generowanie raportów z wyborem preagregatów

REST/functions.php

@@ -746,5 +746,6 @@ function exportToCSVFile($res, $fullpath, array $headers = null, $delimiter = ';
     }
 
     fclose($fp);
-    return ['ok'=>true, 'path'=>$fullpath, 'rows'=>$count, 'error'=>null];
+    $chmod_ok = @chmod($fullpath, 0664);
+    return ['ok'=>true, 'path'=>$fullpath, 'rows'=>$count, 'chmod'=>$chmod_ok, 'error'=>null];
 }

modules/EcmInvoiceOuts/ai/analysisAI.py

@@ -0,0 +1,713 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+analysisAI.py — pobiera dane z MySQL, liczy wyłącznie WSKAZANE preagregaty,
+renderuje HTML i (opcjonalnie) dodaje analizę AI — tylko jeśli ją zaznaczysz.
+
+Parametry CLI (z formularza PHP):
+  --date-from YYYY-MM-DD
+  --date-to   YYYY-MM-DD     (zamieniane wewnętrznie na +1 dzień, bo SQL ma warunek '< date_to')
+  --metric    NAZWA          (można podać wiele razy: --metric a --metric b ...)
+  --metrics   CSV            (opcjonalnie alternatywnie: --metrics a,b,c)
+  --ai        true|false     (czy uruchomić analizę AI — tylko gdy są preagregaty z danymi)
+
+Preagregaty:
+  - kpis (aliasy: basic, basic_totals) — podstawowe KPI: sprzedaż, ilość, dokumenty, ASP
+  - daily_sales, product_summary, customer_summary, product_daily,
+    top10_products_by_sales, top10_customers_by_sales  (z preaggregates.py)
+"""
+
+import os, sys, json, math, time, warnings, argparse, traceback, html
+from datetime import date, timedelta, datetime
+
+# (1) Wycisza ostrzeżenia urllib3 (LibreSSL / stary OpenSSL)
+try:
+    from urllib3.exceptions import NotOpenSSLWarning
+    warnings.filterwarnings("ignore", category=NotOpenSSLWarning)
+except Exception:
+    pass
+
+# (2) Importy zewnętrzne
+import requests
+import mysql.connector
+import pandas as pd
+
+LOOKER_URL = "https://lookerstudio.google.com/u/0/reporting/107d4ccc-e7eb-4c38-8dce-00700b44f60e/page/ba1YF"
+
+# ========== KONFIGURACJA KLUCZA AI ==========
+API_KEY = "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"
+API_KEY_HARDCODE = API_KEY
+
+# === Import preagregatów ===
+from preaggregates import serialize_for_ai
+import preaggregates as pre  # pre.AGGREGATORS, pre.to_df
+
+# ========== UTILKI ==========
+
+def html_fatal(msg, title="Błąd"):
+    sys.stdout.write(
+        '<div style="font-family:system-ui,-apple-system,Segoe UI,Roboto,Arial,sans-serif;'
+        'max-width:900px;margin:24px auto;padding:16px 20px;border:1px solid #fecaca;'
+        'border-radius:12px;background:#fff5f5;color:#991b1b;">'
+        f'<h3 style="margin:0 0 8px;font-size:18px;">{html.escape(title)}</h3>'
+        f'<pre style="white-space:pre-wrap;margin:0;">{html.escape(msg)}</pre>'
+        '</div>'
+    )
+    sys.exit(1)
+
+def connect_html_or_die(cfg, label="MySQL"):
+    try:
+        return mysql.connector.connect(**cfg)
+    except mysql.connector.Error as e:
+        host = cfg.get("host"); port = cfg.get("port"); user = cfg.get("user")
+        base = (f"[{label}] Błąd połączenia ({host}:{port} jako '{user}').\n"
+                f"errno={getattr(e,'errno',None)} sqlstate={getattr(e,'sqlstate',None)}\n"
+                f"msg={getattr(e,'msg',str(e))}")
+        if os.environ.get("DEBUG"):
+            base += "\n\n" + traceback.format_exc()
+        html_fatal(base, title="Błąd połączenia MySQL")
+
+def getenv(k, d=None):
+    return os.environ.get(k, d)
+
+def last_full_month_bounds():
+    today_first = date.today().replace(day=1)
+    to_dt = today_first
+    prev_last = today_first - timedelta(days=1)
+    from_dt = prev_last.replace(day=1)
+    return from_dt.isoformat(), to_dt.isoformat()
+
+def add_one_day(iso_date):
+    try:
+        return (datetime.strptime(iso_date, "%Y-%m-%d") + timedelta(days=1)).strftime("%Y-%m-%d")
+    except Exception:
+        return iso_date  # w razie czego oddaj wejście
+
+def safe_num(v, ndigits=None):
+    try:
+        f = float(v)
+        if not math.isfinite(f):
+            return None
+        return round(f, ndigits) if ndigits is not None else f
+    except Exception:
+        return None
+
+def safe_date(v):
+    if v is None:
+        return None
+    try:
+        if hasattr(v, "date"):
+            return str(v.date())
+        s = str(v)
+        if len(s) >= 10 and s[4] == '-' and s[7] == '-':
+            return s[:10]
+        return s
+    except Exception:
+        return None
+
+def fmt_money(v):
+    try:
+        return "{:,.2f}".format(float(v)).replace(",", " ").replace(".", ",")
+    except Exception:
+        return str(v)
+
+def compact_table(table, limit=30):
+    out = []
+    if not table:
+        return out
+    lim = int(limit)
+    for i, row in enumerate(table):
+        if i >= lim: break
+        new = {}
+        for k, v in row.items():
+            if isinstance(v, float):
+                new[k] = round(v, 6) if math.isfinite(v) else None
+            else:
+                new[k] = v
+        out.append(new)
+    return out
+
+def call_openai_chat(api_key, model, system_prompt, user_payload_json,
+                     temperature=0.3, connect_timeout=10, read_timeout=90, max_retries=3):
+    url = "https://api.openai.com/v1/chat/completions"
+    headers = {"Authorization": "Bearer " + api_key, "Content-Type": "application/json"}
+    body = {
+        "model": model,
+        "messages": [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": "Dane (JSON):\n\n" + user_payload_json},
+        ],
+        "temperature": temperature,
+    }
+    last_err = None
+    for attempt in range(1, int(max_retries) + 1):
+        try:
+            r = requests.post(url, headers=headers, json=body, timeout=(connect_timeout, read_timeout))
+            if 200 <= r.status_code < 300:
+                data = r.json()
+                return data.get("choices", [{}])[0].get("message", {}).get("content", "")
+            last_err = RuntimeError("OpenAI HTTP {}: {}".format(r.status_code, r.text))
+        except requests.exceptions.RequestException as e:
+            last_err = e
+        time.sleep(min(2 ** attempt, 10))
+    raise RuntimeError("OpenAI request failed: {}".format(last_err))
+
+def html_table(records, title=None, max_rows=20):
+    if not records:
+        return '<div class="empty">Brak danych</div>'
+    cols = list(records[0].keys())
+    body_rows = records[:max_rows]
+    thead = "".join("<th>{}</th>".format(c) for c in cols)
+    trs = []
+    for r in body_rows:
+        tds = []
+        for c in cols:
+            val = r.get(c, "")
+            if isinstance(val, (int, float)):
+                if any(x in c.lower() for x in ("sales", "total", "netto", "value", "asp", "qty", "quantity", "share", "change")):
+                    tds.append('<td class="num">{}</td>'.format(fmt_money(val) if "sales" in c.lower() or "total" in c.lower() or "netto" in c.lower() else val))
+                else:
+                    tds.append('<td class="num">{}</td>'.format(val))
+            else:
+                tds.append('<td>{}</td>'.format(val))
+        trs.append("<tr>{}</tr>".format("".join(tds)))
+    cap = '<div class="tbl-title">{}</div>'.format(title) if title else ""
+    return (
+        cap +
+        '<div class="tbl-wrap"><table class="tbl">'
+        '<thead><tr>{}</tr></thead><tbody>{}</tbody></table></div>'.format(thead, "".join(trs))
+    )
+
+def render_report_html(period_label, kpis, parts, ai_section, model_alias):
+    css = (
+        "font-family:system-ui,-apple-system,Segoe UI,Roboto,Arial,sans-serif;"
+        "max-width:1200px;margin:24px auto;padding:16px 20px;border:1px solid #e5e7eb;"
+        "border-radius:12px;background:#fff;color:#111827"
+    )
+    kpi_item = (
+        '<div class="kpi"><div class="kpi-label">{label}</div>'
+        '<div class="kpi-value">{value}</div></div>'
+    )
+    kpi_html = "".join(kpi_item.format(label=lbl, value=val) for (lbl, val) in kpis)
+    sections_html = "".join(parts)
+    if ai_section and not ai_section.lstrip().startswith("<div"):
+        ai_section = '<div class="ai-section">{}</div>'.format(ai_section)
+
+    return f"""
+<div style="{css}">
+  <h2 style="margin:0 0 12px;font-size:22px;">Raport sprzedaży — {period_label}</h2>
+  <div style="display:grid;grid-template-columns:repeat(4,minmax(0,1fr));gap:12px;margin:12px 0 20px;">
+    {kpi_html}
+  </div>
+  {sections_html if sections_html.strip() else '<div class="empty">Nie wybrano żadnych preagregatów — brak sekcji do wyświetlenia.</div>'}
+  <div style="margin-top:20px;border-top:1px solid #e5e7eb;padding-top:16px;">
+    <h3 style="margin:0 0 8px;font-size:18px;">Analiza i rekomendacje{(' (AI · ' + model_alias + ')') if model_alias else ''}</h3>
+    {ai_section if ai_section else '<div style="color:#6b7280">Analiza AI wyłączona lub brak danych.</div>'}
+  </div>
+
+  <!-- STOPKA z linkiem do Looker Studio -->
+  <div style="margin-top:20px;border-top:1px dashed #e5e7eb;padding-top:12px;display:flex;justify-content:flex-end;">
+    <a href="{LOOKER_URL}" target="_blank" rel="noopener"
+       style="text-decoration:none;padding:8px 12px;border:1px solid #d1d5db;border-radius:8px;
+              background:#f9fafb;color:#111827;font-weight:600;">
+      → Otwórz pełny raport w Looker Studio
+    </a>
+  </div>
+</div>
+<style>
+  .kpi {{background:#f8fafc;border:1px solid #e5e7eb;border-radius:10px;padding:12px;}}
+  .kpi-label {{font-size:12px;color:#6b7280;margin-bottom:4px;}}
+  .kpi-value {{font-size:18px;font-weight:700;}}
+  .tbl-title {{font-weight:600;margin:16px 0 8px;font-size:15px;}}
+  .tbl-wrap {{overflow-x:auto;border:1px solid #e5e7eb;border-radius:8px;}}
+  .tbl {{border-collapse:collapse;width:100%;font-size:14px;}}
+  .tbl thead th {{text-align:left;background:#f3f4f6;padding:8px;border-bottom:1px solid #e5e7eb;white-space:nowrap;}}
+  .tbl tbody td {{padding:8px;border-bottom:1px solid #f3f4f6;vertical-align:top;}}
+  .tbl td.num {{text-align:right;white-space:nowrap;}}
+  .empty {{color:#6b7280;font-style:italic;margin:8px 0;}}
+  .ai-section {{background:#f8fafc;border:1px solid #e5e7eb;border-radius:10px;padding:12px;}}
+</style>
+"""
+
+
+# ========== UPSerTY DO REPORTING (jak u Ciebie) ==========
+
+def _ensure_rank_and_share(items, key_sales="sales"):
+    if not items: return
+    total_sales = sum((x.get(key_sales) or 0) for x in items)
+    sorted_items = sorted(
+        items,
+        key=lambda x: ((x.get(key_sales) or 0), str(x.get("product_code") or x.get("customer_name") or "")),
+        reverse=True
+    )
+    rank_map, rank = {}, 1
+    for x in sorted_items:
+        key = x.get("product_code") or x.get("customer_name") or ""
+        if key not in rank_map:
+            rank_map[key] = rank
+            rank += 1
+    for x in items:
+        key = x.get("product_code") or x.get("customer_name") or ""
+        if not x.get("rank_in_period"):
+            x["rank_in_period"] = rank_map.get(key, 0)
+        if "mix_share_sales" not in x:
+            x["mix_share_sales"] = ((x.get(key_sales) or 0) / total_sales) if total_sales else 0.0
+
+def upsert_daily_sales(cur, daily):
+    if not daily: return 0
+    sql = """
+    INSERT INTO reporting_daily_sales
+      (period_date, qty, sales, docs, asp, sales_rolling7, sales_dod_pct)
+    VALUES (%s,%s,%s,%s,%s,%s,%s)
+    ON DUPLICATE KEY UPDATE
+      qty=VALUES(qty), sales=VALUES(sales), docs=VALUES(docs),
+      asp=VALUES(asp), sales_rolling7=VALUES(sales_rolling7), sales_dod_pct=VALUES(sales_dod_pct),
+      generated_at=CURRENT_TIMESTAMP
+    """
+    rows = []
+    for r in daily:
+        period_date = safe_date(r.get("register_date") or r.get("period_date") or r.get("date"))
+        rows.append((
+            period_date,
+            safe_num(r.get("qty")),
+            safe_num(r.get("sales")),
+            safe_num(r.get("docs")),
+            safe_num(r.get("asp"), 6),
+            safe_num(r.get("sales_rolling7"), 6),
+            safe_num(r.get("sales_pct_change_dod") or r.get("sales_dod_pct"), 6),
+        ))
+    cur.executemany(sql, rows)
+    return len(rows)
+
+def upsert_product_summary(cur, prod, period_from, period_to):
+    if not prod: return 0
+    _ensure_rank_and_share(prod, key_sales="sales")
+    sql = """
+    INSERT INTO reporting_product_summary
+      (period_start, period_end, product_code, product_name, qty, sales, docs,
+       asp_weighted, mix_share_sales, rank_in_period)
+    VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)
+    ON DUPLICATE KEY UPDATE
+      qty=VALUES(qty), sales=VALUES(sales), docs=VALUES(docs),
+      asp_weighted=VALUES(asp_weighted), mix_share_sales=VALUES(mix_share_sales),
+      rank_in_period=VALUES(rank_in_period), generated_at=CURRENT_TIMESTAMP
+    """
+    rows = []
+    for r in prod:
+        rows.append((
+            period_from, period_to,
+            r.get("product_code"), r.get("product_name"),
+            safe_num(r.get("qty")),
+            safe_num(r.get("sales")),
+            safe_num(r.get("docs")),
+            safe_num(r.get("asp_weighted"), 6),
+            safe_num(r.get("mix_share_sales"), 6),
+            int(r.get("rank_in_period") or 0),
+        ))
+    cur.executemany(sql, rows)
+    return len(rows)
+
+def upsert_customer_summary(cur, cust, period_from, period_to):
+    if not cust: return 0
+    _ensure_rank_and_share(cust, key_sales="sales")
+    sql = """
+    INSERT INTO reporting_customer_summary
+      (period_start, period_end, customer_name, qty, sales, docs,
+       asp_weighted, mix_share_sales, rank_in_period)
+    VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s)
+    ON DUPLICATE KEY UPDATE
+      qty=VALUES(qty), sales=VALUES(sales), docs=VALUES(docs),
+      asp_weighted=VALUES(asp_weighted), mix_share_sales=VALUES(mix_share_sales),
+      rank_in_period=VALUES(rank_in_period), generated_at=CURRENT_TIMESTAMP
+    """
+    rows = []
+    for r in cust:
+        rows.append((
+            period_from, period_to,
+            r.get("customer_name"),
+            safe_num(r.get("qty")),
+            safe_num(r.get("sales")),
+            safe_num(r.get("docs")),
+            safe_num(r.get("asp_weighted"), 6),
+            safe_num(r.get("mix_share_sales"), 6),
+            int(r.get("rank_in_period") or 0),
+        ))
+    cur.executemany(sql, rows)
+    return len(rows)
+
+def upsert_product_daily(cur, prod_daily):
+    if not prod_daily: return 0
+    sql = """
+    INSERT INTO reporting_product_daily
+      (period_date, product_code, product_name, qty, sales, asp)
+    VALUES (%s,%s,%s,%s,%s,%s)
+    ON DUPLICATE KEY UPDATE
+      qty=VALUES(qty), sales=VALUES(sales), asp=VALUES(asp),
+      generated_at=CURRENT_TIMESTAMP
+    """
+    rows = []
+    for r in prod_daily:
+        period_date = safe_date(r.get("register_date") or r.get("period_date") or r.get("date"))
+        qty = safe_num(r.get("qty"))
+        sales = safe_num(r.get("sales"))
+        asp = safe_num((sales / qty) if (qty and sales is not None and qty != 0) else r.get("asp"), 6)
+        rows.append((
+            period_date,
+            r.get("product_code"),
+            r.get("product_name"),
+            qty, sales, asp
+        ))
+    cur.executemany(sql, rows)
+    return len(rows)
+
+# ========== ARGPARSE & LOGIKA WYBORU ==========
+
+def parse_cli_args():
+    p = argparse.ArgumentParser()
+    p.add_argument('--date-from', dest='date_from', required=False, help='YYYY-MM-DD')
+    p.add_argument('--date-to',   dest='date_to',   required=False, help='YYYY-MM-DD (inclusive, we add +1 day internally)')
+    # akceptuj obie formy: wielokrotne --metric oraz (opcjonalnie) --metrics CSV
+    p.add_argument('--metric',    dest='metric', action='append', default=[], help='Nazwa preagregatu; można podać wiele razy')
+    p.add_argument('--metrics',   dest='metrics', action='append', default=[], help='CSV: a,b,c (można podać wiele razy)')
+    p.add_argument('--ai',        dest='ai', choices=['true','false'], default='false')
+    return p.parse_args()
+
+def collect_metric_names(args):
+    names = []
+    # z --metric (powtarzalne)
+    if args.metric:
+        names.extend([s.strip() for s in args.metric if s and s.strip()])
+    # z --metrics (może być kilka wystąpień; każde może być CSV)
+    for entry in (args.metrics or []):
+        if not entry:
+            continue
+        for part in str(entry).replace(';', ',').replace(' ', ',').split(','):
+            part = part.strip()
+            if part:
+                names.append(part)
+    # aliasy dla kpis
+    alias_map = {'basic': 'kpis', 'basic_totals': 'kpis'}
+    names = [alias_map.get(n, n) for n in names]
+    # deduplikacja z zachowaniem kolejności
+    seen = set()
+    uniq = []
+    for n in names:
+        if n not in seen:
+            seen.add(n)
+            uniq.append(n)
+    return uniq
+
+def compute_selected_preaggs(rows, names):
+    """
+    Liczy TYLKO wskazane preagregaty. ZAWSZE zwraca DataFrame'y (nigdy listy).
+    Obsługuje pseudo-agregat 'kpis' (podstawowe KPI).
+    """
+    results = {}
+    if not names:
+        return results
+    df = pre.to_df(rows)
+
+    # kpis — pseudoagregat
+    def compute_kpis_df(dfx):
+        if dfx is None or dfx.empty:
+            return pd.DataFrame([{
+                "total_sales": 0.0,
+                "total_qty": 0.0,
+                "total_docs": 0,
+                "asp": None,
+            }])
+        total_sales = float(dfx["total_netto"].sum())
+        total_qty   = float(dfx["quantity"].sum())
+        total_docs  = int(dfx["document_no"].nunique())
+        asp = (total_sales / total_qty) if total_qty else None
+        return pd.DataFrame([{
+            "total_sales": total_sales,
+            "total_qty": total_qty,
+            "total_docs": total_docs,
+            "asp": asp,
+        }])
+
+    for name in names:
+        if name == 'kpis':
+            results[name] = compute_kpis_df(df)
+            continue
+
+        fn = pre.AGGREGATORS.get(name)
+        if not fn:
+            results[name] = pd.DataFrame()  # nieznany agregat -> pusty
+            continue
+        try:
+            out = fn(df)
+            if out is None:
+                results[name] = pd.DataFrame()
+            elif hasattr(out, "copy"):
+                results[name] = out.copy()
+            else:
+                results[name] = pd.DataFrame(out)
+        except Exception:
+            # np. top10_* na pustych danych -> zwróć pusty wynik
+            results[name] = pd.DataFrame()
+
+    return results
+
+def sanitize_serialized(serialized_dict):
+    """
+    Jeśli jakikolwiek agregat zwrócił błąd (np. _error), zamieniamy na pustą listę.
+    """
+    clean = {}
+    for k, records in (serialized_dict or {}).items():
+        if not records:
+            clean[k] = []
+            continue
+        if isinstance(records, list) and isinstance(records[0], dict) and ('_error' in records[0]):
+            clean[k] = []
+        else:
+            clean[k] = records
+    return clean
+
+def has_any_rows(serialized_dict):
+    for records in (serialized_dict or {}).values():
+        if records:  # lista niepusta
+            return True
+    return False
+
+# ========== MAIN ==========
+
+def main():
+    # --- CLI ---
+    args = parse_cli_args()
+    with_ai      = (args.ai == 'true')
+    metric_names = collect_metric_names(args)
+
+    # --- Daty: preferuj CLI; 'date_to' inkluzywne (dodajemy +1 dzień dla SQL '<') ---
+    if args.date_from and args.date_to:
+        period_from, period_to = args.date_from, add_one_day(args.date_to)
+        shown_label = "{} .. {}".format(args.date_from, args.date_to)
+    else:
+        env_from, env_to = getenv("PERIOD_FROM"), getenv("PERIOD_TO")
+        if env_from and env_to:
+            period_from, period_to = env_from, env_to
+            # label dla czytelności: to-1d
+            try:
+                to_label = (datetime.strptime(period_to, "%Y-%m-%d") - timedelta(days=1)).strftime("%Y-%m-%d")
+            except Exception:
+                to_label = period_to
+            shown_label = "{} .. {}".format(period_from, to_label)
+        else:
+            period_from, period_to = last_full_month_bounds()
+            # label: poprzedni pełny miesiąc
+            try:
+                to_label = (datetime.strptime(period_to, "%Y-%m-%d") - timedelta(days=1)).strftime("%Y-%m-%d")
+            except Exception:
+                to_label = period_to
+            shown_label = "{} .. {}".format(period_from, to_label)
+
+    # --- DB ---
+    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")),
+    }
+    invoice_type = getenv("INVOICE_TYPE", "normal")
+
+    # --- SQL -> rows (UWZGLĘDNIJ DATY; typ wg ENV) ---
+    try:
+        cnx = mysql.connector.connect(**cfg)
+        cur = cnx.cursor()
+        if invoice_type:
+            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
+                """,
+                (period_from, period_to, invoice_type),
+            )
+        else:
+            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
+                """,
+                (period_from, period_to),
+            )
+        rows = cur.fetchall()
+        cur.close()
+        cnx.close()
+    except Exception as e:
+        html_fatal(str(e), title="Błąd połączenia/zapytania MySQL")
+
+    # --- LICZ TYLKO WYBRANE PREAGREGATY (w tym pseudo 'kpis') ---
+    results = {}
+    serialized = {}
+    if metric_names:
+        results = compute_selected_preaggs(rows, metric_names)
+        serialized = serialize_for_ai(results)
+        serialized = sanitize_serialized(serialized)  # usuń ewentualne _error -> traktuj jako puste
+    else:
+        serialized = {}
+
+    # --- ZAPIS do reporting (tylko to, co faktycznie policzyłeś) ---
+    try:
+        if serialized:
+            rep_cfg = {
+                "host": "host.docker.internal",
+                "port": 3307,
+                "user": "remote",
+                "password": os.environ.get("REPORTING_PASSWORD", "areiufh*&^yhdua"),
+                "database": "ai",
+            }
+            if os.environ.get("REPORTING_SSL_CA"):
+                rep_cfg["ssl_ca"] = os.environ["REPORTING_SSL_CA"]
+            if os.environ.get("REPORTING_SSL_CERT"):
+                rep_cfg["ssl_cert"] = os.environ["REPORTING_SSL_CERT"]
+            if os.environ.get("REPORTING_SSL_KEY"):
+                rep_cfg["ssl_key"]  = os.environ["REPORTING_SSL_KEY"]
+
+            cnx2 = connect_html_or_die(rep_cfg, label="ReportingDB")
+            cur2 = cnx2.cursor()
+
+            if "daily_sales" in serialized:
+                upsert_daily_sales(cur2, serialized.get("daily_sales") or [])
+            if "product_summary" in serialized:
+                upsert_product_summary(cur2, serialized.get("product_summary") or [], period_from, period_to)
+            if "customer_summary" in serialized:
+                upsert_customer_summary(cur2, serialized.get("customer_summary") or [], period_from, period_to)
+            if "product_daily" in serialized:
+                upsert_product_daily(cur2, serialized.get("product_daily") or [])
+
+            cnx2.commit()
+            cur2.close(); cnx2.close()
+    except Exception as e:
+        sys.stderr.write(f"[reporting] ERROR: {e}\n")
+
+    # --- KPI: jeśli wybrano 'kpis' -> bierz z wyników; w przeciwnym razie spróbuj z daily_sales; inaczej zera ---
+    kpis = []
+    if "kpis" in results and isinstance(results["kpis"], pd.DataFrame) and not results["kpis"].empty:
+        r = results["kpis"].iloc[0]
+        total_sales = r.get("total_sales") or 0
+        total_qty   = r.get("total_qty") or 0
+        total_docs  = r.get("total_docs") or 0
+        asp         = r.get("asp")
+    else:
+        daily = serialized.get("daily_sales") or []
+        total_sales = sum((x.get("sales") or 0) for x in daily) if daily else 0
+        total_qty   = sum((x.get("qty") or 0) for x in daily) if daily else 0
+        total_docs  = sum((x.get("docs") or 0) for x in daily) if daily else 0
+        asp = (total_sales / total_qty) if total_qty else None
+
+    kpis = [
+        ("Sprzedaż (PLN)", fmt_money(total_sales)),
+        ("Ilość (szt.)", "{:,.0f}".format(total_qty).replace(",", " ")),
+        ("Dokumenty", "{:,.0f}".format(total_docs).replace(",", " ")),
+        ("ASP (PLN/szt.)", fmt_money(asp) if asp is not None else "—"),
+    ]
+
+    # --- Sekcje HTML: renderuj tylko te, które policzyłeś ---
+    parts = []
+    if "top10_products_by_sales" in serialized:
+        parts.append(html_table(serialized.get("top10_products_by_sales") or [], title="Top 10 produktów (po sprzedaży)", max_rows=10))
+    if "top10_customers_by_sales" in serialized:
+        parts.append(html_table(serialized.get("top10_customers_by_sales") or [], title="Top 10 klientów (po sprzedaży)",  max_rows=10))
+    if "daily_sales" in serialized:
+        parts.append(html_table(serialized.get("daily_sales") or [], title="Sprzedaż dzienna (skrót)", max_rows=30))
+    if "product_summary" in serialized:
+        parts.append(html_table(serialized.get("product_summary") or [], title="Podsumowanie produktów (skrót)", max_rows=30))
+    if "customer_summary" in serialized:
+        parts.append(html_table(serialized.get("customer_summary") or [], title="Podsumowanie klientów (skrót)", max_rows=30))
+    if "product_daily" in serialized:
+        parts.append(html_table(serialized.get("product_daily") or [], title="Produkt × Dzień (próbka)", max_rows=30))
+
+    # --- AI tylko gdy: --ai true ORAZ jest co najmniej jeden rekord w którymś z wybranych agregatów ---
+    api_key = API_KEY_HARDCODE or getenv("OPENAI_API_KEY", "")
+    model = getenv("OPENAI_MODEL", "gpt-4.1")
+    MODEL_ALIAS = {
+        "gpt-4.1": "GPT-4.1",
+        "gpt-4.1-mini": "GPT-4.1-mini",
+        "gpt-4o": "GPT-4o",
+        "gpt-4o-mini": "GPT-4o-mini",
+    }
+    model_alias = MODEL_ALIAS.get(model, model)
+
+    ai_section = ""
+    if with_ai and has_any_rows(serialized):
+        try:
+            ai_data = {"kpis_hint": {"period_label": shown_label}}
+            for name, records in serialized.items():
+                ai_data[name] = compact_table(records, 100)
+            ai_json = json.dumps(ai_data, ensure_ascii=False, separators=(",", ":"), default=str)
+
+            ai_section = call_openai_chat(
+                api_key=(api_key or ""),
+                model=model,
+                system_prompt=("Jesteś analitykiem sprzedaży. Zwróć TYLKO jedną sekcję HTML (bez <html>/<head>/<body>). "
+                               "Streszcz kluczowe trendy i daj 3–6 zaleceń. Po polsku."),
+                user_payload_json=ai_json,
+                temperature=0.3,
+                connect_timeout=10,
+                read_timeout=90,
+                max_retries=3,
+            )
+        except Exception as e:
+            err = str(e)
+            if "insufficient_quota" in err or "You exceeded your current quota" in err:
+                try:
+                    ai_section = call_openai_chat(
+                        api_key=(api_key or ""),
+                        model="gpt-4.1-mini",
+                        system_prompt=("Jesteś analitykiem sprzedaży. Zwróć TYLKO jedną sekcję HTML (bez <html>/<head>/<body>). "
+                                       "Streszcz kluczowe trendy i daj 3–6 zaleceń. Po polsku."),
+                        user_payload_json=ai_json,
+                        temperature=0.3,
+                        connect_timeout=10,
+                        read_timeout=90,
+                        max_retries=2,
+                    )
+                    model_alias = "GPT-4.1-mini"
+                except Exception as ee:
+                    ai_section = (
+                        '<div style="color:#991b1b;background:#fff5f5;border:1px solid #fecaca;'
+                        'padding:10px;border-radius:8px;">Brak dostępnego limitu API. {}</div>'.format(str(ee))
+                    )
+            else:
+                ai_section = (
+                    '<div style="color:#991b1b;background:#fff5f5;border:1px solid #fecaca;'
+                    'padding:10px;border-radius:8px;">Błąd wywołania AI: {}</div>'.format(err)
+                )
+    else:
+        ai_section = '<div style="color:#6b7280">Analiza AI wyłączona lub brak wybranych danych.</div>'
+        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()

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/report_form.php

@@ -0,0 +1,177 @@
+<?php
+/**
+ * report_form.php — formularz + uruchomienie analysisAI.py z parametrami
+ * ZGODNE z PHP 5.6 i Sugar 6 (wyciszone E_STRICT/E_DEPRECATED/NOTICE).
+ */
+
+// --- wycisz „hałas” Sugar CRM ---
+error_reporting(E_ALL & ~E_STRICT & ~E_DEPRECATED & ~E_NOTICE);
+ini_set('display_errors', '0');
+// (opcjonalnie) loguj do pliku
+// ini_set('log_errors', '1');
+// ini_set('error_log', '/var/log/php_form_errors.log');
+
+// --- ŚCIEŻKI (dostosuj do swojej instalacji) ---
+$python = '/usr/local/bin/python3';
+$script = '/var/www/html/modules/EcmInvoiceOuts/ai/analysisAI.py';
+$baseDir = dirname($script);
+
+// --- domyślne wartości pól ---
+$defaultDateTo   = date('Y-m-d');
+$defaultDateFrom = date('Y-m-d', strtotime('-7 days'));
+
+// --- zbieranie POST (PHP 5.6 friendly) ---
+$submitted      = (isset($_SERVER['REQUEST_METHOD']) && $_SERVER['REQUEST_METHOD'] === 'POST');
+$post_date_from = isset($_POST['date_from']) ? $_POST['date_from'] : $defaultDateFrom;
+$post_date_to   = isset($_POST['date_to'])   ? $_POST['date_to']   : $defaultDateTo;
+$post_preaggs   = (isset($_POST['preaggs']) && is_array($_POST['preaggs'])) ? $_POST['preaggs'] : array();
+$post_with_ai   = !empty($_POST['with_ai']);
+
+function h($v) { return htmlspecialchars($v, ENT_QUOTES, 'UTF-8'); }
+function is_valid_date_yyyy_mm_dd($d) {
+    return (bool)preg_match('/^\d{4}-\d{2}-\d{2}$/', $d);
+}
+
+// --- wykonanie skryptu Pythona, jeśli formularz został wysłany ---
+$ran = false;
+$ok  = false;
+$rc  = 0;
+$out = '';
+$err = '';
+
+if ($submitted) {
+    // prosta walidacja dat
+    if (!is_valid_date_yyyy_mm_dd($post_date_from) || !is_valid_date_yyyy_mm_dd($post_date_to)) {
+        $err = "Nieprawidłowy format daty. Użyj YYYY-MM-DD.";
+        $ran = true;
+    } else {
+        // zbuduj argumenty
+        $args = array(
+                '--date-from', $post_date_from,
+                '--date-to',   $post_date_to,
+                '--ai',        ($post_with_ai ? 'true' : 'false')
+        );
+        if (!empty($post_preaggs)) {
+            // CSV z zaznaczonych preagregatów
+            $args[] = '--metrics';
+            $args[] = implode(',', $post_preaggs);
+        }
+
+        // komenda: przejdź do katalogu skryptu, uruchom pythona; zbierz stdout+stderr
+        $cmd = 'cd ' . escapeshellarg($baseDir) . ' && ' .
+                escapeshellcmd($python) . ' ' . escapeshellarg($script);
+
+        foreach ($args as $a) {
+            $cmd .= ' ' . escapeshellarg($a);
+        }
+
+        $output = array();
+        $returnVar = 0;
+        exec($cmd . ' 2>&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" . $out;
+        }
+    }
+}
+?>
+<!doctype html>
+<html lang="pl">
+<head>
+    <meta charset="utf-8">
+    <title>Generator raportu sprzedaży</title>
+    <meta name="viewport" content="width=device-width, initial-scale=1">
+    <style>
+        body { font: 14px/1.4 system-ui, Arial, sans-serif; padding: 20px; }
+        fieldset { margin-bottom: 16px; padding: 12px; border-radius: 6px; border:1px solid #e5e5e5; }
+        .row { display: flex; gap: 16px; flex-wrap: wrap; }
+        .col { min-width: 220px; flex: 1; }
+        label { display:block; margin: 6px 0; }
+        input[type="date"], button { padding: 6px 10px; font-size:14px; }
+        button { margin-top: 10px; cursor: pointer; border:1px solid #0a66c2; background:#0a66c2; color:#fff; border-radius:8px; }
+        .pill { display:inline-block; padding:2px 8px; border-radius:999px; background:#eee; margin:4px 6px 0 0; }
+        .out { white-space: normal; background: #fff; border:1px solid #ddd; padding:12px; border-radius:6px; }
+        .error { white-space: pre-wrap; background: #fff3f3; border:1px solid #f3c2c2; padding:12px; border-radius:6px; color:#b00020; }
+        .muted { color:#666; }
+    </style>
+</head>
+<body>
+
+<h1>Raport sprzedaży — parametry</h1>
+
+<form method="post">
+    <!-- zakres dat -->
+    <fieldset>
+        <legend>Zakres dat</legend>
+        <div class="row">
+            <div class="col">
+                <label>Data od:
+                    <input type="date" name="date_from" value="<?php echo h($post_date_from); ?>" required>
+                </label>
+            </div>
+            <div class="col">
+                <label>Data do:
+                    <input type="date" name="date_to" value="<?php echo h($post_date_to); ?>" required>
+                </label>
+            </div>
+        </div>
+    </fieldset>
+
+    <!-- preagregaty -->
+    <fieldset>
+        <legend>Preagregaty do analizy</legend>
+        <label><input type="checkbox" name="preaggs[]" value="daily_sales"             <?php echo in_array('daily_sales', $post_preaggs, true) ? 'checked' : ''; ?>> Dzienne sprzedaże</label>
+        <label><input type="checkbox" name="preaggs[]" value="product_summary"         <?php echo in_array('product_summary', $post_preaggs, true) ? 'checked' : ''; ?>> Podsumowanie produktów</label>
+        <label><input type="checkbox" name="preaggs[]" value="customer_summary"        <?php echo in_array('customer_summary', $post_preaggs, true) ? 'checked' : ''; ?>> Podsumowanie klientów</label>
+        <label><input type="checkbox" name="preaggs[]" value="product_daily"           <?php echo in_array('product_daily', $post_preaggs, true) ? 'checked' : ''; ?>> Sprzedaż produktu dziennie</label>
+        <label><input type="checkbox" name="preaggs[]" value="top10_products_by_sales" <?php echo in_array('top10_products_by_sales', $post_preaggs, true) ? 'checked' : ''; ?>> Top10 produktów</label>
+        <label><input type="checkbox" name="preaggs[]" value="top10_customers_by_sales"<?php echo in_array('top10_customers_by_sales', $post_preaggs, true) ? 'checked' : ''; ?>> Top10 klientów</label>
+    </fieldset>
+
+    <!-- AI -->
+    <fieldset>
+        <legend>Analiza AI</legend>
+        <label>
+            <input type="checkbox" name="with_ai" <?php echo $post_with_ai ? 'checked' : ''; ?>> Dołącz analizę AI
+        </label>
+    </fieldset>
+
+    <button type="submit">Generuj</button>
+</form>
+
+<?php if ($submitted): ?>
+    <hr>
+    <h2>Użyte parametry</h2>
+    <p>
+        <span class="pill">Od: <?php echo h($post_date_from); ?></span>
+        <span class="pill">Do: <?php echo h($post_date_to); ?></span>
+        <span class="pill">AI: <?php echo $post_with_ai ? 'tak' : 'nie'; ?></span>
+    </p>
+    <p>Preagregaty:
+        <?php
+        if (!empty($post_preaggs)) {
+            foreach ($post_preaggs as $p) {
+                echo '<span class="pill">'.h($p).'</span>';
+            }
+        } else {
+            echo '<span class="muted">brak</span>';
+        }
+        ?>
+    </p>
+
+    <h2>Wynik analizy</h2>
+    <?php if (!$ok): ?>
+        <div class="error"><?php echo h($err); ?></div>
+    <?php else: ?>
+        <!-- Zakładamy, że Python zwraca gotowy HTML -->
+        <div class="out"><?php echo $out; ?></div>
+    <?php endif; ?>
+<?php endif; ?>
+
+</body>
+</html>

modules/EcmInvoiceOuts/test.php

@@ -1,16 +1,28 @@
 <?php
-// Runs the Python script, waits for completion, and returns its output.
+$python = '/usr/local/bin/python3';
+$script = '/var/www/html/modules/EcmInvoiceOuts/ai/analysisAI.py';
+$cmd = escapeshellcmd("$python $script");
 
-$cmd = 'python3 /var/www/html/modules/EcmInvoiceOuts/ai/test.py';
+// 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" . 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