modules/EcmInvoiceOuts/ai/analysisAI.py

@@ -0,0 +1,74 @@
+#!/usr/bin/env python3
+import os, sys, json
+from preaggregates import compute_preaggregates, serialize_for_ai
+
+try:
+    import mysql.connector
+except Exception as e:
+    sys.stderr.write("MySQL connector not available: %s\n" % e)
+    sys.exit(1)
+
+def getenv(key, default=None):
+    return os.environ.get(key, default)
+
+def main():
+    cfg = {
+        #"host": getenv("MYSQL_HOST", "twinpol-mysql56"),
+        "host": getenv("MYSQL_HOST", "localhost"),
+        "user": getenv("MYSQL_USER", "root"),
+        "password": getenv("MYSQL_PASSWORD", "rootpassword"),
+        "database": getenv("MYSQL_DATABASE", "preDb_0dcc87940d3655fa574b253df04ca1c3"),
+        "port": int(getenv("MYSQL_PORT", "3306")),
+    }
+
+    try:
+        cnx = mysql.connector.connect(**cfg)
+        cur = cnx.cursor()
+        #cur.execute("SELECT COUNT(*) FROM ecminvoiceouts WHERE YEAR(register_date)=2025")
+        cur.execute("""
+            SELECT i.document_no,
+                   i.parent_name,
+                   DATE(i.register_date) AS register_date,
+                   ii.code,
+                   ii.name,
+                   ii.quantity,
+                   ii.total_netto
+            FROM ecminvoiceoutitems AS ii
+            JOIN ecminvoiceouts      AS i ON i.id = ii.ecminvoiceout_id
+            WHERE i.register_date >= %s
+              AND i.register_date <  %s
+              AND i.type = %s
+        """, ("2025-07-01", "2025-08-01", "normal"))
+        rows = cur.fetchall()
+
+        results = compute_preaggregates(rows)
+
+        # 2) podejrzyj wyniki
+        #  ['daily_sales', 'product_summary', 'customer_summary', 'product_daily',
+        #   'top10_products_by_sales', 'top10_customers_by_sales']
+        print(">> available tables:", list(results.keys()))
+#         print(results["daily_sales"].head(10))
+#         print(results["product_summary"])
+#         print(results["customer_summary"])
+#         print(results["product_daily"])
+#         print(results["top10_products_by_sales"])
+#         print(results["top10_customers_by_sales"])
+        results["daily_sales"].head(10)
+        results["product_summary"]
+        results["customer_summary"]
+        results["product_daily"]
+        results["top10_products_by_sales"]
+        results["top10_customers_by_sales"]
+
+        # 3) zserializuj do lekkiego JSON-a (np. do AI lub do pliku)
+        ai_payload = serialize_for_ai(results)
+        print(json.dumps(ai_payload, ensure_ascii=False, indent=2, default=str))
+
+        cur.close()
+        cnx.close()
+    except Exception as e:
+        sys.stderr.write("Query error: %s\n" % e)
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()

modules/EcmInvoiceOuts/ai/enqueue.php

@@ -1,21 +0,0 @@
-<?php
-// modules/EcmInvoiceOuts/ai/enqueue.php
-$from = $_POST['from'] ?? null;
-$to = $_POST['to'] ?? null;
-$currency = $_POST['currency'] ?? 'PLN';
-$axis = $_POST['axis'] ?? 'sku_id';
-$label = $_POST['label'] ?? 'sku_name';
-$top_n = (int)($_POST['top_n'] ?? 50);
-$goal = $_POST['goal'] ?? 'porównanie Q2 vs Q1';
-
-if (!$from || !$to) { http_response_code(400); exit('Missing from/to'); }
-
-$base = __DIR__;
-@mkdir("$base/queue", 0777, true);
-
-$payload = compact('from','to','currency','axis','label','top_n','goal');
-$id = bin2hex(random_bytes(8));
-file_put_contents("$base/queue/$id.json", json_encode($payload, JSON_UNESCAPED_UNICODE));
-
-header('Content-Type: application/json; charset=utf-8');
-echo json_encode(['job_id' => $id]);

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

modules/EcmInvoiceOuts/ai/result.php

@@ -1,12 +0,0 @@
-<?php
-// modules/EcmInvoiceOuts/ai/result.php
-$base = __DIR__;
-$files = glob("$base/out/*.json");
-rsort($files);
-$latest = $files[0] ?? null;
-
-if (!$latest) { http_response_code(404); exit('Brak wyników'); }
-
-$payload = json_decode(file_get_contents($latest), true);
-header('Content-Type: application/json; charset=utf-8');
-echo json_encode($payload, JSON_UNESCAPED_UNICODE | JSON_PRETTY_PRINT);

modules/EcmInvoiceOuts/ai/test.py

@@ -1,37 +0,0 @@
-#!/usr/bin/env python3
-import os
-import sys
-
-try:
-    import mysql.connector
-except Exception as e:
-    sys.stderr.write("MySQL connector not available: %s\n" % e)
-    sys.exit(1)
-
-def getenv(key, default=None):
-    return os.environ.get(key, default)
-
-def main():
-    cfg = {
-        "host": getenv("MYSQL_HOST", "twinpol-mysql56"),
-        "user": getenv("MYSQL_USER", "root"),
-        "password": getenv("MYSQL_PASSWORD", "rootpassword"),
-        "database": getenv("MYSQL_DATABASE", "preDb_0dcc87940d3655fa574b253df04ca1c3"),
-        "port": int(getenv("MYSQL_PORT", "3306")),
-    }
-
-    try:
-        cnx = mysql.connector.connect(**cfg)
-        cur = cnx.cursor()
-        cur.execute("SELECT COUNT(*) FROM ecminvoiceouts WHERE YEAR(register_date)=2025")
-        row = cur.fetchone()
-        count = int(row[0]) if row and row[0] is not None else 0
-        print(count)
-        cur.close()
-        cnx.close()
-    except Exception as e:
-        sys.stderr.write("Query error: %s\n" % e)
-        sys.exit(1)
-
-if __name__ == "__main__":
-    main()

modules/EcmInvoiceOuts/ai/worker.py

@@ -1,141 +0,0 @@
-# worker.py
-import os, json, io, uuid
-import datetime as dt
-from typing import Dict, Any, List
-
-import polars as pl
-import pymysql
-from tenacity import retry, wait_exponential, stop_after_attempt
-from dotenv import load_dotenv
-
-load_dotenv()
-
-AI_MODEL = os.getenv("AI_MODEL", "gpt-5-pro")
-AI_API_KEY = os.getenv("AI_API_KEY")
-
-MYSQL_CONF = dict(
-    host=os.getenv("MYSQL_HOST", "localhost"),
-    user=os.getenv("MYSQL_USER", "root"),
-    password=os.getenv("MYSQL_PASSWORD", ""),
-    database=os.getenv("MYSQL_DB", "sales"),
-    cursorclass=pymysql.cursors.DictCursor,
-)
-
-def mysql_query(sql: str, params: tuple = ()) -> 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()

modules/EcmInvoiceOuts/test.php

@@ -1,16 +1,44 @@
 <?php
-// Runs the Python script, waits for completion, and returns its output.
+$bins = [
-
+    '/var/www/venv/bin/python',
-$cmd = 'python3 /var/www/html/modules/EcmInvoiceOuts/ai/test.py';
+    '/usr/bin/python3.11',
-$output = [];
+    '/usr/bin/python3.10',
-$returnVar = 0;
+    '/usr/bin/python3.9',
-exec($cmd . ' 2>&1', $output, $returnVar);
+    '/usr/local/bin/python3.11',
-
+    '/usr/local/bin/python3.10',
-if ($returnVar !== 0) {
+    '/usr/local/bin/python3.9',
-    http_response_code(500);
+    '/usr/bin/python3',
-    echo "Error running Python script:\n" . implode("\n", $output);
+    'python3',
-    exit;
+];
+foreach ($bins as $b) {
+    $out = []; $ret = 0;
+    exec("$b -V 2>&1", $out, $ret);
+    echo htmlspecialchars("$b -> ".($out ? implode(' ', $out) : "not found / not executable")." (ret=$ret)")."<br>";
 }
-
+//$python = '/usr/bin/python3';
-// Expect a single line with the count
+//$script = '/var/www/html/modules/EcmInvoiceOuts/ai/analysisAI.py';
-echo trim(implode("\n", $output));
+//$cmd = escapeshellcmd("$python $script");
+//
+//// odczyt
+//$output = [];
+//$returnVar = 0;
+//exec($cmd . ' 2>&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".$body;
+//    exit;
+//}
+//
+//// --- 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