diff --git a/modules/EcmInvoiceOuts/ai/analysisAI.py b/modules/EcmInvoiceOuts/ai/analysisAI.py index 77327e87..dc56c30c 100644 --- a/modules/EcmInvoiceOuts/ai/analysisAI.py +++ b/modules/EcmInvoiceOuts/ai/analysisAI.py @@ -1,31 +1,224 @@ #!/usr/bin/env python3 -import os, sys, json +# -*- coding: utf-8 -*- + +""" +analysisAI.py — pobiera dane z MySQL, liczy preagregaty, renderuje HTML i dokłada analizę AI. + +ZMIENNE ŚRODOWISKOWE (mają domyślne wartości): + OPENAI_API_KEY - klucz do OpenAI (gdy pusty -> skrypt pokaże wersję bez AI) + OPENAI_MODEL - np. gpt-4.1 (domyślnie) + MYSQL_HOST - host MySQL (domyślnie: localhost) + MYSQL_USER - użytkownik MySQL (domyślnie: root) + MYSQL_PASSWORD - hasło MySQL (domyślnie: rootpassword) + MYSQL_DATABASE - nazwa bazy (domyślnie: preDb_0dcc87940d3655fa574b253df04ca1c3) + MYSQL_PORT - port MySQL (domyślnie: 3306) + PERIOD_FROM - data od (YYYY-MM-DD); gdy brak -> poprzedni pełny miesiąc + PERIOD_TO - data do (YYYY-MM-DD, exclusive); gdy brak -> pierwszy dzień bieżącego miesiąca + INVOICE_TYPE - typ dokumentu (domyślnie: normal) +""" + +import os, sys, json, math, time, warnings +from datetime import date, timedelta + +API_KEY = "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" + + +# Wycisz ostrzeżenie urllib3 (LibreSSL na macOS itp.) +try: + from urllib3.exceptions import NotOpenSSLWarning + warnings.filterwarnings("ignore", category=NotOpenSSLWarning) +except Exception: + pass + +import requests +import mysql.connector 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) +# --------- utils --------- -def getenv(key, default=None): - return os.environ.get(key, default) +def getenv(k, d=None): + return os.environ.get(k, d) + +def last_full_month_bounds(): + """Zwraca (from_iso, to_iso) dla poprzedniego pełnego miesiąca.""" + 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 compact_table(table, limit=30): + """Przytnij listę rekordów i znormalizuj liczby (NaN/Inf -> None).""" + out = [] + if not table: + return out + for i, row in enumerate(table): + if i >= int(limit): 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 build_ai_payload(serialized, period_label): + """Kompaktowy JSON do AI (ograniczone rozmiary).""" + return { + "kpis_hint": {"period_label": period_label}, + "daily_sales": compact_table(serialized.get("daily_sales"), 30), + "product_summary": compact_table(serialized.get("product_summary"), 50), + "customer_summary": compact_table(serialized.get("customer_summary"), 50), + "top10_products_by_sales": compact_table(serialized.get("top10_products_by_sales"), 10), + "top10_customers_by_sales": compact_table(serialized.get("top10_customers_by_sales"), 10), + "product_daily_sample": compact_table(serialized.get("product_daily"), 40), + } + +def call_openai_chat(api_key, model, system_prompt, user_payload_json, + temperature=0.3, connect_timeout=10, read_timeout=90, max_retries=3): + """Wywołanie Chat Completions (retry + backoff). Zwraca HTML (sekcję) od AI.""" + 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, + # "max_tokens": 1200, # możesz odkomentować, aby ograniczyć długość odpowiedzi + } + 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 fmt_money(v): + try: + return "{:,.2f}".format(float(v)).replace(",", " ").replace(".", ",") + except Exception: + return str(v) + +def html_table(records, title=None, max_rows=20): + """Proste generowanie tabeli HTML z listy dict-ów.""" + if not records: + return '
Brak danych
' + cols = list(records[0].keys()) + body_rows = records[:max_rows] + thead = "".join("{}".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)): + # format dla kolumn „sales”, „qty”, „asp”, itp. – lekko ładniej + if "sales" in c or "total" in c or "netto" in c: + tds.append('{}'.format(fmt_money(val))) + else: + tds.append('{}'.format(val)) + else: + tds.append('{}'.format(val)) + trs.append("{}".format("".join(tds))) + cap = '
{}
'.format(title) if title else "" + return ( + cap + + '
' + '{}{}
'.format(thead, "".join(trs)) + ) + +def render_report_html(period_label, kpis, parts, ai_section): + """Składa finalny jeden
z lekkim CSS inline.""" + 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 = ( + '
{label}
' + '
{value}
' + ) + kpi_html = "".join( + kpi_item.format(label=lbl, value=val) for (lbl, val) in kpis + ) + sections_html = "".join(parts) + # jeśli AI nie zwróciło
, owiń + if ai_section and not ai_section.lstrip().startswith(" +

Raport sprzedaży — {period_label}

+
+ {kpi_html} +
+ {sections_html} +
+

Analiza i rekomendacje (AI)

+ {ai_section if ai_section else '
Brak odpowiedzi AI (brak OPENAI_API_KEY)
'} +
+
+ +""" + +# --------- main --------- def main(): + # Konfiguracja DB cfg = { - #"host": getenv("MYSQL_HOST", "twinpol-mysql56"), - "host": getenv("MYSQL_HOST", "localhost"), + "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")), } + # Zakres dat + period_from = getenv("PERIOD_FROM") + period_to = getenv("PERIOD_TO") + if not period_from or not period_to: + period_from, period_to = last_full_month_bounds() + period_label = "{} .. {}".format(period_from, period_to) + invoice_type = getenv("INVOICE_TYPE", "normal") + + # Konfiguracja AI + #api_key = getenv("OPENAI_API_KEY", "") + api_key = API_KEY + model = getenv("OPENAI_MODEL", "gpt-4.1") + system_prompt = ( + "Jesteś analitykiem sprzedaży. Zwróć TYLKO jedną sekcję HTML (bez //), " + "może być pojedynczy
z nagłówkami i listami. Podsumuj trendy, wskaż kluczowe produkty/klientów, " + "anomalia/odchylenia oraz daj 3–6 praktycznych rekomendacji. Krótko, konkretnie, po polsku." + ) + + # SQL -> rows try: cnx = mysql.connector.connect(**cfg) cur = cnx.cursor() - #cur.execute("SELECT COUNT(*) FROM ecminvoiceouts WHERE YEAR(register_date)=2025") - cur.execute(""" + cur.execute( + """ SELECT i.document_no, i.parent_name, DATE(i.register_date) AS register_date, @@ -38,37 +231,88 @@ def main(): WHERE i.register_date >= %s AND i.register_date < %s AND i.type = %s - """, ("2025-07-01", "2025-08-01", "normal")) + """, + (period_from, period_to, invoice_type), + ) 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.stdout.write( + '
' + '

Błąd połączenia/zapytania MySQL

' + '

{}

'.format(str(e)) + ) sys.exit(1) + # Preagregaty + try: + results = compute_preaggregates(rows) + serialized = serialize_for_ai(results) + except Exception as e: + sys.stdout.write( + '
' + '

Błąd preagregacji

' + '

{}

'.format(str(e)) + ) + sys.exit(1) + + # KPI (na podstawie daily_sales) + daily = serialized.get("daily_sales") or [] + total_sales = sum((r.get("sales") or 0) for r in daily) + total_qty = sum((r.get("qty") or 0) for r in daily) + total_docs = sum((r.get("docs") or 0) for r in daily) + 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 z Twoich preagregatów + top_prod = serialized.get("top10_products_by_sales") or [] + top_cli = serialized.get("top10_customers_by_sales") or [] + prod_tbl = html_table(top_prod, title="Top 10 produktów (po sprzedaży)", max_rows=10) + cust_tbl = html_table(top_cli, title="Top 10 klientów (po sprzedaży)", max_rows=10) + + # Dane do AI + ai_data = build_ai_payload(serialized, period_label) + ai_json = json.dumps(ai_data, ensure_ascii=False, separators=(",", ":"), default=str) + + # Wołanie AI (opcjonalne) + ai_section = "" + if api_key: + try: + ai_section = call_openai_chat( + api_key=api_key, + model=model, + system_prompt=system_prompt, + user_payload_json=ai_json, + temperature=0.3, + connect_timeout=10, + read_timeout=90, + max_retries=3, + ) + except Exception as e: + ai_section = ( + '
Błąd wywołania AI: {}
'.format(str(e)) + ) + + # Finalny HTML (jeden
) + report_html = render_report_html( + period_label=period_label, + kpis=kpis, + parts=[prod_tbl, cust_tbl], + ai_section=ai_section + ) + + sys.stdout.write(report_html) + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/modules/EcmInvoiceOuts/ai/preaggregates.py b/modules/EcmInvoiceOuts/ai/preaggregates.py index 61e8bbea..b000f06b 100644 --- a/modules/EcmInvoiceOuts/ai/preaggregates.py +++ b/modules/EcmInvoiceOuts/ai/preaggregates.py @@ -117,8 +117,8 @@ def top10_customers_by_sales(df: pd.DataFrame) -> pd.DataFrame: # ------------------- Runner ------------------- -# def compute_preaggregates(rows: List[tuple]) -> dict[str, pd.DataFrame]: -def compute_preaggregates(rows): +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] = {} diff --git a/modules/EcmInvoiceOuts/test.php b/modules/EcmInvoiceOuts/test.php index 06faf59f..d3ba879e 100644 --- a/modules/EcmInvoiceOuts/test.php +++ b/modules/EcmInvoiceOuts/test.php @@ -1,44 +1,28 @@ &1", $out, $ret); - echo htmlspecialchars("$b -> ".($out ? implode(' ', $out) : "not found / not executable")." (ret=$ret)")."
"; +$python = '/usr/local/bin/python3'; +$script = '/var/www/html/modules/EcmInvoiceOuts/ai/analysisAI.py'; +$cmd = escapeshellcmd("$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".$body; + exit; } -//$python = '/usr/bin/python3'; -//$script = '/var/www/html/modules/EcmInvoiceOuts/ai/analysisAI.py'; -//$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 + +// --- 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