319 lines
13 KiB
Python
319 lines
13 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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analysisAI.py — pobiera dane z MySQL, liczy preagregaty, renderuje HTML i dokłada analizę AI.
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ZMIENNE ŚRODOWISKOWE (mają domyślne wartości):
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OPENAI_API_KEY - klucz do OpenAI (gdy pusty -> skrypt pokaże wersję bez AI)
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OPENAI_MODEL - np. gpt-4.1 (domyślnie)
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MYSQL_HOST - host MySQL (domyślnie: localhost)
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MYSQL_USER - użytkownik MySQL (domyślnie: root)
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MYSQL_PASSWORD - hasło MySQL (domyślnie: rootpassword)
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MYSQL_DATABASE - nazwa bazy (domyślnie: preDb_0dcc87940d3655fa574b253df04ca1c3)
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MYSQL_PORT - port MySQL (domyślnie: 3306)
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PERIOD_FROM - data od (YYYY-MM-DD); gdy brak -> poprzedni pełny miesiąc
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PERIOD_TO - data do (YYYY-MM-DD, exclusive); gdy brak -> pierwszy dzień bieżącego miesiąca
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INVOICE_TYPE - typ dokumentu (domyślnie: normal)
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"""
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import os, sys, json, math, time, warnings
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from datetime import date, timedelta
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API_KEY = "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"
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# Wycisz ostrzeżenie urllib3 (LibreSSL na macOS itp.)
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try:
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from urllib3.exceptions import NotOpenSSLWarning
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warnings.filterwarnings("ignore", category=NotOpenSSLWarning)
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except Exception:
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pass
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import requests
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import mysql.connector
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from preaggregates import compute_preaggregates, serialize_for_ai
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# --------- utils ---------
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def getenv(k, d=None):
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return os.environ.get(k, d)
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def last_full_month_bounds():
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"""Zwraca (from_iso, to_iso) dla poprzedniego pełnego miesiąca."""
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today_first = date.today().replace(day=1)
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to_dt = today_first
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prev_last = today_first - timedelta(days=1)
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from_dt = prev_last.replace(day=1)
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return from_dt.isoformat(), to_dt.isoformat()
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def compact_table(table, limit=30):
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"""Przytnij listę rekordów i znormalizuj liczby (NaN/Inf -> None)."""
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out = []
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if not table:
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return out
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for i, row in enumerate(table):
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if i >= int(limit): break
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new = {}
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for k, v in row.items():
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if isinstance(v, float):
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new[k] = round(v, 6) if math.isfinite(v) else None
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else:
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new[k] = v
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out.append(new)
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return out
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def build_ai_payload(serialized, period_label):
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"""Kompaktowy JSON do AI (ograniczone rozmiary)."""
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return {
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"kpis_hint": {"period_label": period_label},
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"daily_sales": compact_table(serialized.get("daily_sales"), 30),
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"product_summary": compact_table(serialized.get("product_summary"), 50),
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"customer_summary": compact_table(serialized.get("customer_summary"), 50),
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"top10_products_by_sales": compact_table(serialized.get("top10_products_by_sales"), 10),
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"top10_customers_by_sales": compact_table(serialized.get("top10_customers_by_sales"), 10),
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"product_daily_sample": compact_table(serialized.get("product_daily"), 40),
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}
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def call_openai_chat(api_key, model, system_prompt, user_payload_json,
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temperature=0.3, connect_timeout=10, read_timeout=90, max_retries=3):
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"""Wywołanie Chat Completions (retry + backoff). Zwraca HTML (sekcję) od AI."""
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url = "https://api.openai.com/v1/chat/completions"
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headers = {"Authorization": "Bearer " + api_key, "Content-Type": "application/json"}
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body = {
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"model": model,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": "Dane (JSON):\n\n" + user_payload_json},
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],
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"temperature": temperature,
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# "max_tokens": 1200, # możesz odkomentować, aby ograniczyć długość odpowiedzi
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}
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last_err = None
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for attempt in range(1, int(max_retries) + 1):
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try:
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r = requests.post(url, headers=headers, json=body, timeout=(connect_timeout, read_timeout))
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if 200 <= r.status_code < 300:
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data = r.json()
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return data.get("choices", [{}])[0].get("message", {}).get("content", "")
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last_err = RuntimeError("OpenAI HTTP {}: {}".format(r.status_code, r.text))
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except requests.exceptions.RequestException as e:
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last_err = e
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time.sleep(min(2 ** attempt, 10))
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raise RuntimeError("OpenAI request failed: {}".format(last_err))
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def fmt_money(v):
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try:
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return "{:,.2f}".format(float(v)).replace(",", " ").replace(".", ",")
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except Exception:
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return str(v)
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def html_table(records, title=None, max_rows=20):
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"""Proste generowanie tabeli HTML z listy dict-ów."""
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if not records:
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return '<div class="empty">Brak danych</div>'
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cols = list(records[0].keys())
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body_rows = records[:max_rows]
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thead = "".join("<th>{}</th>".format(c) for c in cols)
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trs = []
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for r in body_rows:
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tds = []
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for c in cols:
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val = r.get(c, "")
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if isinstance(val, (int, float)):
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# format dla kolumn „sales”, „qty”, „asp”, itp. – lekko ładniej
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if "sales" in c or "total" in c or "netto" in c:
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tds.append('<td class="num">{}</td>'.format(fmt_money(val)))
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else:
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tds.append('<td class="num">{}</td>'.format(val))
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else:
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tds.append('<td>{}</td>'.format(val))
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trs.append("<tr>{}</tr>".format("".join(tds)))
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cap = '<div class="tbl-title">{}</div>'.format(title) if title else ""
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return (
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cap +
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'<div class="tbl-wrap"><table class="tbl">'
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'<thead><tr>{}</tr></thead><tbody>{}</tbody></table></div>'.format(thead, "".join(trs))
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)
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def render_report_html(period_label, kpis, parts, ai_section):
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"""Składa finalny jeden <div> z lekkim CSS inline."""
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css = (
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"font-family:system-ui,-apple-system,Segoe UI,Roboto,Arial,sans-serif;"
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"max-width:1200px;margin:24px auto;padding:16px 20px;border:1px solid #e5e7eb;"
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"border-radius:12px;background:#fff;color:#111827"
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)
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kpi_item = (
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'<div class="kpi"><div class="kpi-label">{label}</div>'
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'<div class="kpi-value">{value}</div></div>'
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)
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kpi_html = "".join(
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kpi_item.format(label=lbl, value=val) for (lbl, val) in kpis
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)
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sections_html = "".join(parts)
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# jeśli AI nie zwróciło <div>, owiń
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if ai_section and not ai_section.lstrip().startswith("<div"):
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ai_section = '<div class="ai-section">{}</div>'.format(ai_section)
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return f"""
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<div style="{css}">
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<h2 style="margin:0 0 12px;font-size:22px;">Raport sprzedaży — {period_label}</h2>
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<div style="display:grid;grid-template-columns:repeat(4,minmax(0,1fr));gap:12px;margin:12px 0 20px;">
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{kpi_html}
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</div>
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{sections_html}
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<div style="margin-top:20px;border-top:1px solid #e5e7eb;padding-top:16px;">
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<h3 style="margin:0 0 8px;font-size:18px;">Analiza i rekomendacje (AI)</h3>
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{ai_section if ai_section else '<div style="color:#6b7280">Brak odpowiedzi AI (brak OPENAI_API_KEY)</div>'}
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</div>
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</div>
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<style>
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.kpi {{background:#f8fafc;border:1px solid #e5e7eb;border-radius:10px;padding:12px;}}
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.kpi-label {{font-size:12px;color:#6b7280;margin-bottom:4px;}}
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.kpi-value {{font-size:18px;font-weight:700;}}
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.tbl-title {{font-weight:600;margin:16px 0 8px;font-size:15px;}}
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.tbl-wrap {{overflow-x:auto;border:1px solid #e5e7eb;border-radius:8px;}}
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.tbl {{border-collapse:collapse;width:100%;font-size:14px;}}
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.tbl thead th {{text-align:left;background:#f3f4f6;padding:8px;border-bottom:1px solid #e5e7eb;white-space:nowrap;}}
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.tbl tbody td {{padding:8px;border-bottom:1px solid #f3f4f6;vertical-align:top;}}
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.tbl td.num {{text-align:right;white-space:nowrap;}}
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.empty {{color:#6b7280;font-style:italic;margin:8px 0;}}
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.ai-section {{background:#f8fafc;border:1px solid #e5e7eb;border-radius:10px;padding:12px;}}
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</style>
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"""
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# --------- main ---------
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def main():
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# Konfiguracja DB
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cfg = {
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"host": getenv("MYSQL_HOST", "twinpol-mysql56"),
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# "host": getenv("MYSQL_HOST", "localhost"),
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"user": getenv("MYSQL_USER", "root"),
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"password": getenv("MYSQL_PASSWORD", "rootpassword"),
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"database": getenv("MYSQL_DATABASE", "preDb_0dcc87940d3655fa574b253df04ca1c3"),
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"port": int(getenv("MYSQL_PORT", "3306")),
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}
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# Zakres dat
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period_from = getenv("PERIOD_FROM")
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period_to = getenv("PERIOD_TO")
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if not period_from or not period_to:
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period_from, period_to = last_full_month_bounds()
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period_label = "{} .. {}".format(period_from, period_to)
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invoice_type = getenv("INVOICE_TYPE", "normal")
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# Konfiguracja AI
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#api_key = getenv("OPENAI_API_KEY", "")
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api_key = API_KEY
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model = getenv("OPENAI_MODEL", "gpt-4.1")
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system_prompt = (
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"Jesteś analitykiem sprzedaży. Zwróć TYLKO jedną sekcję HTML (bez <html>/<head>/<body>), "
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"może być pojedynczy <div> z nagłówkami i listami. Podsumuj trendy, wskaż kluczowe produkty/klientów, "
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"anomalia/odchylenia oraz daj 3–6 praktycznych rekomendacji. Krótko, konkretnie, po polsku."
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)
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# SQL -> rows
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try:
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cnx = mysql.connector.connect(**cfg)
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cur = cnx.cursor()
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cur.execute(
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"""
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SELECT i.document_no,
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i.parent_name,
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DATE(i.register_date) AS register_date,
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ii.code,
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ii.name,
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ii.quantity,
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ii.total_netto
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FROM ecminvoiceoutitems AS ii
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JOIN ecminvoiceouts AS i ON i.id = ii.ecminvoiceout_id
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WHERE i.register_date >= %s
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AND i.register_date < %s
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AND i.type = %s
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""",
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(period_from, period_to, invoice_type),
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)
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rows = cur.fetchall()
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cur.close()
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cnx.close()
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except Exception as e:
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sys.stdout.write(
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'<div style="font-family:system-ui,-apple-system,Segoe UI,Roboto,Arial,sans-serif;'
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'max-width:900px;margin:24px auto;padding:16px 20px;border:1px solid #fecaca;'
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'border-radius:12px;background:#fff5f5;color:#991b1b;">'
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'<h3 style="margin:0 0 8px;font-size:18px;">Błąd połączenia/zapytania MySQL</h3>'
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'<p style="margin:0;">{}</p></div>'.format(str(e))
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)
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sys.exit(1)
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# Preagregaty
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try:
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results = compute_preaggregates(rows)
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serialized = serialize_for_ai(results)
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except Exception as e:
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sys.stdout.write(
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'<div style="font-family:system-ui,-apple-system,Segoe UI,Roboto,Arial,sans-serif;'
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'max-width:900px;margin:24px auto;padding:16px 20px;border:1px solid #fecaca;'
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'border-radius:12px;background:#fff5f5;color:#991b1b;">'
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'<h3 style="margin:0 0 8px;font-size:18px;">Błąd preagregacji</h3>'
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'<p style="margin:0;">{}</p></div>'.format(str(e))
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)
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sys.exit(1)
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# KPI (na podstawie daily_sales)
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daily = serialized.get("daily_sales") or []
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total_sales = sum((r.get("sales") or 0) for r in daily)
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total_qty = sum((r.get("qty") or 0) for r in daily)
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total_docs = sum((r.get("docs") or 0) for r in daily)
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asp = (total_sales / total_qty) if total_qty else None
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kpis = [
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("Sprzedaż (PLN)", fmt_money(total_sales)),
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("Ilość (szt.)", "{:,.0f}".format(total_qty).replace(",", " ")),
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("Dokumenty", "{:,.0f}".format(total_docs).replace(",", " ")),
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("ASP (PLN/szt.)", fmt_money(asp) if asp is not None else "—"),
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]
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# Sekcje HTML z Twoich preagregatów
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top_prod = serialized.get("top10_products_by_sales") or []
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top_cli = serialized.get("top10_customers_by_sales") or []
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prod_tbl = html_table(top_prod, title="Top 10 produktów (po sprzedaży)", max_rows=10)
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cust_tbl = html_table(top_cli, title="Top 10 klientów (po sprzedaży)", max_rows=10)
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# Dane do AI
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ai_data = build_ai_payload(serialized, period_label)
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ai_json = json.dumps(ai_data, ensure_ascii=False, separators=(",", ":"), default=str)
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# Wołanie AI (opcjonalne)
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ai_section = ""
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if api_key:
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try:
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ai_section = call_openai_chat(
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api_key=api_key,
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model=model,
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system_prompt=system_prompt,
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user_payload_json=ai_json,
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temperature=0.3,
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connect_timeout=10,
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read_timeout=90,
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max_retries=3,
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)
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except Exception as e:
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ai_section = (
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'<div style="color:#991b1b;background:#fff5f5;border:1px solid #fecaca;'
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'padding:10px;border-radius:8px;">Błąd wywołania AI: {}</div>'.format(str(e))
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)
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# Finalny HTML (jeden <div>)
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report_html = render_report_html(
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period_label=period_label,
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kpis=kpis,
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parts=[prod_tbl, cust_tbl],
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ai_section=ai_section
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)
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sys.stdout.write(report_html)
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if __name__ == "__main__":
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main()
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