+ Raport sprzedaży — {period_label}
+
+ {kpi_html}
+
+ {sections_html if sections_html.strip() else 'Nie wybrano żadnych preagregatów — brak sekcji do wyświetlenia.
'}
+
+
Analiza i rekomendacje{(' (AI · ' + model_alias + ')') if model_alias else ''}
+ {ai_section if ai_section else '
Analiza AI wyłączona lub brak danych.
'}
+
Brak dostępnego limitu API. {}
'.format(str(ee))
+ )
+ else:
+ ai_section = (
+ 'Błąd wywołania AI: {}
'.format(err)
+ )
+ else:
+ ai_section = 'Analiza AI wyłączona lub brak wybranych danych.
'
+ model_alias = ""
+
+ # --- Finalny HTML ---
+ report_html = render_report_html(
+ period_label=shown_label,
+ kpis=kpis,
+ parts=parts,
+ ai_section=ai_section,
+ model_alias=(model_alias if (with_ai and has_any_rows(serialized)) else "")
+ )
+ sys.stdout.write(report_html)
+
+if __name__ == "__main__":
+ main()
diff --git a/modules/EcmInvoiceOuts/ai/enqueue.php b/modules/EcmInvoiceOuts/ai/enqueue.php
deleted file mode 100644
index d8b10d73..00000000
--- a/modules/EcmInvoiceOuts/ai/enqueue.php
+++ /dev/null
@@ -1,21 +0,0 @@
- $id]);
diff --git a/modules/EcmInvoiceOuts/ai/preaggregates.py b/modules/EcmInvoiceOuts/ai/preaggregates.py
new file mode 100644
index 00000000..b000f06b
--- /dev/null
+++ b/modules/EcmInvoiceOuts/ai/preaggregates.py
@@ -0,0 +1,150 @@
+# --- preagg.py ---------------------------------------------------------------
+from __future__ import annotations
+import pandas as pd
+import numpy as np
+from typing import Callable, Dict, List
+
+# Rejestr agregatorów: name -> funkcja(df) -> DataFrame
+# AGGREGATORS: Dict[str, Callable[[pd.DataFrame], pd.DataFrame]] = {}
+AGGREGATORS = {}
+def aggregator(name: str):
+ """Dekorator do łatwego rejestrowania nowych agregatorów."""
+ def _wrap(func: Callable[[pd.DataFrame], pd.DataFrame]):
+ AGGREGATORS[name] = func
+ return func
+ return _wrap
+
+def to_df(rows: List[tuple]) -> pd.DataFrame:
+ """Konwersja rows -> DataFrame (dopasuj nazwy kolumn do SELECT-a)."""
+ cols = [
+ "document_no",
+ "customer_name", # i.parent_name
+ "register_date", # DATE(i.register_date)
+ "product_code", # ii.code
+ "product_name", # ii.name
+ "quantity", # ii.quantity
+ "total_netto", # ii.total_netto (wartość sprzedaży netto)
+ ]
+ df = pd.DataFrame(rows, columns=cols)
+ if df.empty:
+ return df
+ # Typy
+ df["register_date"] = pd.to_datetime(df["register_date"])
+ df["quantity"] = pd.to_numeric(df["quantity"], errors="coerce").fillna(0.0)
+ df["total_netto"] = pd.to_numeric(df["total_netto"], errors="coerce").fillna(0.0)
+ # ASP (Average Selling Price) – średnia cena pozycji
+ # Uwaga: ASP lepiej liczyć ważoną średnią w agregatach; tu to „unit price” na pozycji.
+ df["unit_price"] = np.where(df["quantity"] != 0, df["total_netto"] / df["quantity"], np.nan)
+ return df
+
+# ------------------- Wbudowane agregatory (możesz dopisywać kolejne) -------------------
+
+@aggregator("daily_sales")
+def daily_sales(df: pd.DataFrame) -> pd.DataFrame:
+ """Dzienna sprzedaż: ilość, wartość, liczba dokumentów, ASP ważony."""
+ if df.empty:
+ return df
+ g = df.groupby(pd.Grouper(key="register_date", freq="D"))
+ out = g.agg(
+ qty=("quantity", "sum"),
+ sales=("total_netto", "sum"),
+ docs=("document_no", "nunique"),
+ ).reset_index()
+ # ASP ważony (sales / qty)
+ out["asp"] = np.where(out["qty"] != 0, out["sales"] / out["qty"], np.nan)
+ # Zmiana d/d
+ out["sales_pct_change_dod"] = out["sales"].pct_change()
+ # Rolling 7
+ out["sales_rolling7"] = out["sales"].rolling(7, min_periods=1).mean()
+ return out
+
+@aggregator("product_summary")
+def product_summary(df: pd.DataFrame) -> pd.DataFrame:
+ """Podsumowanie po produkcie."""
+ if df.empty:
+ return df
+ g = df.groupby(["product_code", "product_name"], as_index=False).agg(
+ qty=("quantity", "sum"),
+ sales=("total_netto", "sum"),
+ docs=("document_no", "nunique"),
+ )
+ g["asp_weighted"] = np.where(g["qty"] != 0, g["sales"] / g["qty"], np.nan)
+ # Udział w koszyku (mix % po wartości)
+ total_sales = g["sales"].sum()
+ g["mix_share_sales"] = np.where(total_sales > 0, g["sales"] / total_sales, 0.0)
+ return g.sort_values("sales", ascending=False)
+
+@aggregator("customer_summary")
+def customer_summary(df: pd.DataFrame) -> pd.DataFrame:
+ """Podsumowanie po kliencie."""
+ if df.empty:
+ return df
+ g = df.groupby(["customer_name"], as_index=False).agg(
+ qty=("quantity", "sum"),
+ sales=("total_netto", "sum"),
+ docs=("document_no", "nunique"),
+ distinct_products=("product_code", "nunique"),
+ )
+ g["asp_weighted"] = np.where(g["qty"] != 0, g["sales"] / g["qty"], np.nan)
+ return g.sort_values("sales", ascending=False)
+
+@aggregator("product_daily")
+def product_daily(df: pd.DataFrame) -> pd.DataFrame:
+ """Dzienna sprzedaż per produkt (przydatne do trendów/rollingów w AI)."""
+ if df.empty:
+ return df
+ g = (df
+ .groupby([pd.Grouper(key="register_date", freq="D"), "product_code", "product_name"], as_index=False)
+ .agg(qty=("quantity", "sum"),
+ sales=("total_netto", "sum")))
+ # Rolling 7 per produkt
+ g = g.sort_values(["product_code", "register_date"])
+ g["sales_rolling7"] = g.groupby("product_code")["sales"].transform(lambda s: s.rolling(7, min_periods=1).mean())
+ g["sales_pct_change_dod"] = g.groupby("product_code")["sales"].pct_change()
+ return g
+
+@aggregator("top10_products_by_sales")
+def top10_products_by_sales(df: pd.DataFrame) -> pd.DataFrame:
+ """Top 10 produktów po wartości sprzedaży (okres z wejścia)."""
+ base = AGGREGATORS["product_summary"](df)
+ return base.nlargest(10, "sales")
+
+@aggregator("top10_customers_by_sales")
+def top10_customers_by_sales(df: pd.DataFrame) -> pd.DataFrame:
+ """Top 10 klientów po wartości sprzedaży."""
+ base = AGGREGATORS["customer_summary"](df)
+ return base.nlargest(10, "sales")
+
+# ------------------- Runner -------------------
+
+def compute_preaggregates(rows: List[tuple]) -> dict[str, pd.DataFrame]:
+#def compute_preaggregates(rows):
+ """Główny punkt wejścia: rows -> df -> uruchom wszystkie agregatory."""
+ df = to_df(rows)
+# results: dict[str, pd.DataFrame] = {}
+ results = {}
+ for name, fn in AGGREGATORS.items():
+ try:
+ results[name] = fn(df).copy()
+ except Exception as e:
+ # Niech agregat nie wysadza całości – zapisz pusty DF + info
+ results[name] = pd.DataFrame({"_error": [str(e)], "_aggregator": [name]})
+ return results
+
+def serialize_for_ai(results: dict[str, pd.DataFrame]) -> dict[str, list[dict]]:
+ """
+ Konwersja wyników do lekkiego JSON-a (listy rekordów),
+ który łatwo przekazać do modelu AI lub zapisać do pliku.
+ """
+# out: dict[str, list[dict]] = {}
+ out = {}
+ for name, df in results.items():
+ if df is None or df.empty:
+ out[name] = []
+ else:
+ # zaokrąglij liczby dla czytelności (opcjonalnie)
+ df2 = df.copy()
+ for c in df2.select_dtypes(include=[np.number]).columns:
+ df2[c] = df2[c].round(6)
+ out[name] = df2.to_dict(orient="records")
+ return out
diff --git a/modules/EcmInvoiceOuts/ai/result.php b/modules/EcmInvoiceOuts/ai/result.php
deleted file mode 100644
index 81a59317..00000000
--- a/modules/EcmInvoiceOuts/ai/result.php
+++ /dev/null
@@ -1,12 +0,0 @@
- pl.DataFrame:
- conn = pymysql.connect(**MYSQL_CONF)
- try:
- with conn.cursor() as cur:
- cur.execute(sql, params)
- rows = cur.fetchall()
- finally:
- conn.close()
- return pl.from_dicts(rows)
-
-def to_csv(df: pl.DataFrame) -> str:
- buf = io.StringIO()
- df.write_csv(buf)
- return buf.getvalue()
-
-SQL_KPIS_DAILY = """
-SELECT DATE(invoice_date) AS d,
- SUM(net_amount) AS revenue,
- SUM(quantity) AS qty,
- ROUND(100*SUM(net_amount - cost_amount)/NULLIF(SUM(net_amount),0), 2) AS gross_margin_pct,
- ROUND(100*SUM(discount_amount)/NULLIF(SUM(gross_amount),0), 2) AS discount_pct
-FROM fact_invoices
-WHERE invoice_date BETWEEN %s AND %s
-GROUP BY 1
-ORDER BY 1;
-"""
-
-SQL_TOP_SEGMENTS = """
-SELECT {axis} AS key,
- ANY_VALUE({label}) AS label,
- SUM(net_amount) AS revenue,
- SUM(quantity) AS qty,
- ROUND(100*SUM(net_amount - cost_amount)/NULLIF(SUM(net_amount),0), 2) AS gross_margin_pct,
- ROUND(100*(SUM(net_amount) - LAG(SUM(net_amount)) OVER(ORDER BY 1))/
- NULLIF(LAG(SUM(net_amount)) OVER(ORDER BY 1),0), 2) AS trend_30d
-FROM fact_invoices
-WHERE invoice_date BETWEEN DATE_SUB(%s, INTERVAL 60 DAY) AND %s
-GROUP BY 1
-ORDER BY revenue DESC
-LIMIT %s;
-"""
-
-class AIClient:
- def __init__(self, api_key: str): self.api_key = api_key
- @retry(wait=wait_exponential(multiplier=1, min=1, max=20), stop=stop_after_attempt(6))
- def structured_analysis(self, prompt: str, schema: Dict[str, Any]) -> Dict[str, Any]:
- # TODO: PODMIEŃ na realne wywołanie modelu z "Structured Outputs"
- raise NotImplementedError("Wire your model SDK here")
-
- @retry(wait=wait_exponential(multiplier=1, min=1, max=20), stop=stop_after_attempt(6))
- def batch_submit(self, ndjson_lines: List[str]) -> str:
- # TODO: PODMIEŃ na rzeczywiste Batch API
- raise NotImplementedError
-
-def run_online(from_date: str, to_date: str, currency: str, axis: str, label: str, top_n: int, goal: str) -> Dict[str, Any]:
- kpis = mysql_query(SQL_KPIS_DAILY, (from_date, to_date))
- top = mysql_query(SQL_TOP_SEGMENTS.format(axis=axis, label=label), (from_date, to_date, top_n))
-
- csv_blocks = ("## kpis_daily\n" + to_csv(kpis) + "\n\n" +
- "## top_segments\n" + to_csv(top))
-
- with open(os.path.join(os.path.dirname(__file__), "sales-analysis.schema.json"), "r", encoding="utf-8") as f:
- schema = json.load(f)
-
- prompt = f"""
-Jesteś analitykiem sprzedaży. Otrzymasz: (a) kontekst, (b) dane.
-Zwróć **wyłącznie** JSON zgodny ze schema.
-
-Kontekst:
-- Waluta: {currency}
-- Zakres: {from_date} → {to_date}
-- Cel: {goal}
-- Poziom segmentacji: {axis}
-
-Dane (CSV):
-{csv_blocks}
-
-Wskazówki:
-- Użyj danych jak są (nie wymyślaj liczb).
-- W meta.scope wpisz opis zakresu i segmentacji.
-- Jeśli brak anomalii – anomalies: [].
-- Kwoty do 2 miejsc, procenty do 1.
-"""
-
- ai = AIClient(AI_API_KEY)
- result = ai.structured_analysis(prompt, schema)
-
- out_dir = os.path.join(os.path.dirname(__file__), "out")
- os.makedirs(out_dir, exist_ok=True)
- out_path = os.path.join(out_dir, f"{uuid.uuid4()}.json")
- with open(out_path, "w", encoding="utf-8") as f:
- json.dump(result, f, ensure_ascii=False)
- return {"status": "ok", "path": out_path}
-
-def run_batch(from_date: str, to_date: str, axis: str, label: str):
- # Zgodnie z blueprintem – generujemy linie NDJSON (skrót; pełny wariant w PDF)
- # TODO: dodać realne wywołania batch_submit i zapisać ID/stan
- raise NotImplementedError("Implement batch per blueprint")
-
-if __name__ == "__main__":
- import argparse
- p = argparse.ArgumentParser()
- sub = p.add_subparsers(dest="cmd")
- o = sub.add_parser("online")
- o.add_argument("from_date"); o.add_argument("to_date"); o.add_argument("currency")
- o.add_argument("axis", choices=["sku_id","client_id","region_code"])
- o.add_argument("label"); o.add_argument("top_n", type=int, nargs="?", default=50)
- o.add_argument("goal")
- b = sub.add_parser("batch")
- b.add_argument("from_date"); b.add_argument("to_date"); b.add_argument("axis"); b.add_argument("label")
- args = p.parse_args()
-
- if args.cmd == "online":
- print(run_online(args.from_date, args.to_date, args.currency, args.axis, args.label, args.top_n, args.goal))
- elif args.cmd == "batch":
- print(run_batch(args.from_date, args.to_date, args.axis, args.label))
- else:
- p.print_help()
diff --git a/modules/EcmInvoiceOuts/report_form.php b/modules/EcmInvoiceOuts/report_form.php
new file mode 100644
index 00000000..7697d5b4
--- /dev/null
+++ b/modules/EcmInvoiceOuts/report_form.php
@@ -0,0 +1,177 @@
+&1', $output, $returnVar);
+
+ $ran = true;
+ $rc = $returnVar;
+ $out = implode("\n", $output);
+ $ok = ($returnVar === 0);
+
+ if (!$ok && $err === '') {
+ $err = "Błąd uruchamiania skryptu Python (kod: " . $rc . "):\n" . $out;
+ }
+ }
+}
+?>
+
+
+
+
+ Generator raportu sprzedaży
+
+
+
+
+
+Raport sprzedaży — parametry
+
+
+
+
+
+ Użyte parametry
+
+ Od:
+ Do:
+ AI:
+
+ Preagregaty:
+ '.h($p).'';
+ }
+ } else {
+ echo 'brak';
+ }
+ ?>
+
+
+ Wynik analizy
+
+
+
+
+
+
+
+
+
+
diff --git a/modules/EcmInvoiceOuts/test.php b/modules/EcmInvoiceOuts/test.php
index fc8dc317..d3ba879e 100644
--- a/modules/EcmInvoiceOuts/test.php
+++ b/modules/EcmInvoiceOuts/test.php
@@ -1,16 +1,28 @@
&1', $output, $returnVar);
+$body = implode("\n", $output);
+// błąd Pythona
if ($returnVar !== 0) {
+ // pokaż błąd jako tekst
+ while (ob_get_level()) { ob_end_clean(); }
+ header_remove();
+ header('Content-Type: text/plain; charset=utf-8');
http_response_code(500);
- echo "Error running Python script:\n" . implode("\n", $output);
+ echo "Error running Python script:\n".$body;
exit;
}
-// Expect a single line with the count
-echo trim(implode("\n", $output));
+// --- WYMUSZENIE RENDEROWANIA HTML ---
+while (ob_get_level()) { ob_end_clean(); } // wyczyść wszystkie bufory
+header_remove(); // usuń nagłówki ustawione wcześniej przez framework
+header('Content-Type: text/html; charset=utf-8');
+echo $body;
+exit; // ZATRZYMAJ framework (np. SugarCRM), żeby nic już nie dopisywał