesiąc i zapisuje jako JSON.
+
+Użycie:
+    python scripts/reports/fetch_monthly_report_data.py --customer studio-zoe.pl --month 2026-02
+    python scripts/reports/fetch_monthly_report_data.py --customer 3871661050 --month 2026-02 --output output/report.json
+"""
+
+import argparse
+import calendar
+import csv
+import json
+import os
+import re
+import sys
+import io
+import tomllib
+from datetime import datetime, timedelta
+from pathlib import Path
+from urllib.parse import parse_qs, urlparse
+
+sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace")
+sys.path.insert(0, str(Path(__file__).parent.parent))
+from lib.gads_client import get_client, get_customer_id, run_query
+import requests
+
+ROOT = Path(__file__).parent.parent.parent
+sys.path.insert(0, str(ROOT))
+from src.gads_v2.config import load_env
+
+load_env(ROOT / ".env")
+
+
+def load_client_report_config(domain):
+    """Load scalar report settings for a client from config/clients.toml."""
+    config_path = ROOT / "config" / "clients.toml"
+    if not config_path.exists():
+        return {}
+    data = tomllib.loads(config_path.read_text(encoding="utf-8"))
+    return data.get("clients", {}).get(domain, {})
+
+
+def parse_month(month_str):
+    """Parse YYYY-MM to (year, month) and calculate date range."""
+    year, month = map(int, month_str.split("-"))
+    last_day = calendar.monthrange(year, month)[1]
+    start = f"{year}-{month:02d}-01"
+    end = f"{year}-{month:02d}-{last_day:02d}"
+    return year, month, start, end
+
+
+def prev_month(year, month):
+    """Calculate previous month's date range."""
+    if month == 1:
+        py, pm = year - 1, 12
+    else:
+        py, pm = year, month - 1
+    last_day = calendar.monthrange(py, pm)[1]
+    start = f"{py}-{pm:02d}-01"
+    end = f"{py}-{pm:02d}-{last_day:02d}"
+    return py, pm, start, end
+
+
+def pct_change(current, previous):
+    """Calculate percentage change, handling zero division."""
+    if previous == 0:
+        return 100.0 if current > 0 else 0.0
+    return round(((current - previous) / previous) * 100, 1)
+
+
+def normalize_header(value):
+    value = (value or "").strip().lower()
+    replacements = {
+        "ą": "a",
+        "ć": "c",
+        "ę": "e",
+        "ł": "l",
+        "ń": "n",
+        "ó": "o",
+        "ś": "s",
+        "ź": "z",
+        "ż": "z",
+    }
+    for src, dst in replacements.items():
+        value = value.replace(src, dst)
+    return re.sub(r"[^a-z0-9]+", "", value)
+
+
+def parse_money(value):
+    text = str(value or "").strip()
+    if not text:
+        return 0.0
+    text = text.replace("PLN", "").replace("zl", "").replace("zł", "")
+    text = text.replace("\u00a0", " ").replace(" ", "")
+    if "," in text and "." in text:
+        text = text.replace(".", "").replace(",", ".")
+    elif "," in text:
+        text = text.replace(",", ".")
+    text = re.sub(r"[^0-9.\-]", "", text)
+    return round(float(text), 2) if text else 0.0
+
+
+def parse_int_value(value):
+    return int(round(parse_money(value)))
+
+
+def parse_history_month(value):
+    text = str(value or "").strip()
+    if not text:
+        return ""
+    if re.fullmatch(r"\d{4}-\d{2}", text):
+        return text
+    if re.fullmatch(r"\d{2}[.-]\d{4}", text):
+        month, year = re.split(r"[.-]", text)
+        return f"{int(year):04d}-{int(month):02d}"
+    if re.fullmatch(r"\d{4}[./-]\d{1,2}[./-]\d{1,2}", text):
+        year, month, _day = re.split(r"[./-]", text)
+        return f"{int(year):04d}-{int(month):02d}"
+    if re.fullmatch(r"\d{1,2}[./-]\d{1,2}[./-]\d{4}", text):
+        _day, month, year = re.split(r"[./-]", text)
+        return f"{int(year):04d}-{int(month):02d}"
+    return text
+
+
+def parse_sheet_config(sheet_config):
+    value = str(sheet_config or "").strip()
+    if not value:
+        return "", "0"
+    if value.startswith("http"):
+        parsed = urlparse(value)
+        match = re.search(r"/spreadsheets/d/([^/]+)", parsed.path)
+        spreadsheet_id = match.group(1) if match else value
+        query_gid = parse_qs(parsed.query).get("gid", [None])[0]
+        fragment_gid = parse_qs(parsed.fragment).get("gid", [None])[0]
+        return spreadsheet_id, query_gid or fragment_gid or ""
+    if ":" in value:
+        return value.split(":", 1)
+    return value, ""
+
+
+def fetch_sales_history_from_sheet(domain, sheet_config):
+    """Fetch monthly sales history from a public Google Sheet CSV export."""
+    spreadsheet_id, gid = parse_sheet_config(sheet_config)
+    export_url = f"https://docs.google.com/spreadsheets/d/{spreadsheet_id}/gviz/tq?tqx=out:csv"
+    if gid:
+        export_url += f"&gid={gid}"
+    response = requests.get(export_url, timeout=30)
+    response.raise_for_status()
+    response.encoding = "utf-8"
+
+    reader = csv.DictReader(io.StringIO(response.text))
+    history = []
+    for row in reader:
+        normalized = {normalize_header(key): value for key, value in row.items()}
+        month = parse_history_month(
+            normalized.get("month")
+            or normalized.get("miesiac")
+            or normalized.get("data")
+            or normalized.get("date")
+        )
+        revenue = parse_money(
+            normalized.get("revenue")
+            or normalized.get("przychod")
+            or normalized.get("przychody")
+            or normalized.get("sprzedaz")
+            or normalized.get("wartosc")
+        )
+        transactions = parse_int_value(
+            normalized.get("transactions")
+            or normalized.get("transakcje")
+            or normalized.get("zamowienia")
+            or normalized.get("orders")
+        )
+        if not month or not revenue:
+            continue
+        aov = parse_money(
+            normalized.get("aov")
+            or normalized.get("sredniakoszyka")
+            or normalized.get("sredniawartosckoszyka")
+            or normalized.get("sredniawartosczamowienia")
+        )
+        if not aov and transactions:
+            aov = round(revenue / transactions, 2)
+        history.append({
+            "month": month,
+            "transactions": transactions,
+            "revenue": revenue,
+            "aov": aov,
+            "source": "google_sheet",
+        })
+    return sorted(history, key=lambda item: item["month"])
+
+
+def apply_sheet_ecommerce(report, sales_history, month, previous_month):
+    """Use Google Sheet sales data for e-commerce KPI cards."""
+    by_month = {row["month"]: row for row in sales_history}
+    current = by_month.get(month)
+    if not current:
+        return False
+    previous = by_month.get(previous_month, {"transactions": 0, "revenue": 0.0, "aov": 0.0})
+    has_previous = previous_month in by_month
+    ecommerce = (report.get("ga4") or {}).get("ecommerce") or {}
+    ecommerce["current"] = {
+        "transactions": current.get("transactions", 0),
+        "revenue": current.get("revenue", 0.0),
+        "aov": current.get("aov", 0.0),
+    }
+    ecommerce["previous"] = {
+        "transactions": previous.get("transactions", 0),
+        "revenue": previous.get("revenue", 0.0),
+        "aov": previous.get("aov", 0.0),
+    }
+    ecommerce["mom_change"] = {
+        "transactions_pct": pct_change(ecommerce["current"]["transactions"], ecommerce["previous"]["transactions"]) if has_previous else None,
+        "revenue_pct": pct_change(ecommerce["current"]["revenue"], ecommerce["previous"]["revenue"]) if has_previous else None,
+        "aov_pct": pct_change(ecommerce["current"]["aov"], ecommerce["previous"]["aov"]) if has_previous else None,
+    }
+    ecommerce["source"] = "google_sheet"
+    if report.get("ga4") is None:
+        report["ga4"] = {}
+    report["ga4"]["ecommerce"] = ecommerce
+    return True
+
+
+def fetch_google_ads_data(client, customer_id, start_date, end_date):
+    """Fetch campaign metrics for a date range."""
+    query = f"""
+        SELECT campaign.id, campaign.name, campaign.status,
+               campaign.advertising_channel_type,
+               metrics.impressions, metrics.clicks,
+               metrics.cost_micros, metrics.conversions,
+               metrics.conversions_value,
+               metrics.ctr, metrics.average_cpc
+        FROM campaign
+        WHERE segments.date BETWEEN '{start_date}' AND '{end_date}'
+          AND campaign.status != 'REMOVED'
+    """
+    rows = run_query(client, customer_id, query)
+
+    campaigns = {}
+    for r in rows:
+        cid = str(r.campaign.id)
+        if cid not in campaigns:
+            campaigns[cid] = {
+                "id": cid,
+                "name": r.campaign.name,
+                "status": r.campaign.status.name,
+                "type": r.campaign.advertising_channel_type.name,
+                "impressions": 0, "clicks": 0, "cost": 0.0,
+                "conversions": 0.0, "conversion_value": 0.0,
+                "ctr": 0.0, "cpc": 0.0,
+            }
+        c = campaigns[cid]
+        c["impressions"] += r.metrics.impressions
+        c["clicks"] += r.metrics.clicks
+        c["cost"] += r.metrics.cost_micros / 1_000_000
+        c["conversions"] += r.metrics.conversions
+        c["conversion_value"] += r.metrics.conversions_value
+
+    # Calculate derived metrics
+    for c in campaigns.values():
+        c["cost"] = round(c["cost"], 2)
+        c["conversions"] = round(c["conversions"], 1)
+        c["conversion_value"] = round(c["conversion_value"], 2)
+        c["ctr"] = round((c["clicks"] / c["impressions"] * 100) if c["impressions"] else 0, 2)
+        c["cpc"] = round((c["cost"] / c["clicks"]) if c["clicks"] else 0, 2)
+        c["cpa"] = round((c["cost"] / c["conversions"]) if c["conversions"] else 0, 2)
+        c["roas"] = round((c["conversion_value"] / c["cost"]) if c["cost"] else 0, 2)
+
+    return list(campaigns.values())
+
+
+def calc_totals(campaigns):
+    """Sum up totals across campaigns."""
+    t = {"impressions": 0, "clicks": 0, "cost": 0.0, "conversions": 0.0, "conversion_value": 0.0}
+    for c in campaigns:
+        t["impressions"] += c["impressions"]
+        t["clicks"] += c["clicks"]
+        t["cost"] += c["cost"]
+        t["conversions"] += c["conversions"]
+        t["conversion_value"] += c.get("conversion_value", 0.0)
+
+    t["cost"] = round(t["cost"], 2)
+    t["conversions"] = round(t["conversions"], 1)
+    t["conversion_value"] = round(t["conversion_value"], 2)
+    t["ctr"] = round((t["clicks"] / t["impressions"] * 100) if t["impressions"] else 0, 2)
+    t["cpc"] = round((t["cost"] / t["clicks"]) if t["clicks"] else 0, 2)
+    t["cpa"] = round((t["cost"] / t["conversions"]) if t["conversions"] else 0, 2)
+    t["roas"] = round((t["conversion_value"] / t["cost"]) if t["cost"] else 0, 2)
+    return t
+
+
+def fetch_daily_data(client, customer_id, start_date, end_date):
+    """Fetch daily breakdown for charts."""
+    query = f"""
+        SELECT segments.date,
+               metrics.impressions, metrics.clicks, metrics.cost_micros
+        FROM campaign
+        WHERE segments.date BETWEEN '{start_date}' AND '{end_date}'
+          AND campaign.status != 'REMOVED'
+    """
+    rows = run_query(client, customer_id, query)
+
+    daily = {}
+    for r in rows:
+        d = r.segments.date
+        if d not in daily:
+            daily[d] = {"date": d, "impressions": 0, "clicks": 0, "cost": 0.0}
+        daily[d]["impressions"] += r.metrics.impressions
+        daily[d]["clicks"] += r.metrics.clicks
+        daily[d]["cost"] += r.metrics.cost_micros / 1_000_000
+
+    result = sorted(daily.values(), key=lambda x: x["date"])
+    for d in result:
+        d["cost"] = round(d["cost"], 2)
+    return result
+
+
+def fetch_search_terms(client, customer_id, start_date, end_date, limit=15):
+    """Fetch top search terms by clicks."""
+    query = f"""
+        SELECT search_term_view.search_term,
+               metrics.impressions, metrics.clicks,
+               metrics.cost_micros, metrics.conversions
+        FROM search_term_view
+        WHERE segments.date BETWEEN '{start_date}' AND '{end_date}'
+        ORDER BY metrics.clicks DESC
+        LIMIT {limit}
+    """
+    rows = run_query(client, customer_id, query)
+
+    terms = []
+    for r in rows:
+        clicks = r.metrics.clicks
+        impressions = r.metrics.impressions
+        terms.append({
+            "term": r.search_term_view.search_term,
+            "impressions": impressions,
+            "clicks": clicks,
+            "cost": round(r.metrics.cost_micros / 1_000_000, 2),
+            "conversions": round(r.metrics.conversions, 1),
+            "ctr": round((clicks / impressions * 100) if impressions else 0, 2),
+        })
+    return terms
+
+
+def fetch_ga4_data(property_id, start_date, end_date, prev_start, prev_end):
+    """Fetch GA4 data: sessions, users, traffic sources, devices."""
+    from google.oauth2.credentials import Credentials
+    from google.analytics.data_v1beta import BetaAnalyticsDataClient
+    from google.analytics.data_v1beta.types import (
+        RunReportRequest, DateRange, Metric, Dimension, OrderBy,
+    )
+
+    credentials = Credentials(
+        token=None,
+        refresh_token=os.environ["GA4_REFRESH_TOKEN"],
+        client_id=os.environ["GOOGLE_ADS_OAUTH2_CLIENT_ID"],
+        client_secret=os.environ["GOOGLE_ADS_OAUTH2_CLIENT_SECRET"],
+        token_uri="https://oauth2.googleapis.com/token",
+    )
+    client = BetaAnalyticsDataClient(credentials=credentials)
+    prop = f"properties/{property_id}"
+
+    # 1. Sessions & Users (current + previous month)
+    def get_totals(sd, ed):
+        resp = client.run_report(RunReportRequest(
+            property=prop,
+            date_ranges=[DateRange(start_date=sd, end_date=ed)],
+            metrics=[
+                Metric(name="sessions"),
+                Metric(name="totalUsers"),
+                Metric(name="newUsers"),
+                Metric(name="screenPageViews"),
+                Metric(name="averageSessionDuration"),
+                Metric(name="bounceRate"),
+            ],
+        ))
+        row = resp.rows[0] if resp.rows else None
+        if not row:
+            return {"sessions": 0, "users": 0, "new_users": 0, "pageviews": 0, "avg_duration": 0, "bounce_rate": 0}
+        return {
+            "sessions": int(row.metric_values[0].value),
+            "users": int(row.metric_values[1].value),
+            "new_users": int(row.metric_values[2].value),
+            "pageviews": int(row.metric_values[3].value),
+            "avg_duration": round(float(row.metric_values[4].value), 1),
+            "bounce_rate": round(float(row.metric_values[5].value) * 100, 1),
+        }
+
+    current = get_totals(start_date, end_date)
+    previous = get_totals(prev_start, prev_end)
+
+    # 2. Traffic sources
+    resp = client.run_report(RunReportRequest(
+        property=prop,
+        date_ranges=[DateRange(start_date=start_date, end_date=end_date)],
+        dimensions=[Dimension(name="sessionSourceMedium")],
+        metrics=[Metric(name="sessions")],
+        order_bys=[OrderBy(metric=OrderBy.MetricOrderBy(metric_name="sessions"), desc=True)],
+        limit=10,
+    ))
+    sources = []
+    for row in resp.rows:
+        sources.append({
+            "source_medium": row.dimension_values[0].value,
+            "sessions": int(row.metric_values[0].value),
+        })
+
+    # 3. Devices
+    resp = client.run_report(RunReportRequest(
+        property=prop,
+        date_ranges=[DateRange(start_date=start_date, end_date=end_date)],
+        dimensions=[Dimension(name="deviceCategory")],
+        metrics=[Metric(name="sessions")],
+        order_bys=[OrderBy(metric=OrderBy.MetricOrderBy(metric_name="sessions"), desc=True)],
+    ))
+    devices = []
+    for row in resp.rows:
+        devices.append({
+            "device": row.dimension_values[0].value,
+            "sessions": int(row.metric_values[0].value),
+        })
+
+    # 4. Daily sessions (for chart)
+    resp = client.run_report(RunReportRequest(
+        property=prop,
+        date_ranges=[DateRange(start_date=start_date, end_date=end_date)],
+        dimensions=[Dimension(name="date")],
+        metrics=[Metric(name="sessions"), Metric(name="totalUsers")],
+        order_bys=[OrderBy(dimension=OrderBy.DimensionOrderBy(dimension_name="date"))],
+    ))
+    daily_sessions = []
+    for row in resp.rows:
+        raw = row.dimension_values[0].value
+        formatted = f"{raw[:4]}-{raw[4:6]}-{raw[6:]}"
+        daily_sessions.append({
+            "date": formatted,
+            "sessions": int(row.metric_values[0].value),
+            "users": int(row.metric_values[1].value),
+        })
+
+    return {
+        "current": current,
+        "previous": previous,
+        "mom_change": {
+            "sessions_pct": pct_change(current["sessions"], previous["sessions"]),
+            "users_pct": pct_change(current["users"], previous["users"]),
+            "new_users_pct": pct_change(current["new_users"], previous["new_users"]),
+            "pageviews_pct": pct_change(current["pageviews"], previous["pageviews"]),
+            "avg_duration_pct": pct_change(current["avg_duration"], previous["avg_duration"]),
+            "bounce_rate_pct": pct_change(current["bounce_rate"], previous["bounce_rate"]),
+        },
+        "sources": sources,
+        "devices": devices,
+        "daily": daily_sessions,
+    }
+
+
+def fetch_ga4_ecommerce(property_id, start_date, end_date, prev_start, prev_end):
+    """Fetch GA4 e-commerce data: transactions, revenue, AOV."""
+    from google.oauth2.credentials import Credentials
+    from google.analytics.data_v1beta import BetaAnalyticsDataClient
+    from google.analytics.data_v1beta.types import (
+        RunReportRequest, DateRange, Metric, Dimension, OrderBy,
+    )
+
+    credentials = Credentials(
+        token=None,
+        refresh_token=os.environ["GA4_REFRESH_TOKEN"],
+        client_id=os.environ["GOOGLE_ADS_OAUTH2_CLIENT_ID"],
+        client_secret=os.environ["GOOGLE_ADS_OAUTH2_CLIENT_SECRET"],
+        token_uri="https://oauth2.googleapis.com/token",
+    )
+    client = BetaAnalyticsDataClient(credentials=credentials)
+    prop = f"properties/{property_id}"
+
+    def get_ecom(sd, ed):
+        resp = client.run_report(RunReportRequest(
+            property=prop,
+            date_ranges=[DateRange(start_date=sd, end_date=ed)],
+            metrics=[
+                Metric(name="transactions"),
+                Metric(name="purchaseRevenue"),
+                Metric(name="averagePurchaseRevenue"),
+            ],
+        ))
+        row = resp.rows[0] if resp.rows else None
+        if not row:
+            return {"transactions": 0, "revenue": 0.0, "aov": 0.0}
+        return {
+            "transactions": int(row.metric_values[0].value),
+            "revenue": round(float(row.metric_values[1].value), 2),
+            "aov": round(float(row.metric_values[2].value), 2),
+        }
+
+    current = get_ecom(start_date, end_date)
+    previous = get_ecom(prev_start, prev_end)
+
+    # Daily revenue chart
+    resp = client.run_report(RunReportRequest(
+        property=prop,
+        date_ranges=[DateRange(start_date=start_date, end_date=end_date)],
+        dimensions=[Dimension(name="date")],
+        metrics=[Metric(name="purchaseRevenue"), Metric(name="transactions")],
+        order_bys=[OrderBy(dimension=OrderBy.DimensionOrderBy(dimension_name="date"))],
+    ))
+    daily_revenue = []
+    for row in resp.rows:
+        raw = row.dimension_values[0].value
+        formatted = f"{raw[:4]}-{raw[4:6]}-{raw[6:]}"
+        daily_revenue.append({
+            "date": formatted,
+            "revenue": round(float(row.metric_values[0].value), 2),
+            "transactions": int(row.metric_values[1].value),
+        })
+
+    # Revenue by source
+    resp = client.run_report(RunReportRequest(
+        property=prop,
+        date_ranges=[DateRange(start_date=start_date, end_date=end_date)],
+        dimensions=[Dimension(name="sessionSourceMedium")],
+        metrics=[Metric(name="purchaseRevenue"), Metric(name="transactions")],
+        order_bys=[OrderBy(metric=OrderBy.MetricOrderBy(metric_name="purchaseRevenue"), desc=True)],
+        limit=10,
+    ))
+    revenue_by_source = []
+    for row in resp.rows:
+        revenue_by_source.append({
+            "source_medium": row.dimension_values[0].value,
+            "revenue": round(float(row.metric_values[0].value), 2),
+            "transactions": int(row.metric_values[1].value),
+        })
+
+    # Top products by revenue
+    resp = client.run_report(RunReportRequest(
+        property=prop,
+        date_ranges=[DateRange(start_date=start_date, end_date=end_date)],
+        dimensions=[Dimension(name="itemName")],
+        metrics=[
+            Metric(name="itemRevenue"),
+            Metric(name="itemsPurchased"),
+        ],
+        order_bys=[OrderBy(metric=OrderBy.MetricOrderBy(metric_name="itemRevenue"), desc=True)],
+        limit=10,
+    ))
+    top_products = []
+    for row in resp.rows:
+        top_products.append({
+            "name": row.dimension_values[0].value,
+            "revenue": round(float(row.metric_values[0].value), 2),
+            "quantity": int(row.metric_values[1].value),
+        })
+
+    return {
+        "current": current,
+        "previous": previous,
+        "mom_change": {
+            "transactions_pct": pct_change(current["transactions"], previous["transactions"]),
+            "revenue_pct": pct_change(current["revenue"], previous["revenue"]),
+            "aov_pct": pct_change(current["aov"], previous["aov"]),
+        },
+        "daily": daily_revenue,
+        "revenue_by_source": revenue_by_source,
+        "top_products": top_products,
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Pobierz dane do raportu miesięcznego")
+    parser.add_argument("--customer", required=True, help="Domena lub Google Ads customer ID")
+    parser.add_argument("--month", required=True, help="Miesiąc raportu (YYYY-MM)")
+    parser.add_argument("--output", help="Ścieżka do pliku JSON")
+    parser.add_argument("--ga4-property", help="GA4 Property ID (domyślnie z .env)")
+    parser.add_argument("--skip-ga4", action="store_true", help="Pomiń dane GA4")
+    args = parser.parse_args()
+
+    customer_id = get_customer_id(args.customer)
+    client = get_client(use_proto_plus=True)
+
+    year, month, start_date, end_date = parse_month(args.month)
+    py, pm, prev_start, prev_end = prev_month(year, month)
+    month_names_pl = {
+        1: "Styczeń", 2: "Luty", 3: "Marzec", 4: "Kwiecień",
+        5: "Maj", 6: "Czerwiec", 7: "Lipiec", 8: "Sierpień",
+        9: "Wrzesień", 10: "Październik", 11: "Listopad", 12: "Grudzień",
+    }
+
+    # Resolve domain name for output
+    domain = args.customer if not args.customer.replace("-", "").isdigit() else args.customer
+
+    print(f"Pobieram dane Google Ads: {domain} za {args.month}...")
+
+    # Google Ads data
+    campaigns = fetch_google_ads_data(client, customer_id, start_date, end_date)
+    prev_campaigns = fetch_google_ads_data(client, customer_id, prev_start, prev_end)
+    totals = calc_totals(campaigns)
+    prev_totals = calc_totals(prev_campaigns)
+    daily = fetch_daily_data(client, customer_id, start_date, end_date)
+    search_terms = fetch_search_terms(client, customer_id, start_date, end_date)
+
+    mom_change = {
+        "impressions_pct": pct_change(totals["impressions"], prev_totals["impressions"]),
+        "clicks_pct": pct_change(totals["clicks"], prev_totals["clicks"]),
+        "cost_pct": pct_change(totals["cost"], prev_totals["cost"]),
+        "conversions_pct": pct_change(totals["conversions"], prev_totals["conversions"]),
+        "conversion_value_pct": pct_change(totals["conversion_value"], prev_totals["conversion_value"]),
+        "ctr_pct": pct_change(totals["ctr"], prev_totals["ctr"]),
+        "cpc_pct": pct_change(totals["cpc"], prev_totals["cpc"]),
+        "cpa_pct": pct_change(totals["cpa"], prev_totals["cpa"]),
+        "roas_pct": pct_change(totals["roas"], prev_totals["roas"]),
+    }
+
+    report = {
+        "client": domain,
+        "month": args.month,
+        "month_name": month_names_pl[month],
+        "year": year,
+        "prev_month": f"{py}-{pm:02d}",
+        "prev_month_name": month_names_pl[pm],
+        "generated_at": datetime.now().isoformat(),
+        "google_ads": {
+            "campaigns": campaigns,
+            "totals": totals,
+            "prev_totals": prev_totals,
+            "mom_change": mom_change,
+            "daily": daily,
+            "search_terms": search_terms,
+        },
+    }
+
+    # GA4 data
+    if not args.skip_ga4:
+        ga4_property = args.ga4_property
+        if not ga4_property:
+            # Try to find GA4 property in .env
+            env_key = f"GA4_PROPERTY_ID_{domain}"
+            ga4_property = os.environ.get(env_key)
+
+        if ga4_property:
+            print(f"Pobieram dane GA4 (property: {ga4_property})...")
+            try:
+                ga4 = fetch_ga4_data(ga4_property, start_date, end_date, prev_start, prev_end)
+                report["ga4"] = ga4
+                print(f"  GA4: {ga4['current']['sessions']} sesji, {ga4['current']['users']} uzytkownikow")
+            except Exception as e:
+                print(f"  UWAGA: Blad GA4: {e}")
+                report["ga4"] = None
+        else:
+            print(f"  Brak GA4 Property ID w .env ({env_key}) - pomijam GA4")
+            report["ga4"] = None
+    else:
+        report["ga4"] = None
+
+    # Semstorm SEO data
+    semstorm_login = os.environ.get("SEMSTORM_LOGIN", "")
+    if semstorm_login:
+        print(f"Pobieram dane Semstorm...")
+        try:
+            sys.path.insert(0, str(Path(__file__).parent))
+            from fetch_semstorm_data import fetch_domain_stats
+            semstorm = fetch_domain_stats(domain, args.month)
+            report["semstorm"] = semstorm
+            if semstorm and semstorm.get("current"):
+                cur = semstorm["current"]
+                print(f"  Semstorm: TOP3={cur['top3']}, TOP10={cur['top10']}, TOP50={cur['top50']}, traffic={cur['traffic']}")
+        except Exception as e:
+            print(f"  UWAGA: Blad Semstorm: {e}")
+            report["semstorm"] = None
+    else:
+        report["semstorm"] = None
+
+    # E-commerce data: Shoper (primary) or GA4 (fallback)
+    shoper_key = f"SHOPER_API_URL_{domain}"
+    if os.environ.get(shoper_key):
+        print(f"Pobieram dane e-commerce ze Shoper...")
+        try:
+            from fetch_shoper_data import fetch_shoper_ecommerce
+            shoper_ecom = fetch_shoper_ecommerce(domain, args.month, f"{py}-{pm:02d}")
+            if shoper_ecom and shoper_ecom["current"]["transactions"] > 0:
+                # Get revenue_by_source and top_products from GA4
+                if report.get("ga4") and ga4_property:
+                    try:
+                        ga4_ecom = fetch_ga4_ecommerce(ga4_property, start_date, end_date, prev_start, prev_end)
+                        if ga4_ecom:
+                            shoper_ecom["revenue_by_source"] = ga4_ecom.get("revenue_by_source", [])
+                            shoper_ecom["top_products"] = ga4_ecom.get("top_products", [])
+                    except Exception as e:
+                        print(f"  UWAGA: GA4 revenue_by_source/top_products: {e}")
+                        shoper_ecom["revenue_by_source"] = []
+                        shoper_ecom["top_products"] = []
+                if report.get("ga4") is None:
+                    report["ga4"] = {}
+                report["ga4"]["ecommerce"] = shoper_ecom
+                cur = shoper_ecom["current"]
+                print(f"  Shoper: {cur['transactions']} zamówień, {cur['revenue']:.2f} PLN, AOV {cur['aov']:.2f} PLN")
+            else:
+                if report.get("ga4"):
+                    report["ga4"]["ecommerce"] = None
+        except Exception as e:
+            print(f"  UWAGA: Blad Shoper: {e}")
+            if report.get("ga4"):
+                report["ga4"]["ecommerce"] = None
+    elif report.get("ga4") and ga4_property:
+        print(f"Pobieram dane GA4 e-commerce...")
+        try:
+            ecom = fetch_ga4_ecommerce(ga4_property, start_date, end_date, prev_start, prev_end)
+            if ecom and ecom["current"]["transactions"] > 0:
+                report["ga4"]["ecommerce"] = ecom
+                cur = ecom["current"]
+                print(f"  E-commerce (GA4): {cur['transactions']} transakcji, {cur['revenue']:.2f} PLN przychodu")
+            else:
+                report["ga4"]["ecommerce"] = None
+        except Exception as e:
+            print(f"  UWAGA: Blad GA4 e-commerce: {e}")
+            if report.get("ga4"):
+                report["ga4"]["ecommerce"] = None
+
+    # Monthly sales history for chart. Prefer client Google Sheet when configured.
+    client_report_config = load_client_report_config(domain)
+    sales_history_sheet = client_report_config.get("sales_history_sheet") or os.environ.get(f"GSHEET_SALES_HISTORY_{domain}")
+    report_start = os.environ.get(f"REPORT_START_DATE_{domain}")
+    if sales_history_sheet:
+        try:
+            sales_history = fetch_sales_history_from_sheet(domain, sales_history_sheet)
+            if apply_sheet_ecommerce(report, sales_history, args.month, f"{py}-{pm:02d}"):
+                current_sheet = report["ga4"]["ecommerce"]["current"]
+                print(
+                    f"  E-commerce (Google Sheet): {current_sheet['transactions']} transakcji, "
+                    f"{current_sheet['revenue']:.2f} PLN przychodu"
+                )
+            filtered = [e for e in sales_history if not report_start or e["month"] >= report_start]
+            report["sales_history"] = filtered
+            print(f"  Historia sprzedaży z Google Sheet: {len(filtered)} miesięcy")
+        except Exception as e:
+            report["sales_history"] = []
+            print(f"  UWAGA: Nie udalo sie pobrac historii sprzedazy z Google Sheet: {e}")
+    else:
+        ecom_data = report.get("ga4", {}).get("ecommerce") if report.get("ga4") else None
+        if ecom_data and ecom_data.get("current", {}).get("transactions", 0) > 0:
+            history_path = ROOT / "clients" / domain / "sales_history.json"
+            history_path.parent.mkdir(parents=True, exist_ok=True)
+            sales_history = []
+            if history_path.exists():
+                with open(history_path, "r", encoding="utf-8") as f:
+                    sales_history = json.load(f)
+            cur_entry = {
+                "month": args.month,
+                "transactions": ecom_data["current"]["transactions"],
+                "revenue": ecom_data["current"]["revenue"],
+                "aov": ecom_data["current"]["aov"],
+                "source": ecom_data.get("source", "ga4"),
+            }
+            by_month = {e["month"]: e for e in sales_history}
+            by_month[args.month] = cur_entry
+            sales_history = sorted(by_month.values(), key=lambda x: x["month"])
+            with open(history_path, "w", encoding="utf-8") as f:
+                json.dump(sales_history, f, indent=2, ensure_ascii=False)
+            filtered = [e for e in sales_history if not report_start or e["month"] >= report_start]
+            report["sales_history"] = filtered
+            print(f"  Historia sprzedaży: {len(filtered)} miesięcy zapisanych")
+        else:
+            report["sales_history"] = []
+
+    # SEO links from Google Sheets
+    seo_links_key = f"GSHEET_SEO_LINKS_{domain}"
+    if os.environ.get(seo_links_key):
+        print(f"Pobieram linki SEO...")
+        try:
+            from fetch_seo_links import fetch_seo_links, fetch_seo_activities
+            seo_links = fetch_seo_links(domain, args.month)
+            report["seo_links"] = seo_links or []
+            print(f"  Linki SEO: {len(report['seo_links'])} w {args.month}")
+            # SEO activities (text box)
+            seo_act_key = f"GSHEET_SEO_ACTIVITIES_{domain}"
+            if os.environ.get(seo_act_key):
+                seo_activities = fetch_seo_activities(domain, args.month)
+                report["seo_activities"] = seo_activities
+                if seo_activities:
+                    print(f"  Działania SEO: {len(seo_activities)} znaków")
+        except Exception as e:
+            print(f"  UWAGA: Blad SEO links: {e}")
+            report["seo_links"] = []
+    else:
+        report["seo_links"] = []
+
+    # Output
+    if args.output:
+        output_path = Path(args.output)
+    else:
+        output_path = ROOT / "scripts" / "reports" / "output" / f"{domain}_{args.month}.json"
+
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    with open(output_path, "w", encoding="utf-8") as f:
+        json.dump(report, f, ensure_ascii=False, indent=2)
+
+    print(f"\nZapisano: {output_path}")
+    print(f"Google Ads: {totals['clicks']} klikniec, {totals['conversions']} konwersji, {totals['cost']:.2f} PLN")
+
+
+if __name__ == "__main__":
+    main()

scripts/reports/fetch_semstorm_data.py

@@ -0,0 +1,171 @@
+#!/usr/bin/env python3
+"""
+Pobiera dane SEO z Semstorm API (pozycje TOP 3/10/20/50, traffic).
+
+Użycie:
+    python scripts/reports/fetch_semstorm_data.py --domain innsi.pl
+    python scripts/reports/fetch_semstorm_data.py --domain innsi.pl --month 2026-02
+"""
+
+import argparse
+import json
+import os
+import sys
+import io
+from pathlib import Path
+
+if __name__ == "__main__":
+    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace")
+# When imported as module, don't touch stdout
+
+import requests
+ROOT = Path(__file__).parent.parent.parent
+sys.path.insert(0, str(ROOT))
+from src.gads_v2.config import load_env
+
+load_env(ROOT / ".env")
+
+
+def get_semstorm_token():
+    """Authenticate and get bearer token."""
+    base = os.environ.get("SEMSTORM_API_BASE", "https://api.semstorm.com")
+    login = os.environ.get("SEMSTORM_LOGIN", "")
+    password = os.environ.get("SEMSTORM_PASSWORD", "")
+
+    if not login or not password:
+        raise ValueError("Brak SEMSTORM_LOGIN / SEMSTORM_PASSWORD w .env")
+
+    r = requests.post(f"{base}/consumer/login", data={
+        "username": login,
+        "password": password,
+    }, headers={"Accept": "application/json"}, timeout=30)
+    r.raise_for_status()
+
+    token = r.json().get("token", "")
+    if not token:
+        raise ValueError("Semstorm: brak tokenu w odpowiedzi logowania")
+    return base, token
+
+
+def _history_path(domain):
+    """Path to local cumulative Semstorm history file."""
+    return ROOT / "clients" / domain / "semstorm_history.json"
+
+
+def _load_local_history(domain):
+    """Load locally stored Semstorm history."""
+    path = _history_path(domain)
+    if path.exists():
+        with open(path, "r", encoding="utf-8") as f:
+            return json.load(f)
+    return []
+
+
+def _save_local_history(domain, entries):
+    """Save Semstorm history locally (deduplicated, sorted)."""
+    path = _history_path(domain)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    # Deduplicate by month, keep latest per month
+    by_month = {}
+    for e in entries:
+        by_month[e["month"]] = e
+    sorted_entries = sorted(by_month.values(), key=lambda x: x["date"])
+    with open(path, "w", encoding="utf-8") as f:
+        json.dump(sorted_entries, f, indent=2, ensure_ascii=False)
+    return sorted_entries
+
+
+def _get_report_start(domain):
+    """Get REPORT_START_DATE for domain from .env."""
+    key = f"REPORT_START_DATE_{domain}"
+    return os.environ.get(key)
+
+
+def fetch_domain_stats(domain, month=None):
+    """Fetch Semstorm domain stats. Merges API data with local history."""
+    base, token = get_semstorm_token()
+
+    r = requests.post(f"{base}/semstorm/v4/explorer/domain-stats",
+        json={"domains": [domain]},
+        headers={
+            "Accept": "application/json",
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {token}",
+        },
+        timeout=30,
+    )
+    r.raise_for_status()
+    data = r.json()
+
+    api_entries = []
+    if data.get("success") and domain in data.get("results", {}):
+        domain_data = data["results"][domain]
+        for date_key, metrics in domain_data.items():
+            kw = metrics.get("keywords", {})
+            api_entries.append({
+                "date": f"{date_key[:4]}-{date_key[4:6]}-{date_key[6:]}",
+                "month": f"{date_key[:4]}-{date_key[4:6]}",
+                "top3": kw.get("top3", 0),
+                "top10": kw.get("top10", 0),
+                "top20": kw.get("top20", 0),
+                "top50": kw.get("top50", 0),
+                "top100": kw.get("top100", 0),
+                "traffic": metrics.get("traffic", 0),
+            })
+
+    # Merge with local history (local + API, deduplicated)
+    local_entries = _load_local_history(domain)
+    all_entries = local_entries + api_entries
+    entries = _save_local_history(domain, all_entries)
+
+    # Filter by REPORT_START_DATE if set
+    start = _get_report_start(domain)
+    if start:
+        entries = [e for e in entries if e["month"] >= start]
+
+    # If month specified, find that month + previous for MoM
+    if month:
+        current = next((e for e in entries if e["month"] == month), None)
+        prev_entries = [e for e in entries if e["month"] < month]
+        previous = prev_entries[-1] if prev_entries else None
+
+        result = {
+            "current": current,
+            "previous": previous,
+            "history": entries,
+        }
+        if current and previous:
+            result["mom_change"] = {
+                "top3_pct": _pct(current["top3"], previous["top3"]),
+                "top10_pct": _pct(current["top10"], previous["top10"]),
+                "top50_pct": _pct(current["top50"], previous["top50"]),
+                "traffic_pct": _pct(current["traffic"], previous["traffic"]),
+            }
+        return result
+
+    # Return latest + history
+    return {
+        "current": entries[-1] if entries else None,
+        "previous": entries[-2] if len(entries) > 1 else None,
+        "history": entries,
+    }
+
+
+def _pct(current, previous):
+    if previous == 0:
+        return 100.0 if current > 0 else 0.0
+    return round(((current - previous) / previous) * 100, 1)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Pobierz dane Semstorm")
+    parser.add_argument("--domain", required=True)
+    parser.add_argument("--month", help="YYYY-MM")
+    args = parser.parse_args()
+
+    data = fetch_domain_stats(args.domain, args.month)
+    print(json.dumps(data, indent=2, ensure_ascii=False))
+
+
+if __name__ == "__main__":
+    main()

scripts/reports/fetch_seo_links.py

@@ -0,0 +1,114 @@
+#!/usr/bin/env python3
+"""
+Pobiera linki SEO z Google Sheets (publiczny CSV export).
+
+Użycie:
+    python scripts/reports/fetch_seo_links.py --domain innsi.pl --month 2026-02
+"""
+
+import argparse
+import csv
+import io
+import json
+import os
+import sys
+from pathlib import Path
+
+if __name__ == "__main__":
+    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace")
+
+import requests
+ROOT = Path(__file__).parent.parent.parent
+sys.path.insert(0, str(ROOT))
+from src.gads_v2.config import load_env
+
+load_env(ROOT / ".env")
+
+
+def fetch_seo_links(domain, month):
+    """Fetch SEO links for given domain and month from Google Sheets.
+
+    Returns list of dicts: [{"date": "2026-02-01", "url": "https://..."}]
+    """
+    env_key = f"GSHEET_SEO_LINKS_{domain}"
+    sheet_config = os.environ.get(env_key, "")
+    if not sheet_config:
+        return None
+
+    if ":" in sheet_config:
+        spreadsheet_id, gid = sheet_config.split(":", 1)
+    else:
+        spreadsheet_id = sheet_config
+        gid = "0"
+
+    export_url = f"https://docs.google.com/spreadsheets/d/{spreadsheet_id}/export?format=csv&gid={gid}"
+
+    r = requests.get(export_url, timeout=30)
+    r.raise_for_status()
+    r.encoding = "utf-8"
+
+    reader = csv.DictReader(io.StringIO(r.text))
+    links = []
+    for row in reader:
+        date = row.get("Data", "").strip()
+        url = row.get("URL", "").strip()
+        if not date or not url:
+            continue
+        # Match month (date format: YYYY-MM-DD)
+        if date[:7] == month:
+            links.append({"date": date, "url": url})
+
+    return links
+
+
+def fetch_seo_activities(domain, month):
+    """Fetch SEO activities description for given domain and month.
+
+    Returns string with activities text, or None.
+    """
+    env_key = f"GSHEET_SEO_ACTIVITIES_{domain}"
+    sheet_config = os.environ.get(env_key, "")
+    if not sheet_config:
+        return None
+
+    if ":" in sheet_config:
+        spreadsheet_id, gid = sheet_config.split(":", 1)
+    else:
+        spreadsheet_id = sheet_config
+        gid = "0"
+
+    export_url = f"https://docs.google.com/spreadsheets/d/{spreadsheet_id}/export?format=csv&gid={gid}"
+
+    r = requests.get(export_url, timeout=30)
+    r.raise_for_status()
+    r.encoding = "utf-8"
+
+    reader = csv.DictReader(io.StringIO(r.text))
+    for row in reader:
+        date = row.get("Data", "").strip()
+        text = row.get("URL", "").strip()  # Column is named URL but contains text
+        if not date or not text:
+            continue
+        if date[:7] == month:
+            return text
+
+    return None
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Pobierz linki SEO z Google Sheets")
+    parser.add_argument("--domain", required=True)
+    parser.add_argument("--month", required=True, help="YYYY-MM")
+    args = parser.parse_args()
+
+    links = fetch_seo_links(args.domain, args.month)
+    if links is None:
+        print(f"Brak konfiguracji GSHEET_SEO_LINKS_{args.domain} w .env")
+        sys.exit(1)
+
+    print(json.dumps(links, indent=2, ensure_ascii=False))
+    print(f"\nLiczba linkow w {args.month}: {len(links)}")
+
+
+if __name__ == "__main__":
+    main()

scripts/reports/fetch_shoper_data.py

@@ -0,0 +1,180 @@
+#!/usr/bin/env python3
+"""
+Pobiera dane e-commerce ze Shoper API (zamówienia, przychody, AOV).
+
+Użycie:
+    python scripts/reports/fetch_shoper_data.py --domain innsi.pl --month 2026-02
+"""
+
+import argparse
+import json
+import os
+import sys
+import io
+from collections import defaultdict
+from pathlib import Path
+
+if __name__ == "__main__":
+    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace")
+
+import requests
+ROOT = Path(__file__).parent.parent.parent
+sys.path.insert(0, str(ROOT))
+from src.gads_v2.config import load_env
+
+load_env(ROOT / ".env")
+
+
+def _shoper_auth(domain):
+    """Authenticate to Shoper API, return (base_url, headers)."""
+    base = os.environ[f"SHOPER_API_URL_{domain}"].rstrip("/")
+    login = os.environ[f"SHOPER_API_LOGIN_{domain}"]
+    password = os.environ[f"SHOPER_API_PASSWORD_{domain}"]
+
+    r = requests.post(f"{base}/auth", auth=(login, password), timeout=15)
+    r.raise_for_status()
+    token = r.json()["access_token"]
+    return base, {"Authorization": f"Bearer {token}"}
+
+
+def _collect_orders_for_month(base, headers, target_month):
+    """Paginate orders (newest first) and collect all for target_month."""
+    orders = []
+    page = 1
+    found = False
+
+    while page < 100:
+        r = requests.get(
+            f"{base}/orders?limit=50&page={page}&order=date+desc",
+            headers=headers, timeout=20,
+        )
+        r.raise_for_status()
+        data = r.json()
+        page_orders = data.get("list", [])
+        if not page_orders:
+            break
+
+        for o in page_orders:
+            month = o["date"][:7]
+            if month == target_month:
+                found = True
+                orders.append(o)
+            elif found and month < target_month:
+                return orders
+
+        page += 1
+
+    return orders
+
+
+def fetch_shoper_ecommerce(domain, month, prev_month):
+    """Fetch Shoper e-commerce data for month and previous month.
+
+    Returns dict compatible with ga4.ecommerce structure.
+    """
+    base, headers = _shoper_auth(domain)
+
+    # Collect orders for both months in one pass
+    current_orders = []
+    prev_orders = []
+    page = 1
+    found_current = False
+    found_prev = False
+    passed_prev = False
+
+    while page < 200 and not passed_prev:
+        r = requests.get(
+            f"{base}/orders?limit=50&page={page}&order=date+desc",
+            headers=headers, timeout=20,
+        )
+        r.raise_for_status()
+        data = r.json()
+        page_orders = data.get("list", [])
+        if not page_orders:
+            break
+
+        for o in page_orders:
+            m = o["date"][:7]
+            if m == month:
+                found_current = True
+                current_orders.append(o)
+            elif m == prev_month:
+                found_prev = True
+                prev_orders.append(o)
+            elif found_prev and m < prev_month:
+                passed_prev = True
+                break
+
+        page += 1
+
+    def _summarize(orders):
+        if not orders:
+            return {"transactions": 0, "revenue": 0.0, "aov": 0.0}
+        total = sum(float(o["sum"]) for o in orders)
+        count = len(orders)
+        return {
+            "transactions": count,
+            "revenue": round(total, 2),
+            "aov": round(total / count, 2),
+        }
+
+    current = _summarize(current_orders)
+    previous = _summarize(prev_orders)
+
+    # Daily breakdown
+    daily_map = defaultdict(lambda: {"revenue": 0.0, "transactions": 0})
+    for o in current_orders:
+        day = o["date"][:10]
+        daily_map[day]["revenue"] += float(o["sum"])
+        daily_map[day]["transactions"] += 1
+
+    daily = []
+    for day in sorted(daily_map.keys()):
+        daily.append({
+            "date": day,
+            "revenue": round(daily_map[day]["revenue"], 2),
+            "transactions": daily_map[day]["transactions"],
+        })
+
+    # MoM change
+    def _pct(cur, prev):
+        if prev == 0:
+            return 100.0 if cur > 0 else 0.0
+        return round(((cur - prev) / prev) * 100, 1)
+
+    mom = {
+        "transactions_pct": _pct(current["transactions"], previous["transactions"]),
+        "revenue_pct": _pct(current["revenue"], previous["revenue"]),
+        "aov_pct": _pct(current["aov"], previous["aov"]),
+    }
+
+    return {
+        "source": "shoper",
+        "current": current,
+        "previous": previous,
+        "mom_change": mom,
+        "daily": daily,
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Pobierz dane e-commerce ze Shoper")
+    parser.add_argument("--domain", required=True)
+    parser.add_argument("--month", required=True, help="YYYY-MM")
+    args = parser.parse_args()
+
+    # Calculate prev month
+    y, m = map(int, args.month.split("-"))
+    if m == 1:
+        prev = f"{y-1}-12"
+    else:
+        prev = f"{y}-{m-1:02d}"
+
+    data = fetch_shoper_ecommerce(args.domain, args.month, prev)
+    print(json.dumps(data, indent=2, ensure_ascii=False))
+    cur = data["current"]
+    print(f"\n{args.month}: {cur['transactions']} zamówień, {cur['revenue']:.2f} PLN, AOV {cur['aov']:.2f} PLN")
+
+
+if __name__ == "__main__":
+    main()

scripts/reports/fetch_shopify_orders.py

@@ -0,0 +1,204 @@
+"""
+Pobiera dane zamówień z Shopify Admin przez Playwright.
+Loguje się do panelu, przechodzi do Analytics > Reports, pobiera dane sprzedaży.
+
+Użycie:
+    python scripts/reports/fetch_shopify_orders.py --customer laitica.pl --month 2026-03
+"""
+
+import argparse
+import json
+import os
+import re
+import sys
+import time
+from datetime import datetime, timedelta
+from pathlib import Path
+
+from dotenv import load_dotenv
+from playwright.sync_api import sync_playwright, TimeoutError as PlaywrightTimeout
+
+
+def get_month_range(month_str: str):
+    """Zwraca (first_day, last_day) dla danego miesiąca YYYY-MM."""
+    year, month = map(int, month_str.split("-"))
+    first_day = datetime(year, month, 1)
+    if month == 12:
+        last_day = datetime(year + 1, 1, 1) - timedelta(days=1)
+    else:
+        last_day = datetime(year, month + 1, 1) - timedelta(days=1)
+    return first_day.strftime("%Y-%m-%d"), last_day.strftime("%Y-%m-%d")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Pobierz zamówienia z Shopify Admin")
+    parser.add_argument("--customer", required=True, help="Domena klienta")
+    parser.add_argument("--month", required=True, help="Miesiąc YYYY-MM")
+    parser.add_argument("--headless", action="store_true", help="Tryb headless")
+    args = parser.parse_args()
+
+    load_dotenv(Path(__file__).resolve().parents[2] / ".env")
+
+    domain = args.customer
+    admin_url = os.environ.get(f"SHOPIFY_ADMIN_URL_{domain}")
+    login_email = os.environ.get(f"SHOPIFY_LOGIN_{domain}")
+    login_password = os.environ.get(f"SHOPIFY_PASSWORD_{domain}")
+
+    if not all([admin_url, login_email, login_password]):
+        print(f"Brak danych logowania w .env dla {domain}")
+        sys.exit(1)
+
+    first_day, last_day = get_month_range(args.month)
+    print(f"Shopify Admin: {admin_url}")
+    print(f"Okres: {first_day} — {last_day}")
+
+    with sync_playwright() as p:
+        browser = p.chromium.launch(headless=args.headless)
+        context = browser.new_context(
+            viewport={"width": 1280, "height": 900},
+            locale="pl-PL",
+        )
+        page = context.new_page()
+
+        # --- Logowanie ręczne ---
+        print("\n1. Otwieram stronę logowania Shopify...")
+        print(">>> ZALOGUJ SIĘ RĘCZNIE W OKNIE PRZEGLĄDARKI <<<")
+        print(">>> Czekam max 180 sekund na zalogowanie...\n")
+        page.goto(admin_url, wait_until="domcontentloaded", timeout=30000)
+
+        # Czekaj aż URL będzie wskazywał na zalogowany admin
+        for i in range(180):
+            time.sleep(1)
+            current_url = page.url
+            if "/store/" in current_url and "accounts.shopify.com" not in current_url and "lookup" not in current_url:
+                print(f"  Zalogowano! ({current_url})")
+                break
+            if i % 15 == 0 and i > 0:
+                print(f"  Czekam na logowanie... ({i}s)")
+        else:
+            print("  Timeout 180s — nie zalogowano.")
+            browser.close()
+            sys.exit(1)
+
+        # Przejdź do admina sklepu
+        print("2. Przechodzę do admina sklepu...")
+        page.goto(f"{admin_url}/orders?status=any", wait_until="networkidle", timeout=30000)
+        time.sleep(3)
+
+        # --- Pobieranie zamówień przez URL z filtrami dat ---
+        print(f"3. Pobieram zamówienia za {args.month}...")
+
+        # Shopify Admin API endpoint przez stronę
+        # Używamy filtrów w URL zamówień
+        orders_url = (
+            f"{admin_url}/orders.json"
+            f"?status=any"
+            f"&created_at_min={first_day}T00:00:00"
+            f"&created_at_max={last_day}T23:59:59"
+            f"&limit=250"
+        )
+
+        # Próba pobrania przez API endpoint (admin jest zalogowany)
+        response = page.goto(orders_url, wait_until="networkidle", timeout=30000)
+
+        orders_data = None
+        if response and response.status == 200:
+            try:
+                body = page.locator("body").inner_text()
+                orders_data = json.loads(body)
+                print(f"  Pobrano dane JSON: {len(orders_data.get('orders', []))} zamówień")
+            except (json.JSONDecodeError, Exception):
+                print("  Nie udało się sparsować JSON z orders.json")
+
+        # Jeśli API nie zadziałało, pobierz z UI
+        if not orders_data:
+            print("  Próbuję pobrać z Analytics...")
+            # Przejdź do raportu sprzedaży
+            analytics_url = (
+                f"{admin_url}/analytics/reports/finances_summary"
+                f"?since={first_day}&until={last_day}"
+            )
+            page.goto(analytics_url, wait_until="networkidle", timeout=30000)
+            time.sleep(5)
+
+            # Screenshot dla debugowania
+            screenshot_path = Path(__file__).parent / "output" / f"shopify_debug_{domain}.png"
+            page.screenshot(path=str(screenshot_path), full_page=True)
+            print(f"  Screenshot: {screenshot_path}")
+
+            # Próba wyciągnięcia danych z Analytics page
+            page_text = page.inner_text("body")
+            print(f"  Tekst strony (pierwsze 2000 znaków):")
+            print(f"  {page_text[:2000]}")
+
+            # Fallback — przejdź do orders z filtrem dat
+            print("\n  Fallback: liczę zamówienia z listy orders...")
+            page.goto(
+                f"{admin_url}/orders?inContextTimelineDate%5Bgte%5D={first_day}"
+                f"&inContextTimelineDate%5Blte%5D={last_day}&status=any",
+                wait_until="networkidle",
+                timeout=30000,
+            )
+            time.sleep(5)
+
+            screenshot_path2 = Path(__file__).parent / "output" / f"shopify_orders_{domain}.png"
+            page.screenshot(path=str(screenshot_path2), full_page=True)
+            print(f"  Screenshot orders: {screenshot_path2}")
+
+            page_text = page.inner_text("body")
+            print(f"  Orders page text (2000 chars):")
+            print(f"  {page_text[:2000]}")
+
+        # Przetwórz dane zamówień (jeśli mamy JSON)
+        if orders_data and "orders" in orders_data:
+            orders = orders_data["orders"]
+
+            # Filtruj tylko opłacone/zrealizowane
+            paid_orders = [
+                o for o in orders
+                if o.get("financial_status") in ("paid", "partially_refunded", "refunded")
+                or o.get("fulfillment_status") in ("fulfilled", "partial", None)
+            ]
+
+            total_revenue = sum(float(o.get("total_price", 0)) for o in paid_orders)
+            total_orders = len(paid_orders)
+            aov = total_revenue / total_orders if total_orders > 0 else 0
+
+            result = {
+                "source": "shopify_admin",
+                "month": args.month,
+                "transactions": total_orders,
+                "revenue": round(total_revenue, 2),
+                "aov": round(aov, 2),
+                "orders_detail": [
+                    {
+                        "id": o.get("name", o.get("id")),
+                        "date": o.get("created_at", "")[:10],
+                        "total": float(o.get("total_price", 0)),
+                        "status": o.get("financial_status", ""),
+                    }
+                    for o in paid_orders
+                ],
+            }
+
+            # Zapisz
+            out_dir = Path(__file__).parent / "output"
+            out_dir.mkdir(exist_ok=True)
+            out_path = out_dir / f"shopify_{domain}_{args.month}.json"
+            with open(out_path, "w", encoding="utf-8") as f:
+                json.dump(result, f, indent=2, ensure_ascii=False)
+
+            print(f"\n=== WYNIK ===")
+            print(f"Zamówienia: {total_orders}")
+            print(f"Przychód: {total_revenue:.2f} PLN")
+            print(f"AOV: {aov:.2f} PLN")
+            print(f"Zapisano: {out_path}")
+        else:
+            print("\nNie udało się automatycznie pobrać danych.")
+            print("Sprawdź screenshoty i spróbuj ręcznie podać dane.")
+
+        browser.close()
+
+
+if __name__ == "__main__":
+    main()

scripts/reports/generate_ga4_token.py

@@ -0,0 +1,74 @@
+"""
+Generate OAuth2 refresh token with GA4 Analytics scope.
+Manual approach - no local server needed.
+"""
+import os
+import urllib.parse
+import requests
+from dotenv import load_dotenv
+
+load_dotenv()
+
+CLIENT_ID = os.getenv("GOOGLE_ADS_OAUTH2_CLIENT_ID")
+CLIENT_SECRET = os.getenv("GOOGLE_ADS_OAUTH2_CLIENT_SECRET")
+
+SCOPES = "https://www.googleapis.com/auth/analytics.readonly"
+REDIRECT_URI = "http://localhost"
+
+# Step 1: Build auth URL
+params = {
+    "client_id": CLIENT_ID,
+    "redirect_uri": REDIRECT_URI,
+    "response_type": "code",
+    "scope": SCOPES,
+    "access_type": "offline",
+    "prompt": "consent",
+}
+auth_url = "https://accounts.google.com/o/oauth2/auth?" + urllib.parse.urlencode(params)
+
+print("=" * 60)
+print("KROK 1: Otworz ten URL w przegladarce:")
+print("=" * 60)
+print(auth_url)
+print("=" * 60)
+print()
+print("KROK 2: Zaloguj sie i zezwol na dostep.")
+print("Przegladarka przekieruje na http://localhost/?code=XXXXXX")
+print("Strona NIE zaladuje sie (to normalne!).")
+print("Skopiuj CALY URL z paska adresu przegladarki i wklej tutaj:")
+print()
+
+redirect_url = input("Wklej URL z paska adresu: ").strip()
+
+# Step 2: Extract code from URL
+parsed = urllib.parse.urlparse(redirect_url)
+query_params = urllib.parse.parse_qs(parsed.query)
+
+if "code" not in query_params:
+    print("Blad: nie znaleziono kodu w URL. Upewnij sie ze skopiowales caly URL.")
+    exit(1)
+
+code = query_params["code"][0]
+
+# Step 3: Exchange code for tokens
+token_response = requests.post("https://oauth2.googleapis.com/token", data={
+    "code": code,
+    "client_id": CLIENT_ID,
+    "client_secret": CLIENT_SECRET,
+    "redirect_uri": REDIRECT_URI,
+    "grant_type": "authorization_code",
+})
+
+if token_response.status_code != 200:
+    print(f"Blad: {token_response.text}")
+    exit(1)
+
+tokens = token_response.json()
+refresh_token = tokens.get("refresh_token")
+
+print()
+print("=" * 60)
+print("GA4 REFRESH TOKEN:")
+print("=" * 60)
+print(refresh_token)
+print("=" * 60)

scripts/reports/list_ga4_properties.py

@@ -0,0 +1,32 @@
+"""List all GA4 properties accessible by this token."""
+import os
+from dotenv import load_dotenv
+from google.oauth2.credentials import Credentials
+from google.analytics.admin_v1beta import AnalyticsAdminServiceClient
+
+load_dotenv()
+
+CLIENT_ID = os.getenv("GOOGLE_ADS_OAUTH2_CLIENT_ID")
+CLIENT_SECRET = os.getenv("GOOGLE_ADS_OAUTH2_CLIENT_SECRET")
+REFRESH_TOKEN = os.getenv("GA4_REFRESH_TOKEN")
+
+credentials = Credentials(
+    token=None,
+    refresh_token=REFRESH_TOKEN,
+    client_id=CLIENT_ID,
+    client_secret=CLIENT_SECRET,
+    token_uri="https://oauth2.googleapis.com/token",
+)
+
+client = AnalyticsAdminServiceClient(credentials=credentials)
+
+print("Accounts:")
+print("-" * 60)
+for account in client.list_accounts():
+    print(f"  Account: {account.name} | {account.display_name}")
+
+    print("  Properties:")
+    request = {"filter": f"parent:{account.name}"}
+    for prop in client.list_properties(request=request):
+        print(f"    Property ID: {prop.name.replace('properties/', '')} | {prop.display_name}")
+    print()

scripts/reports/output/aruba.rzeszow.pl/2026-02/index.html

@@ -0,0 +1,716 @@
+<!DOCTYPE html>
+<html lang="pl">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Raport Luty 2026 &mdash; Aruba Rzeszow</title>
+    <script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js"></script>
+    <style>
+        :root {
+            --primary: #0d8b8b;
+            --primary-dark: #065a5a;
+            --primary-light: #e8f5f5;
+            --green: #27ae60;
+            --red: #e74c3c;
+            --gray: #6c757d;
+            --light-gray: #f8f9fa;
+            --border: #e9ecef;
+        }
+
+        * { margin: 0; padding: 0; box-sizing: border-box; }
+
+        body {
+            font-family: system-ui, -apple-system, 'Segoe UI', Roboto, sans-serif;
+            color: #333;
+            background: #f5f5f5;
+            line-height: 1.6;
+        }
+
+        .hero {
+            background: linear-gradient(135deg, var(--primary-dark) 0%, var(--primary) 60%, #10a5a5 100%);
+            color: white;
+            padding: 60px 40px;
+            position: relative;
+            overflow: hidden;
+        }
+
+        .hero::before {
+            content: '';
+            position: absolute;
+            top: 0; right: 0;
+            width: 50%;
+            height: 100%;
+            background: url("data:image/svg+xml,%3Csvg width='400' height='400' xmlns='http://www.w3.org/2000/svg'%3E%3Cdefs%3E%3Cpattern id='grid' width='40' height='40' patternUnits='userSpaceOnUse'%3E%3Ccircle cx='20' cy='20' r='1.5' fill='rgba(255,255,255,0.15)'/%3E%3C/pattern%3E%3C/defs%3E%3Crect width='400' height='400' fill='url(%23grid)'/%3E%3Cline x1='20' y1='60' x2='100' y2='20' stroke='rgba(255,255,255,0.08)' stroke-width='1'/%3E%3Cline x1='100' y1='20' x2='180' y2='80' stroke='rgba(255,255,255,0.08)' stroke-width='1'/%3E%3Cline x1='180' y1='80' x2='260' y2='40' stroke='rgba(255,255,255,0.08)' stroke-width='1'/%3E%3Cline x1='260' y1='40' x2='340' y2='100' stroke='rgba(255,255,255,0.08)' stroke-width='1'/%3E%3Cline x1='60' y1='140' x2='140' y2='120' stroke='rgba(255,255,255,0.06)' stroke-width='1'/%3E%3Cline x1='140' y1='120' x2='220' y2='180' stroke='rgba(255,255,255,0.06)' stroke-width='1'/%3E%3Cline x1='220' y1='180' x2='300' y2='140' stroke='rgba(255,255,255,0.06)' stroke-width='1'/%3E%3Cline x1='300' y1='140' x2='380' y2='200' stroke='rgba(255,255,255,0.06)' stroke-width='1'/%3E%3C/svg%3E") repeat;
+            opacity: 0.7;
+        }
+
+        .hero-content {
+            max-width: 1400px;
+            margin: 0 auto;
+            position: relative;
+            z-index: 1;
+        }
+
+        .logo {
+            display: flex;
+            align-items: center;
+            gap: 12px;
+            margin-bottom: 50px;
+        }
+
+        .logo svg {
+            height: 40px;
+        }
+
+        .logo-text {
+            font-size: 22px;
+            font-weight: 700;
+            letter-spacing: 3px;
+            text-transform: uppercase;
+        }
+
+        .logo-subtitle {
+            font-size: 11px;
+            letter-spacing: 2px;
+            opacity: 0.85;
+            text-transform: lowercase;
+        }
+
+        .hero h1 {
+            font-size: 42px;
+            font-weight: 700;
+            line-height: 1.2;
+            margin-bottom: 16px;
+        }
+
+        .hero .meta {
+            font-size: 18px;
+            opacity: 0.9;
+        }
+
+        .container {
+            max-width: 1400px;
+            margin: 0 auto;
+            padding: 0 20px;
+        }
+
+        .report-section {
+            background: white;
+            border-radius: 12px;
+            padding: 32px;
+            margin: 24px auto;
+            max-width: 1400px;
+            box-shadow: 0 1px 3px rgba(0,0,0,0.08);
+        }
+
+        .section-title {
+            color: var(--primary-dark);
+            font-size: 24px;
+            font-weight: 700;
+            margin-bottom: 24px;
+            padding-bottom: 12px;
+            border-bottom: 3px solid var(--primary);
+        }
+
+        .kpi-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(170px, 1fr));
+            gap: 16px;
+            margin-bottom: 32px;
+        }
+
+        .kpi-card {
+            background: var(--light-gray);
+            border-radius: 10px;
+            padding: 20px;
+            text-align: center;
+            border: 1px solid var(--border);
+        }
+
+        .kpi-label {
+            font-size: 12px;
+            color: var(--primary);
+            text-transform: uppercase;
+            font-weight: 600;
+            letter-spacing: 0.5px;
+            margin-bottom: 8px;
+        }
+
+        .kpi-value {
+            font-size: 28px;
+            font-weight: 700;
+            color: #2c3e50;
+            white-space: nowrap;
+        }
+
+        .kpi-unit {
+            font-size: 14px;
+            font-weight: 400;
+            color: var(--gray);
+        }
+
+        .kpi-change {
+            font-size: 13px;
+            margin-top: 6px;
+            font-weight: 500;
+        }
+
+        .chart-container {
+            margin: 24px 0;
+        }
+
+        .chart-container h3 {
+            color: var(--primary-dark);
+            margin-bottom: 12px;
+            font-size: 16px;
+        }
+
+        .chart-container canvas {
+            max-height: 300px;
+        }
+
+        .data-table {
+            width: 100%;
+            border-collapse: collapse;
+            font-size: 14px;
+        }
+
+        .data-table thead {
+            background: var(--primary);
+            color: white;
+        }
+
+        .data-table th {
+            padding: 10px 12px;
+            text-align: left;
+            font-weight: 600;
+            font-size: 12px;
+            text-transform: uppercase;
+            letter-spacing: 0.5px;
+        }
+
+        .data-table td {
+            padding: 10px 12px;
+            border-bottom: 1px solid var(--border);
+        }
+
+        .data-table tbody tr:hover {
+            background: var(--primary-light);
+        }
+
+        .data-table .num {
+            text-align: right;
+            font-variant-numeric: tabular-nums;
+            white-space: nowrap;
+        }
+
+        .badge {
+            display: inline-block;
+            padding: 2px 8px;
+            border-radius: 4px;
+            font-size: 11px;
+            font-weight: 600;
+        }
+
+        .badge-search { background: #dbeafe; color: #1e40af; }
+        .badge-performance_max { background: #fef3c7; color: #92400e; }
+        .badge-shopping { background: #d1fae5; color: #065f46; }
+        .badge-display { background: #ede9fe; color: #5b21b6; }
+        .badge-demand_gen { background: #fce7f3; color: #9d174d; }
+
+        .summary-box {
+            background: var(--primary-light);
+            border-left: 4px solid var(--primary);
+            padding: 24px;
+            border-radius: 0 10px 10px 0;
+            font-size: 15px;
+            line-height: 1.8;
+        }
+
+        .two-col {
+            display: grid;
+            grid-template-columns: 1fr 1fr;
+            gap: 24px;
+            margin-top: 24px;
+        }
+
+        .bar {
+            height: 8px;
+            background: var(--primary);
+            border-radius: 4px;
+            min-width: 4px;
+        }
+
+        .recommendations-list {
+            display: flex;
+            flex-direction: column;
+            gap: 16px;
+        }
+
+        .rec-item {
+            display: flex;
+            gap: 16px;
+            padding: 16px;
+            background: var(--light-gray);
+            border-radius: 8px;
+            border-left: 4px solid var(--primary);
+        }
+
+        .rec-icon {
+            font-size: 20px;
+            flex-shrink: 0;
+            margin-top: 2px;
+        }
+
+        .rec-item p {
+            margin-top: 4px;
+            color: var(--gray);
+            font-size: 14px;
+        }
+
+        .questions-box {
+            background: linear-gradient(135deg, #fff8e1 0%, #fff3cd 100%);
+            border-left: 4px solid #f59e0b;
+            border-radius: 10px;
+            padding: 24px 28px;
+            margin-top: 16px;
+        }
+        .questions-box h3 {
+            margin: 0 0 8px 0;
+            color: #92400e;
+            font-size: 18px;
+        }
+        .questions-box .intro {
+            color: #78350f;
+            font-size: 14px;
+            margin-bottom: 16px;
+        }
+        .questions-list {
+            display: flex;
+            flex-direction: column;
+            gap: 14px;
+        }
+        .q-item {
+            display: flex;
+            gap: 14px;
+            padding: 14px 16px;
+            background: rgba(255,255,255,0.7);
+            border-radius: 8px;
+        }
+        .q-num {
+            flex-shrink: 0;
+            width: 28px;
+            height: 28px;
+            border-radius: 50%;
+            background: #f59e0b;
+            color: white;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            font-weight: 700;
+            font-size: 14px;
+        }
+        .q-item strong { color: #92400e; display: block; margin-bottom: 4px; }
+        .q-item p { margin: 0; color: #57534e; font-size: 14px; line-height: 1.6; }
+
+        .footer {
+            text-align: center;
+            padding: 32px;
+            color: var(--gray);
+            font-size: 13px;
+        }
+
+        .footer a {
+            color: var(--primary);
+            text-decoration: none;
+        }
+
+        @media (max-width: 768px) {
+            .hero { padding: 40px 20px; }
+            .hero h1 { font-size: 28px; }
+            .kpi-grid { grid-template-columns: repeat(2, 1fr); }
+            .two-col { grid-template-columns: 1fr; }
+            .report-section { padding: 20px; margin: 12px; }
+        }
+
+        @media print {
+            body { background: white; }
+            .report-section { box-shadow: none; page-break-inside: avoid; }
+            .hero { padding: 30px; }
+        }
+    </style>
+</head>
+<body>
+
+    <!-- HERO / TITLE -->
+    <header class="hero">
+        <div class="hero-content">
+            <div class="logo">
+                <img src="https://www.project-pro.pl/upload/filemanager/Project-Design/logos/project-pro/logo-white.svg" alt="Project-Pro" style="height: 40px;">
+            </div>
+            <h1>Raport z działań marketingowych</h1>
+            <div class="meta">Aruba Rzeszow &mdash; Luty 2026</div>
+        </div>
+    </header>
+
+    <!-- RECOMMENDATIONS -->
+    
+    <section class="report-section" id="recommendations">
+        <h2 class="section-title">Wnioski i rekomendacje</h2>
+        <div class="recommendations-list">
+            <div class="rec-item"><span class="rec-icon">&#9888;</span><div><strong>Spadek konwersji do obserwacji</strong><p>Liczba konwersji spadla o 30.2% miesiac do miesiaca. Rekomendujemy sprawdzenie kampanii o najwiekszym spadku wolumenu.</p></div></div><div class="rec-item"><span class="rec-icon">&#128200;</span><div><strong>ROAS liczony z Google Ads</strong><p>ROAS z Google Ads wyniosl 9.50. Ten wskaznik liczymy z wartosci konwersji Google Ads, nie z przychodow sklepu.</p></div></div>
+        </div>
+    </section>
+    
+
+    
+
+    <!-- GA4 SECTION -->
+    
+
+    <!-- E-COMMERCE -->
+    
+
+    <!-- PRODUCT OPTIMIZATIONS -->
+    
+
+    <!-- TOP ADS PRODUCTS -->
+    
+
+    <!-- YEAR OVER YEAR -->
+    
+
+    <!-- GOOGLE ADS KPIs -->
+    <section class="report-section" id="ads-kpi">
+        <h2 class="section-title">Google Ads &mdash; Podsumowanie</h2>
+        <div class="kpi-grid">
+        <div class="kpi-card">
+            <div class="kpi-label">Wyświetlenia</div>
+            <div class="kpi-value">181 763<span class="kpi-unit"></span></div>
+            <div class="kpi-change" style="color: #e74c3c">
+                &#9660; -12.6% vs Styczeń
+            </div>
+        </div>
+        <div class="kpi-card">
+            <div class="kpi-label">Kliknięcia</div>
+            <div class="kpi-value">4 628<span class="kpi-unit"></span></div>
+            <div class="kpi-change" style="color: #e74c3c">
+                &#9660; -27.0% vs Styczeń
+            </div>
+        </div>
+        <div class="kpi-card">
+            <div class="kpi-label">CTR</div>
+            <div class="kpi-value">2.5<span class="kpi-unit">%</span></div>
+            <div class="kpi-change" style="color: #e74c3c">
+                &#9660; -16.4% vs Styczeń
+            </div>
+        </div>
+        <div class="kpi-card">
+            <div class="kpi-label">Konwersje</div>
+            <div class="kpi-value">214<span class="kpi-unit"></span></div>
+            <div class="kpi-change" style="color: #e74c3c">
+                &#9660; -30.2% vs Styczeń
+            </div>
+        </div>
+        <div class="kpi-card">
+            <div class="kpi-label">Koszt</div>
+            <div class="kpi-value">3788.97<span class="kpi-unit"> PLN</span></div>
+            <div class="kpi-change" style="color: #27ae60">
+                &#9660; -0.3% vs Styczeń
+            </div>
+        </div>
+        <div class="kpi-card">
+            <div class="kpi-label">CPA</div>
+            <div class="kpi-value">17.63<span class="kpi-unit"> PLN</span></div>
+            <div class="kpi-change" style="color: #e74c3c">
+                &#9650; +42.8% vs Styczeń
+            </div>
+        </div>
+        <div class="kpi-card">
+            <div class="kpi-label">ROAS</div>
+            <div class="kpi-value">9.50<span class="kpi-unit">x</span></div>
+            <div class="kpi-change" style="color: #e74c3c">
+                &#9660; -30.7% vs Styczeń
+            </div>
+        </div></div>
+    </section>
+
+    <!-- DAILY CHART -->
+    <section class="report-section" id="ads-chart">
+        <h2 class="section-title">Google Ads &mdash; Aktywność dzienna</h2>
+        <div class="chart-container">
+            <canvas id="dailyClicksChart"></canvas>
+        </div>
+    </section>
+
+    <!-- CAMPAIGNS TABLE -->
+    <section class="report-section" id="campaigns">
+        <h2 class="section-title">Kampanie</h2>
+        <table class="data-table">
+            <thead>
+                <tr>
+                    <th>Kampania</th>
+                    <th>Typ</th>
+                    <th>Wyświetlenia</th>
+                    <th>Kliknięcia</th>
+                    <th>CTR</th>
+                    <th>Konwersje</th>
+                    <th>Koszt</th>
+                    <th>CPA</th>
+                </tr>
+            </thead>
+            <tbody>
+            <tr>
+                <td>[Search] brand</td>
+                <td><span class="badge badge-search">SEARCH</span></td>
+                <td class="num">1 572</td>
+                <td class="num">495</td>
+                <td class="num">31.5%</td>
+                <td class="num">24</td>
+                <td class="num">430.53 PLN</td>
+                <td class="num">17.94 PLN</td>
+            </tr>
+            <tr>
+                <td>[DSA] produkty</td>
+                <td><span class="badge badge-search">SEARCH</span></td>
+                <td class="num">16 608</td>
+                <td class="num">1 208</td>
+                <td class="num">7.3%</td>
+                <td class="num">12</td>
+                <td class="num">445.16 PLN</td>
+                <td class="num">37.41 PLN</td>
+            </tr>
+            <tr>
+                <td>[PMax] products (catch-all)</td>
+                <td><span class="badge badge-performance_max">PERFORMANCE_MAX</span></td>
+                <td class="num">158 661</td>
+                <td class="num">2 886</td>
+                <td class="num">1.8%</td>
+                <td class="num">178</td>
+                <td class="num">2828.52 PLN</td>
+                <td class="num">15.89 PLN</td>
+            </tr>
+            <tr>
+                <td>[PLA] produkty (bestsellers)</td>
+                <td><span class="badge badge-shopping">SHOPPING</span></td>
+                <td class="num">4 922</td>
+                <td class="num">39</td>
+                <td class="num">0.8%</td>
+                <td class="num">1</td>
+                <td class="num">84.76 PLN</td>
+                <td class="num">84.76 PLN</td>
+            </tr></tbody>
+        </table>
+    </section>
+
+    <!-- SEARCH TERMS -->
+    <section class="report-section" id="search-terms">
+        <h2 class="section-title">Najpopularniejsze frazy wyszukiwania</h2>
+        <table class="data-table">
+            <thead>
+                <tr>
+                    <th>#</th>
+                    <th>Fraza</th>
+                    <th>Wyświetlenia</th>
+                    <th>Kliknięcia</th>
+                    <th>CTR</th>
+                    <th>Konwersje</th>
+                </tr>
+            </thead>
+            <tbody>
+            <tr>
+                <td class="num">1</td>
+                <td>aruba rzeszów</td>
+                <td class="num">770</td>
+                <td class="num">251</td>
+                <td class="num">32.6%</td>
+                <td class="num">11</td>
+            </tr>
+            <tr>
+                <td class="num">2</td>
+                <td>aruba hurtownia</td>
+                <td class="num">113</td>
+                <td class="num">45</td>
+                <td class="num">39.8%</td>
+                <td class="num">2</td>
+            </tr>
+            <tr>
+                <td class="num">3</td>
+                <td>onygen krem</td>
+                <td class="num">1 114</td>
+                <td class="num">34</td>
+                <td class="num">3.0%</td>
+                <td class="num">1</td>
+            </tr>
+            <tr>
+                <td class="num">4</td>
+                <td>aruba rzeszow</td>
+                <td class="num">117</td>
+                <td class="num">29</td>
+                <td class="num">24.8%</td>
+                <td class="num">2</td>
+            </tr>
+            <tr>
+                <td class="num">5</td>
+                <td>aruba sklep</td>
+                <td class="num">54</td>
+                <td class="num">29</td>
+                <td class="num">53.7%</td>
+                <td class="num">3</td>
+            </tr>
+            <tr>
+                <td class="num">6</td>
+                <td>makijaż permanentny brwi</td>
+                <td class="num">217</td>
+                <td class="num">20</td>
+                <td class="num">9.2%</td>
+                <td class="num">0</td>
+            </tr>
+            <tr>
+                <td class="num">7</td>
+                <td>autoklaw</td>
+                <td class="num">98</td>
+                <td class="num">18</td>
+                <td class="num">18.4%</td>
+                <td class="num">0</td>
+            </tr>
+            <tr>
+                <td class="num">8</td>
+                <td>brwi permanentne</td>
+                <td class="num">231</td>
+                <td class="num">18</td>
+                <td class="num">7.8%</td>
+                <td class="num">0</td>
+            </tr>
+            <tr>
+                <td class="num">9</td>
+                <td>aruba kosmetyki</td>
+                <td class="num">30</td>
+                <td class="num">15</td>
+                <td class="num">50.0%</td>
+                <td class="num">0</td>
+            </tr>
+            <tr>
+                <td class="num">10</td>
+                <td>hurtownia aruba</td>
+                <td class="num">52</td>
+                <td class="num">14</td>
+                <td class="num">26.9%</td>
+                <td class="num">0</td>
+            </tr>
+            <tr>
+                <td class="num">11</td>
+                <td>radiofrekwencja mikroigłowa</td>
+                <td class="num">342</td>
+                <td class="num">14</td>
+                <td class="num">4.1%</td>
+                <td class="num">0</td>
+            </tr>
+            <tr>
+                <td class="num">12</td>
+                <td>hurtownia aruba rzeszów</td>
+                <td class="num">48</td>
+                <td class="num">13</td>
+                <td class="num">27.1%</td>
+                <td class="num">1</td>
+            </tr>
+            <tr>
+                <td class="num">13</td>
+                <td>pielęgnacja brwi po makijażu permanentnym</td>
+                <td class="num">85</td>
+                <td class="num">13</td>
+                <td class="num">15.3%</td>
+                <td class="num">0</td>
+            </tr>
+            <tr>
+                <td class="num">14</td>
+                <td>gen factor</td>
+                <td class="num">236</td>
+                <td class="num">11</td>
+                <td class="num">4.7%</td>
+                <td class="num">0</td>
+            </tr>
+            <tr>
+                <td class="num">15</td>
+                <td>aruba hurtownia kosmetyczna</td>
+                <td class="num">22</td>
+                <td class="num">10</td>
+                <td class="num">45.5%</td>
+                <td class="num">2</td>
+            </tr></tbody>
+        </table>
+    </section>
+
+    <!-- NEGATIVE KEYWORDS ADDED -->
+    
+
+    <!-- SEMSTORM SEO -->
+    
+
+    <!-- SEO ACTIVITIES -->
+    
+
+    <!-- SEO LINKS -->
+    
+
+    <!-- SUMMARY -->
+    <section class="report-section" id="summary">
+        <h2 class="section-title">Podsumowanie miesiąca</h2>
+        <div class="summary-box">
+            Odnotowano 214 konwersji w tym miesiącu.
+        </div>
+    </section>
+
+    <!-- RECOMMENDATIONS moved to top -->
+
+    <!-- FOOTER -->
+    <div class="footer">
+        Raport wygenerowany przez <a href="https://www.project-pro.pl">Project-Pro</a> &mdash; marketing w wersji PRO
+    </div>
+
+    <script>
+        // Daily clicks chart
+        var ctx1 = document.getElementById('dailyClicksChart').getContext('2d');
+        new Chart(ctx1, {
+            type: 'line',
+            data: {
+                labels: ["02-01", "02-02", "02-03", "02-04", "02-05", "02-06", "02-07", "02-08", "02-09", "02-10", "02-11", "02-12", "02-13", "02-14", "02-15", "02-16", "02-17", "02-18", "02-19", "02-20", "02-21", "02-22", "02-23", "02-24", "02-25", "02-26", "02-27", "02-28"],
+                datasets: [{
+                    label: 'Kliknięcia',
+                    data: [210, 164, 188, 242, 204, 198, 163, 208, 202, 206, 193, 169, 153, 113, 139, 174, 148, 137, 112, 143, 139, 151, 160, 184, 157, 141, 141, 89],
+                    borderColor: '#0d8b8b',
+                    backgroundColor: 'rgba(13,139,139,0.1)',
+                    fill: true,
+                    tension: 0.3,
+                    pointRadius: 3,
+                    pointBackgroundColor: '#0d8b8b',
+                }, {
+                    label: 'Wyświetlenia',
+                    data: [7761, 8752, 6894, 6890, 7048, 8251, 6007, 8393, 6761, 8531, 6071, 5122, 6360, 4092, 5897, 6193, 6761, 6894, 5773, 6152, 6529, 5916, 7070, 7262, 6054, 4538, 5064, 4727],
+                    borderColor: '#95a5a6',
+                    backgroundColor: 'transparent',
+                    borderDash: [5, 5],
+                    tension: 0.3,
+                    pointRadius: 0,
+                    yAxisID: 'y1',
+                }]
+            },
+            options: {
+                responsive: true,
+                interaction: { mode: 'index', intersect: false },
+                scales: {
+                    y: { beginAtZero: true, position: 'left', grid: { color: '#f0f0f0' } },
+                    y1: { beginAtZero: true, position: 'right', grid: { display: false } },
+                    x: { grid: { display: false }, ticks: { maxTicksLimit: 10 } }
+                }
+            }
+        });
+
+        
+        
+        
+        
+    </script>
+
+</body>
+</html>

scripts/reports/output/aruba.rzeszow.pl/2026-04/index.html

@@ -0,0 +1,716 @@
+<!DOCTYPE html>
+<html lang="pl">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Raport Kwiecień 2026 &mdash; Aruba Rzeszow</title>
+    <script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js"></script>
+    <style>
+        :root {
+            --primary: #0d8b8b;
+            --primary-dark: #065a5a;
+            --primary-light: #e8f5f5;
+            --green: #27ae60;
+            --red: #e74c3c;
+            --gray: #6c757d;
+            --light-gray: #f8f9fa;
+            --border: #e9ecef;
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+
+        * { margin: 0; padding: 0; box-sizing: border-box; }
+
+        body {
+            font-family: system-ui, -apple-system, 'Segoe UI', Roboto, sans-serif;
+            color: #333;
+            background: #f5f5f5;
+            line-height: 1.6;
+        }
+
+        .hero {
+            background: linear-gradient(135deg, var(--primary-dark) 0%, var(--primary) 60%, #10a5a5 100%);
+            color: white;
+            padding: 60px 40px;
+            position: relative;
+            overflow: hidden;
+        }
+
+        .hero::before {
+            content: '';
+            position: absolute;
+            top: 0; right: 0;
+            width: 50%;
+            height: 100%;
+            background: url("data:image/svg+xml,%3Csvg width='400' height='400' xmlns='http://www.w3.org/2000/svg'%3E%3Cdefs%3E%3Cpattern id='grid' width='40' height='40' patternUnits='userSpaceOnUse'%3E%3Ccircle cx='20' cy='20' r='1.5' fill='rgba(255,255,255,0.15)'/%3E%3C/pattern%3E%3C/defs%3E%3Crect width='400' height='400' fill='url(%23grid)'/%3E%3Cline x1='20' y1='60' x2='100' y2='20' stroke='rgba(255,255,255,0.08)' stroke-width='1'/%3E%3Cline x1='100' y1='20' x2='180' y2='80' stroke='rgba(255,255,255,0.08)' stroke-width='1'/%3E%3Cline x1='180' y1='80' x2='260' y2='40' stroke='rgba(255,255,255,0.08)' stroke-width='1'/%3E%3Cline x1='260' y1='40' x2='340' y2='100' stroke='rgba(255,255,255,0.08)' stroke-width='1'/%3E%3Cline x1='60' y1='140' x2='140' y2='120' stroke='rgba(255,255,255,0.06)' stroke-width='1'/%3E%3Cline x1='140' y1='120' x2='220' y2='180' stroke='rgba(255,255,255,0.06)' stroke-width='1'/%3E%3Cline x1='220' y1='180' x2='300' y2='140' stroke='rgba(255,255,255,0.06)' stroke-width='1'/%3E%3Cline x1='300' y1='140' x2='380' y2='200' stroke='rgba(255,255,255,0.06)' stroke-width='1'/%3E%3C/svg%3E") repeat;
+            opacity: 0.7;
+        }
+
+        .hero-content {
+            max-width: 1400px;
+            margin: 0 auto;
+            position: relative;
+            z-index: 1;
+        }
+
+        .logo {
+            display: flex;
+            align-items: center;
+            gap: 12px;
+            margin-bottom: 50px;
+        }
+
+        .logo svg {
+            height: 40px;
+        }
+
+        .logo-text {
+            font-size: 22px;
+            font-weight: 700;
+            letter-spacing: 3px;
+            text-transform: uppercase;
+        }
+
+        .logo-subtitle {
+            font-size: 11px;
+            letter-spacing: 2px;
+            opacity: 0.85;
+            text-transform: lowercase;
+        }
+
+        .hero h1 {
+            font-size: 42px;
+            font-weight: 700;
+            line-height: 1.2;
+            margin-bottom: 16px;
+        }
+
+        .hero .meta {
+            font-size: 18px;
+            opacity: 0.9;
+        }
+
+        .container {
+            max-width: 1400px;
+            margin: 0 auto;
+            padding: 0 20px;
+        }
+
+        .report-section {
+            background: white;
+            border-radius: 12px;
+            padding: 32px;
+            margin: 24px auto;
+            max-width: 1400px;
+            box-shadow: 0 1px 3px rgba(0,0,0,0.08);
+        }
+
+        .section-title {
+            color: var(--primary-dark);
+            font-size: 24px;
+            font-weight: 700;
+            margin-bottom: 24px;
+            padding-bottom: 12px;
+            border-bottom: 3px solid var(--primary);
+        }
+
+        .kpi-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(170px, 1fr));
+            gap: 16px;
+            margin-bottom: 32px;
+        }
+
+        .kpi-card {
+            background: var(--light-gray);
+            border-radius: 10px;
+            padding: 20px;
+            text-align: center;
+            border: 1px solid var(--border);
+        }
+
+        .kpi-label {
+            font-size: 12px;
+            color: var(--primary);
+            text-transform: uppercase;
+            font-weight: 600;
+            letter-spacing: 0.5px;
+            margin-bottom: 8px;
+        }
+
+        .kpi-value {
+            font-size: 28px;
+            font-weight: 700;
+            color: #2c3e50;
+            white-space: nowrap;
+        }
+
+        .kpi-unit {
+            font-size: 14px;
+            font-weight: 400;
+            color: var(--gray);
+        }
+
+        .kpi-change {
+            font-size: 13px;
+            margin-top: 6px;
+            font-weight: 500;
+        }
+
+        .chart-container {
+            margin: 24px 0;
+        }
+
+        .chart-container h3 {
+            color: var(--primary-dark);
+            margin-bottom: 12px;
+            font-size: 16px;
+        }
+
+        .chart-container canvas {
+            max-height: 300px;
+        }
+
+        .data-table {
+            width: 100%;
+            border-collapse: collapse;
+            font-size: 14px;
+        }
+
+        .data-table thead {
+            background: var(--primary);
+            color: white;
+        }
+
+        .data-table th {
+            padding: 10px 12px;
+            text-align: left;
+            font-weight: 600;
+            font-size: 12px;
+            text-transform: uppercase;
+            letter-spacing: 0.5px;
+        }
+
+        .data-table td {
+            padding: 10px 12px;
+            border-bottom: 1px solid var(--border);
+        }
+
+        .data-table tbody tr:hover {
+            background: var(--primary-light);
+        }
+
+        .data-table .num {
+            text-align: right;
+            font-variant-numeric: tabular-nums;
+            white-space: nowrap;
+        }
+
+        .badge {
+            display: inline-block;
+            padding: 2px 8px;
+            border-radius: 4px;
+            font-size: 11px;
+            font-weight: 600;
+        }
+
+        .badge-search { background: #dbeafe; color: #1e40af; }
+        .badge-performance_max { background: #fef3c7; color: #92400e; }
+        .badge-shopping { background: #d1fae5; color: #065f46; }
+        .badge-display { background: #ede9fe; color: #5b21b6; }
+        .badge-demand_gen { background: #fce7f3; color: #9d174d; }
+
+        .summary-box {
+            background: var(--primary-light);
+            border-left: 4px solid var(--primary);
+            padding: 24px;
+            border-radius: 0 10px 10px 0;
+            font-size: 15px;
+            line-height: 1.8;
+        }
+
+        .two-col {
+            display: grid;
+            grid-template-columns: 1fr 1fr;
+            gap: 24px;
+            margin-top: 24px;
+        }
+
+        .bar {
+            height: 8px;
+            background: var(--primary);
+            border-radius: 4px;
+            min-width: 4px;
+        }
+
+        .recommendations-list {
+            display: flex;
+            flex-direction: column;
+            gap: 16px;
+        }
+
+        .rec-item {
+            display: flex;
+            gap: 16px;
+            padding: 16px;
+            background: var(--light-gray);
+            border-radius: 8px;
+            border-left: 4px solid var(--primary);
+        }
+
+        .rec-icon {
+            font-size: 20px;
+            flex-shrink: 0;
+            margin-top: 2px;
+        }
+
+        .rec-item p {
+            margin-top: 4px;
+            color: var(--gray);
+            font-size: 14px;
+        }
+
+        .questions-box {
+            background: linear-gradient(135deg, #fff8e1 0%, #fff3cd 100%);
+            border-left: 4px solid #f59e0b;
+            border-radius: 10px;
+            padding: 24px 28px;
+            margin-top: 16px;
+        }
+        .questions-box h3 {
+            margin: 0 0 8px 0;
+            color: #92400e;
+            font-size: 18px;
+        }
+        .questions-box .intro {
+            color: #78350f;
+            font-size: 14px;
+            margin-bottom: 16px;
+        }
+        .questions-list {
+            display: flex;
+            flex-direction: column;
+            gap: 14px;
+        }
+        .q-item {
+            display: flex;
+            gap: 14px;
+            padding: 14px 16px;
+            background: rgba(255,255,255,0.7);
+            border-radius: 8px;
+        }
+        .q-num {
+            flex-shrink: 0;
+            width: 28px;
+            height: 28px;
+            border-radius: 50%;
+            background: #f59e0b;
+            color: white;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            font-weight: 700;
+            font-size: 14px;
+        }
+        .q-item strong { color: #92400e; display: block; margin-bottom: 4px; }
+        .q-item p { margin: 0; color: #57534e; font-size: 14px; line-height: 1.6; }
+
+        .footer {
+            text-align: center;
+            padding: 32px;
+            color: var(--gray);
+            font-size: 13px;
+        }
+
+        .footer a {
+            color: var(--primary);
+            text-decoration: none;
+        }
+
+        @media (max-width: 768px) {
+            .hero { padding: 40px 20px; }
+            .hero h1 { font-size: 28px; }
+            .kpi-grid { grid-template-columns: repeat(2, 1fr); }
+            .two-col { grid-template-columns: 1fr; }
+            .report-section { padding: 20px; margin: 12px; }
+        }
+
+        @media print {
+            body { background: white; }
+            .report-section { box-shadow: none; page-break-inside: avoid; }
+            .hero { padding: 30px; }
+        }
+    </style>
+</head>
+<body>
+
+    <!-- HERO / TITLE -->
+    <header class="hero">
+        <div class="hero-content">
+            <div class="logo">
+                <img src="https://www.project-pro.pl/upload/filemanager/Project-Design/logos/project-pro/logo-white.svg" alt="Project-Pro" style="height: 40px;">
+            </div>
+            <h1>Raport z działań marketingowych</h1>
+            <div class="meta">Aruba Rzeszow &mdash; Kwiecień 2026</div>
+        </div>
+    </header>
+
+    <!-- RECOMMENDATIONS -->
+    
+    <section class="report-section" id="recommendations">
+        <h2 class="section-title">Wnioski i rekomendacje</h2>
+        <div class="recommendations-list">
+            <div class="rec-item"><span class="rec-icon">&#9888;</span><div><strong>Spadek konwersji do obserwacji</strong><p>Liczba konwersji spadla o 8.2% miesiac do miesiaca. Rekomendujemy sprawdzenie kampanii o najwiekszym spadku wolumenu.</p></div></div><div class="rec-item"><span class="rec-icon">&#128200;</span><div><strong>ROAS liczony z Google Ads</strong><p>ROAS z Google Ads wyniosl 8.47. Ten wskaznik liczymy z wartosci konwersji Google Ads, nie z przychodow sklepu.</p></div></div><div class="rec-item"><span class="rec-icon">&#128269;</span><div><strong>Kontrola wzrostu kosztu</strong><p>Koszt reklam wzrosl o 12.2% miesiac do miesiaca. Warto porownac wzrost kosztu ze wzrostem konwersji i wartosci konwersji.</p></div></div>
+        </div>
+    </section>
+    
+
+    
+
+    <!-- GA4 SECTION -->
+    
+
+    <!-- E-COMMERCE -->
+    
+
+    <!-- PRODUCT OPTIMIZATIONS -->
+    
+
+    <!-- TOP ADS PRODUCTS -->
+    
+
+    <!-- YEAR OVER YEAR -->
+    
+
+    <!-- GOOGLE ADS KPIs -->
+    <section class="report-section" id="ads-kpi">
+        <h2 class="section-title">Google Ads &mdash; Podsumowanie</h2>
+        <div class="kpi-grid">
+        <div class="kpi-card">
+            <div class="kpi-label">Wyświetlenia</div>
+            <div class="kpi-value">172 277<span class="kpi-unit"></span></div>
+            <div class="kpi-change" style="color: #e74c3c">
+                &#9660; -0.6% vs Marzec
+            </div>
+        </div>
+        <div class="kpi-card">
+            <div class="kpi-label">Kliknięcia</div>
+            <div class="kpi-value">3 826<span class="kpi-unit"></span></div>
+            <div class="kpi-change" style="color: #27ae60">
+                &#9650; +2.5% vs Marzec
+            </div>
+        </div>
+        <div class="kpi-card">
+            <div class="kpi-label">CTR</div>
+            <div class="kpi-value">2.2<span class="kpi-unit">%</span></div>
+            <div class="kpi-change" style="color: #27ae60">
+                &#9650; +3.3% vs Marzec
+            </div>
+        </div>
+        <div class="kpi-card">
+            <div class="kpi-label">Konwersje</div>
+            <div class="kpi-value">199<span class="kpi-unit"></span></div>
+            <div class="kpi-change" style="color: #e74c3c">
+                &#9660; -8.2% vs Marzec
+            </div>
+        </div>
+        <div class="kpi-card">
+            <div class="kpi-label">Koszt</div>
+            <div class="kpi-value">4880.74<span class="kpi-unit"> PLN</span></div>
+            <div class="kpi-change" style="color: #e74c3c">
+                &#9650; +12.2% vs Marzec
+            </div>
+        </div>
+        <div class="kpi-card">
+            <div class="kpi-label">CPA</div>
+            <div class="kpi-value">24.46<span class="kpi-unit"> PLN</span></div>
+            <div class="kpi-change" style="color: #e74c3c">
+                &#9650; +22.2% vs Marzec
+            </div>
+        </div>
+        <div class="kpi-card">
+            <div class="kpi-label">ROAS</div>
+            <div class="kpi-value">8.47<span class="kpi-unit">x</span></div>
+            <div class="kpi-change" style="color: #e74c3c">
+                &#9660; -1.5% vs Marzec
+            </div>
+        </div></div>
+    </section>
+
+    <!-- DAILY CHART -->
+    <section class="report-section" id="ads-chart">
+        <h2 class="section-title">Google Ads &mdash; Aktywność dzienna</h2>
+        <div class="chart-container">
+            <canvas id="dailyClicksChart"></canvas>
+        </div>
+    </section>
+
+    <!-- CAMPAIGNS TABLE -->
+    <section class="report-section" id="campaigns">
+        <h2 class="section-title">Kampanie</h2>
+        <table class="data-table">
+            <thead>
+                <tr>
+                    <th>Kampania</th>
+                    <th>Typ</th>
+                    <th>Wyświetlenia</th>
+                    <th>Kliknięcia</th>
+                    <th>CTR</th>
+                    <th>Konwersje</th>
+                    <th>Koszt</th>
+                    <th>CPA</th>
+                </tr>
+            </thead>
+            <tbody>
+            <tr>
+                <td>[Search] brand</td>
+                <td><span class="badge badge-search">SEARCH</span></td>
+                <td class="num">1 614</td>
+                <td class="num">483</td>
+                <td class="num">29.9%</td>
+                <td class="num">27</td>
+                <td class="num">337.96 PLN</td>
+                <td class="num">12.52 PLN</td>
+            </tr>
+            <tr>
+                <td>[DSA] produkty</td>
+                <td><span class="badge badge-search">SEARCH</span></td>
+                <td class="num">9 984</td>
+                <td class="num">694</td>
+                <td class="num">7.0%</td>
+                <td class="num">23</td>
+                <td class="num">1098.15 PLN</td>
+                <td class="num">47.75 PLN</td>
+            </tr>
+            <tr>
+                <td>[PMax] products (catch-all)</td>
+                <td><span class="badge badge-performance_max">PERFORMANCE_MAX</span></td>
+                <td class="num">138 921</td>
+                <td class="num">2 164</td>
+                <td class="num">1.6%</td>
+                <td class="num">106</td>
+                <td class="num">2762.99 PLN</td>
+                <td class="num">25.94 PLN</td>
+            </tr>
+            <tr>
+                <td>[PLA] produkty (bestsellers)</td>
+                <td><span class="badge badge-shopping">SHOPPING</span></td>
+                <td class="num">21 758</td>
+                <td class="num">485</td>
+                <td class="num">2.2%</td>
+                <td class="num">43</td>
+                <td class="num">681.64 PLN</td>
+                <td class="num">15.85 PLN</td>
+            </tr></tbody>
+        </table>
+    </section>
+
+    <!-- SEARCH TERMS -->
+    <section class="report-section" id="search-terms">
+        <h2 class="section-title">Najpopularniejsze frazy wyszukiwania</h2>
+        <table class="data-table">
+            <thead>
+                <tr>
+                    <th>#</th>
+                    <th>Fraza</th>
+                    <th>Wyświetlenia</th>
+                    <th>Kliknięcia</th>
+                    <th>CTR</th>
+                    <th>Konwersje</th>
+                </tr>
+            </thead>
+            <tbody>
+            <tr>
+                <td class="num">1</td>
+                <td>aruba rzeszów</td>
+                <td class="num">836</td>
+                <td class="num">246</td>
+                <td class="num">29.4%</td>
+                <td class="num">16</td>
+            </tr>
+            <tr>
+                <td class="num">2</td>
+                <td>gen factor</td>
+                <td class="num">858</td>
+                <td class="num">59</td>
+                <td class="num">6.9%</td>
+                <td class="num">1</td>
+            </tr>
+            <tr>
+                <td class="num">3</td>
+                <td>aruba hurtownia</td>
+                <td class="num">122</td>
+                <td class="num">48</td>
+                <td class="num">39.3%</td>
+                <td class="num">4</td>
+            </tr>
+            <tr>
+                <td class="num">4</td>
+                <td>aruba rzeszow</td>
+                <td class="num">127</td>
+                <td class="num">39</td>
+                <td class="num">30.7%</td>
+                <td class="num">0</td>
+            </tr>
+            <tr>
+                <td class="num">5</td>
+                <td>gen factor green</td>
+                <td class="num">207</td>
+                <td class="num">21</td>
+                <td class="num">10.1%</td>
+                <td class="num">2</td>
+            </tr>
+            <tr>
+                <td class="num">6</td>
+                <td>gen factor</td>
+                <td class="num">604</td>
+                <td class="num">21</td>
+                <td class="num">3.5%</td>
+                <td class="num">1</td>
+            </tr>
+            <tr>
+                <td class="num">7</td>
+                <td>verru immuno</td>
+                <td class="num">495</td>
+                <td class="num">19</td>
+                <td class="num">3.8%</td>
+                <td class="num">2</td>
+            </tr>
+            <tr>
+                <td class="num">8</td>
+                <td>aruba sklep</td>
+                <td class="num">48</td>
+                <td class="num">17</td>
+                <td class="num">35.4%</td>
+                <td class="num">1</td>
+            </tr>
+            <tr>
+                <td class="num">9</td>
+                <td>aurumaris</td>
+                <td class="num">113</td>
+                <td class="num">13</td>
+                <td class="num">11.5%</td>
+                <td class="num">0</td>
+            </tr>
+            <tr>
+                <td class="num">10</td>
+                <td>aruba hurtownia kosmetyczna</td>
+                <td class="num">25</td>
+                <td class="num">12</td>
+                <td class="num">48.0%</td>
+                <td class="num">1</td>
+            </tr>
+            <tr>
+                <td class="num">11</td>
+                <td>aruba kosmetyki</td>
+                <td class="num">33</td>
+                <td class="num">12</td>
+                <td class="num">36.4%</td>
+                <td class="num">1</td>
+            </tr>
+            <tr>
+                <td class="num">12</td>
+                <td>gen factor 09</td>
+                <td class="num">47</td>
+                <td class="num">11</td>
+                <td class="num">23.4%</td>
+                <td class="num">0</td>
+            </tr>
+            <tr>
+                <td class="num">13</td>
+                <td>genfactor</td>
+                <td class="num">111</td>
+                <td class="num">11</td>
+                <td class="num">9.9%</td>
+                <td class="num">2</td>
+            </tr>
+            <tr>
+                <td class="num">14</td>
+                <td>podopharm verru immuno</td>
+                <td class="num">230</td>
+                <td class="num">11</td>
+                <td class="num">4.8%</td>
+                <td class="num">2</td>
+            </tr>
+            <tr>
+                <td class="num">15</td>
+                <td>hurtownia aruba</td>
+                <td class="num">32</td>
+                <td class="num">10</td>
+                <td class="num">31.2%</td>
+                <td class="num">0</td>
+            </tr></tbody>
+        </table>
+    </section>
+
+    <!-- NEGATIVE KEYWORDS ADDED -->
+    
+
+    <!-- SEMSTORM SEO -->
+    
+
+    <!-- SEO ACTIVITIES -->
+    
+
+    <!-- SEO LINKS -->
+    
+
+    <!-- SUMMARY -->
+    <section class="report-section" id="summary">
+        <h2 class="section-title">Podsumowanie miesiąca</h2>
+        <div class="summary-box">
+            Odnotowano 199 konwersji w tym miesiącu. Ruch z reklam wzrósł o 2.5% (3826 kliknięć).
+        </div>
+    </section>
+
+    <!-- RECOMMENDATIONS moved to top -->
+
+    <!-- FOOTER -->
+    <div class="footer">
+        Raport wygenerowany przez <a href="https://www.project-pro.pl">Project-Pro</a> &mdash; marketing w wersji PRO
+    </div>
+
+    <script>
+        // Daily clicks chart
+        var ctx1 = document.getElementById('dailyClicksChart').getContext('2d');
+        new Chart(ctx1, {
+            type: 'line',
+            data: {
+                labels: ["04-01", "04-02", "04-03", "04-04", "04-05", "04-06", "04-07", "04-08", "04-09", "04-10", "04-11", "04-12", "04-13", "04-14", "04-15", "04-16", "04-17", "04-18", "04-19", "04-20", "04-21", "04-22", "04-23", "04-24", "04-25", "04-26", "04-27", "04-28", "04-29", "04-30"],
+                datasets: [{
+                    label: 'Kliknięcia',
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+                    fill: true,
+                    tension: 0.3,
+                    pointRadius: 3,
+                    pointBackgroundColor: '#0d8b8b',
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+                    data: [6909, 5632, 4210, 3045, 2088, 3976, 5853, 7519, 6605, 4340, 3177, 4104, 7332, 7941, 7296, 6191, 4557, 3621, 5409, 7762, 7615, 9246, 9234, 5931, 5078, 5786, 6014, 6078, 5629, 4099],
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+                    backgroundColor: 'transparent',
+                    borderDash: [5, 5],
+                    tension: 0.3,
+                    pointRadius: 0,
+                    yAxisID: 'y1',
+                }]
+            },
+            options: {
+                responsive: true,
+                interaction: { mode: 'index', intersect: false },
+                scales: {
+                    y: { beginAtZero: true, position: 'left', grid: { color: '#f0f0f0' } },
+                    y1: { beginAtZero: true, position: 'right', grid: { display: false } },
+                    x: { grid: { display: false }, ticks: { maxTicksLimit: 10 } }
+                }
+            }
+        });
+
+        
+        
+        
+        
+    </script>
+
+</body>
+</html>

scripts/reports/output/aruba.rzeszow.pl_2026-02.json

@@ -0,0 +1,412 @@
+{
+  "client": "aruba.rzeszow.pl",
+  "month": "2026-02",
+  "month_name": "Luty",
+  "year": 2026,
+  "prev_month": "2026-01",
+  "prev_month_name": "Styczeń",
+  "generated_at": "2026-05-14T23:27:24.133206",
+  "google_ads": {
+    "campaigns": [
+      {
+        "id": "19591441631",
+        "name": "[Search] brand",
+        "status": "ENABLED",
+        "type": "SEARCH",
+        "impressions": 1572,
+        "clicks": 495,
+        "cost": 430.53,
+        "conversions": 24.0,
+        "conversion_value": 5500.26,
+        "ctr": 31.49,
+        "cpc": 0.87,
+        "cpa": 17.94,
+        "roas": 12.78
+      },
+      {
+        "id": "20561423980",
+        "name": "[DSA] produkty",
+        "status": "ENABLED",
+        "type": "SEARCH",
+        "impressions": 16608,
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+  "recommendations": [
+    {
+      "icon": "⚠",
+      "title": "Spadek konwersji do obserwacji",
+      "text": "Liczba konwersji spadla o 30.2% miesiac do miesiaca. Rekomendujemy sprawdzenie kampanii o najwiekszym spadku wolumenu."
+    },
+    {
+      "icon": "📈",
+      "title": "ROAS liczony z Google Ads",
+      "text": "ROAS z Google Ads wyniosl 9.50. Ten wskaznik liczymy z wartosci konwersji Google Ads, nie z przychodow sklepu."
+    }
+  ]
+}

scripts/reports/output/aruba.rzeszow.pl_2026-04.json

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+  "recommendations": [
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+      "icon": "⚠",
+      "title": "Spadek konwersji do obserwacji",
+      "text": "Liczba konwersji spadla o 8.2% miesiac do miesiaca. Rekomendujemy sprawdzenie kampanii o najwiekszym spadku wolumenu."
+    },
+    {
+      "icon": "📈",
+      "title": "ROAS liczony z Google Ads",
+      "text": "ROAS z Google Ads wyniosl 8.47. Ten wskaznik liczymy z wartosci konwersji Google Ads, nie z przychodow sklepu."
+    },
+    {
+      "icon": "🔍",
+      "title": "Kontrola wzrostu kosztu",
+      "text": "Koszt reklam wzrosl o 12.2% miesiac do miesiaca. Warto porownac wzrost kosztu ze wzrostem konwersji i wartosci konwersji."
+    }
+  ]
+}

scripts/reports/output/ibra-makeup.pl_2026-04_recommendations.json

@@ -0,0 +1,97 @@
+{
+  "source": "agent_ai",
+  "instruction": "Uzupelnia agent AI po analizie danych raportu. Skrypt nie powinien sam generowac wnioskow ani rekomendacji.",
+  "context": {
+    "google_ads_totals": {
+      "cost": 6705.35,
+      "clicks": 4339,
+      "conversions": 641.8,
+      "conversion_value": 72679.4,
+      "roas": 10.84,
+      "cpa": 10.45
+    },
+    "google_ads_mom_change": {
+      "cost_pct": 8.2,
+      "clicks_pct": 1.7,
+      "conversions_pct": 7.9,
+      "conversion_value_pct": 0,
+      "roas_pct": 0,
+      "cpa_pct": 0.4
+    },
+    "ga4_ecommerce": {
+      "transactions": 1711,
+      "revenue": 187795.28,
+      "transactions_pct": 0,
+      "revenue_pct": 0
+    },
+    "top_campaigns_by_cost": [
+      {
+        "name": "[Search] brand",
+        "cost": 2712.17,
+        "conversions": 298.2,
+        "conversion_value": 34878.62,
+        "roas": 12.86
+      },
+      {
+        "name": "[PMax] products (catch-all)",
+        "cost": 2263.02,
+        "conversions": 255.0,
+        "conversion_value": 29204.43,
+        "roas": 12.91
+      },
+      {
+        "name": "[PLA] produkty (bestsellers)",
+        "cost": 607.74,
+        "conversions": 70.5,
+        "conversion_value": 7193.01,
+        "roas": 11.84
+      },
+      {
+        "name": "[DG] YouTube Shorts",
+        "cost": 502.01,
+        "conversions": 7.3,
+        "conversion_value": 725.68,
+        "roas": 1.45
+      },
+      {
+        "name": "[GDN] porzucone koszyki",
+        "cost": 304.26,
+        "conversions": 5.8,
+        "conversion_value": 310.57,
+        "roas": 1.02
+      }
+    ]
+  },
+  "recommendations": [
+    {
+      "icon": "✅",
+      "title": "Google Ads utrzymuje bardzo dobrą rentowność",
+      "text": "Konto wygenerowało 641,8 konwersji przy koszcie 6705,35 PLN i ROAS 10,84. Utrzymujemy aktywne kampanie sprzedażowe, a dalsze zwiększanie budżetu prowadzimy sekwencyjnie, przede wszystkim w kampaniach z ROAS powyżej średniej konta."
+    },
+    {
+      "icon": "📈",
+      "title": "Więcej konwersji przy prawie stabilnym koszcie pozyskania",
+      "text": "W porównaniu miesiąc do miesiąca konwersje wzrosły o 7,9%, koszt o 8,2%, a CPA tylko o 0,4%. Skala rosła bez widocznego pogorszenia kosztu pozyskania, dlatego nie tniemy budżetu całościowo. Pracujemy na miksie kampanii i przesuwamy uwagę na te segmenty, które utrzymują rentowność."
+    },
+    {
+      "icon": "🔍",
+      "title": "Brand i PMax niosą główny wynik",
+      "text": "Największą część kosztu i wartości konwersji generują [Search] brand oraz [PMax] products (catch-all). Obie kampanie mają ROAS około 12,9, dlatego zostają główne w strukturze. Zmiany celów ROAS lub budżetów wprowadzamy stopniowo i kontrolujemy wolumen po każdej zmianie."
+    },
+    {
+      "icon": "⚠",
+      "title": "Kampanie z niskim ROAS wymagają osobnej decyzji",
+      "text": "[DG] YouTube Shorts i wybrane kampanie PLA_CL1 mają wyraźnie niższy ROAS niż średnia konta. Nie wyłączamy ich automatycznie tylko na podstawie tego raportu. Rozdzielamy ich role na wsparcie lejka i realną sprzedaż, a przy celu czystej efektywności ograniczamy lub zawężamy je w pierwszej kolejności."
+    },
+    {
+      "icon": "💰",
+      "title": "Sprzedaż sklepu jest mocniejsza w danych z arkusza",
+      "text": "Arkusz sprzedażowy pokazuje 1711 transakcji i 187795,28 PLN przychodu w kwietniu. Średnia wartość koszyka wynosi 109,76 PLN, dlatego w komunikacji i kampaniach wzmacniamy produkty oraz zestawy, które podnoszą wartość zamówienia, zamiast skupiać się wyłącznie na liczbie transakcji."
+    },
+    {
+      "icon": "➤",
+      "title": "Rekomendowany następny krok",
+      "text": "Utrzymujemy główny kierunek konta, a optymalizacje prowadzimy punktowo: kontrolujemy kampanie o niskim ROAS, analizujemy udział brandu w wyniku i porównujemy PMax z PLA pod kątem produktów, które można efektywniej skalować. Zmiany budżetów i celów Smart Bidding wdrażamy pojedynczo, z oceną po kolejnej paczce danych."
+    }
+  ]
+}

scripts/reports/test_ga4_access.py

@@ -0,0 +1,40 @@
+"""Quick test: check if OAuth credentials can access GA4 property."""
+import os
+from dotenv import load_dotenv
+from google.oauth2.credentials import Credentials
+from google.analytics.data_v1beta import BetaAnalyticsDataClient
+from google.analytics.data_v1beta.types import RunReportRequest, DateRange, Metric
+
+load_dotenv()
+
+PROPERTY_ID = os.getenv("GA4_PROPERTY_ID_studio-zoe.pl")
+CLIENT_ID = os.getenv("GOOGLE_ADS_OAUTH2_CLIENT_ID")
+CLIENT_SECRET = os.getenv("GOOGLE_ADS_OAUTH2_CLIENT_SECRET")
+REFRESH_TOKEN = os.getenv("GA4_REFRESH_TOKEN")
+
+print(f"GA4 Property ID: {PROPERTY_ID}")
+
+credentials = Credentials(
+    token=None,
+    refresh_token=REFRESH_TOKEN,
+    client_id=CLIENT_ID,
+    client_secret=CLIENT_SECRET,
+    token_uri="https://oauth2.googleapis.com/token",
+)
+
+client = BetaAnalyticsDataClient(credentials=credentials)
+
+try:
+    request = RunReportRequest(
+        property=f"properties/{PROPERTY_ID}",
+        date_ranges=[DateRange(start_date="2026-02-01", end_date="2026-02-28")],
+        metrics=[Metric(name="sessions"), Metric(name="totalUsers")],
+    )
+    response = client.run_report(request)
+
+    for row in response.rows:
+        print(f"Sessions: {row.metric_values[0].value}, Users: {row.metric_values[1].value}")
+
+    print("\nGA4 access works!")
+except Exception as e:
+    print(f"\nGA4 access failed: {e}")

scripts/reports/upload_report_ftp.py

@@ -0,0 +1,121 @@
+#!/usr/bin/env python3
+"""
+Upload raportu HTML na serwer FTP (adspro.projectpro.pl).
+
+Użycie:
+    python scripts/reports/upload_report_ftp.py --local-dir output/studio-zoe.pl/2026-02/ --remote-path /raporty/studio-zoe/2026-02/
+"""
+
+import argparse
+import ftplib
+import os
+import sys
+import io
+from pathlib import Path
+
+sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace")
+
+ROOT = Path(__file__).parent.parent.parent
+sys.path.insert(0, str(ROOT))
+from src.gads_v2.config import load_env
+
+load_env(ROOT / ".env")
+
+
+def ftp_mkdirs(ftp, path):
+    """Create nested directories on FTP server."""
+    dirs = path.strip("/").split("/")
+    current = ""
+    for d in dirs:
+        current += f"/{d}"
+        try:
+            ftp.cwd(current)
+        except ftplib.error_perm:
+            try:
+                ftp.mkd(current)
+                print(f"  Utworzono katalog: {current}")
+            except ftplib.error_perm:
+                pass  # already exists or no permission
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Upload raportu na FTP")
+    parser.add_argument("--local-dir", required=True, help="Lokalny folder z raportem")
+    parser.add_argument("--remote-path", required=True, help="Sciezka na serwerze (np. /raporty/studio-zoe/2026-02/)")
+    args = parser.parse_args()
+
+    host = os.environ["ADSPRO_HOST"]
+    user = os.environ["ADSPRO_USERNAME"]
+    password = os.environ["ADSPRO_PASSWORD"]
+    base_path = os.environ["ADSPRO_REMOTE_PATH"]
+
+    local_dir = Path(args.local_dir)
+    if not local_dir.is_absolute():
+        local_dir = ROOT / "scripts" / "reports" / local_dir
+
+    if not local_dir.exists():
+        print(f"Blad: folder {local_dir} nie istnieje")
+        sys.exit(1)
+
+    # Fix Git Bash path mangling on Windows (e.g. /raporty -> C:/Program Files/Git/raporty)
+    remote_path = args.remote_path
+    if "Program Files/Git" in remote_path or ":" in remote_path:
+        # Extract the intended path after the Git prefix
+        import re
+        match = re.search(r'/raporty/.+', remote_path)
+        if match:
+            remote_path = match.group(0)
+        else:
+            remote_path = "/" + remote_path.split("/")[-3] + "/" + remote_path.split("/")[-2] + "/" + remote_path.split("/")[-1]
+
+    remote_full = base_path.rstrip("/") + "/" + remote_path.strip("/")
+
+    print(f"Laczenie z {host}...")
+
+    # Try FTP_TLS first, fallback to plain FTP
+    try:
+        ftp = ftplib.FTP_TLS(host, timeout=30)
+        ftp.login(user, password)
+        ftp.prot_p()
+        print("  Polaczono (FTP TLS)")
+    except Exception:
+        ftp = ftplib.FTP(host, timeout=30)
+        ftp.login(user, password)
+        print("  Polaczono (FTP)")
+
+    ftp.encoding = "utf-8"
+
+    # Create remote directory structure
+    print(f"Tworzenie katalogow: {remote_full}")
+    ftp_mkdirs(ftp, remote_full)
+    ftp.cwd(remote_full)
+
+    # Upload all files
+    files_uploaded = 0
+    for file_path in local_dir.rglob("*"):
+        if file_path.is_file():
+            relative = file_path.relative_to(local_dir)
+            remote_file = str(relative).replace("\\", "/")
+
+            # Create subdirectories if needed
+            if "/" in remote_file:
+                subdir = "/".join(remote_file.split("/")[:-1])
+                ftp_mkdirs(ftp, remote_full + "/" + subdir)
+                ftp.cwd(remote_full)
+
+            print(f"  Uploading: {remote_file} ({file_path.stat().st_size:,} bytes)")
+            with open(file_path, "rb") as f:
+                ftp.storbinary(f"STOR {remote_file}", f)
+            files_uploaded += 1
+
+    ftp.quit()
+
+    domain_part = args.remote_path.strip("/")
+    url = f"https://adspro.projectpro.pl/{domain_part}/"
+
+    print(f"\nUpload zakonczony! {files_uploaded} plikow.")
+    print(f"URL: {url}")
+
+
+if __name__ == "__main__":
+    main()