sprzadaz analiza AI

modules/EcmInvoiceOuts/ai/enqueue.php

@@ -0,0 +1,21 @@
+<?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/result.php

@@ -0,0 +1,12 @@
+<?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/worker.py

@@ -0,0 +1,141 @@
+# 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/bimit_invoiceSummary.php

@@ -1,66 +1,164 @@
-<?php
+<!-- KPI -->
+<div class="grid">
+    <div class="card">
+        <div class="kpi-label">Łączny przychód</div>
+        <div class="kpi-value">587&nbsp;679,40 PLN</div>
+    </div>
+    <div class="card">
+        <div class="kpi-label">Liczba faktur</div>
+        <div class="kpi-value">320</div>
+    </div>
+    <div class="card">
+        <div class="kpi-label">Sprzedane jednostki</div>
+        <div class="kpi-value">182&nbsp;619 szt.</div>
+    </div>
+    <div class="card">
+        <div class="kpi-label">AOV — średnia wartość faktury</div>
+        <div class="kpi-value">1&nbsp;836,50 PLN</div>
+        <div class="kpi-sub muted">AOV = przychód / liczba faktur</div>
+    </div>
+</div>
 
-$db = $GLOBALS['db'];
+<!-- Top produkty -->
+<h2>Top produkty wg przychodu</h2>
+<div class="card table-card">
+    <table>
+        <thead>
+        <tr>
+            <th style="width:22%">Kod</th>
+            <th>Produkt</th>
+            <th style="width:18%">Przychód [PLN]</th>
+        </tr>
+        </thead>
+        <tbody>
+        <tr><td>FR00099_250_Wilfa</td><td>WIUCC-250 CLEANING LIQUID COFFEEMAKER, 250 ml</td><td>51&nbsp;217,92</td></tr>
+        <tr><td>AGDPR01</td><td>Środek do czyszczenia pralek automatycznych</td><td>47&nbsp;500,00</td></tr>
+        <tr><td>FR00013_1000_Drekker</td><td>Odkamieniacz do automatycznych ekspresów do kawy, 1000 ml</td><td>30&nbsp;600,00</td></tr>
+        <tr><td>AGDCHRM01</td><td>Płyn do robotów mopujących, 500ml</td><td>22&nbsp;277,70</td></tr>
+        <tr><td>FR00016_10_2g_amz_de</td><td>Cleaning tablets for coffee machines, 10 x 2g</td><td>19&nbsp;426,00</td></tr>
+        </tbody>
+    </table>
+</div>
 
-$query = "
-SELECT s.document_no, s.register_date, s.parent_name, s.total_netto, si.code, si.name, si.quantity, si.price_netto
-FROM ecmsaleitems AS si
-INNER JOIN ecmsales AS s ON si.ecmsale_id = s.id
-WHERE s.register_date >= NOW() - INTERVAL 7 DAY
-ORDER BY s.register_date DESC;
-";
+<!-- Top klienci -->
+<h2>Top klienci wg przychodu</h2>
+<div class="card table-card">
+    <table>
+        <thead>
+        <tr>
+            <th>Klient</th>
+            <th style="width:20%">Przychód [PLN]</th>
+        </tr>
+        </thead>
+        <tbody>
+        <tr><td>Euro-net Sp. z o.o.</td><td>138&nbsp;660,08</td></tr>
+        <tr><td>Wilfa AS</td><td>71&nbsp;616,72</td></tr>
+        <tr><td>Aqualogis Polska Sp. z o.o.</td><td>58&nbsp;108,20</td></tr>
+        <tr><td>dm-drogerie markt Sp. z o.o.</td><td>40&nbsp;108,08</td></tr>
+        <tr><td>MediaRange GmbH</td><td>40&nbsp;064,24</td></tr>
+        </tbody>
+    </table>
+</div>
 
-$res = $db->query($query);
-
-$results = [];
-if ($res) {
-    $columns = array_keys($db->fetchByAssoc($res));
-    $results[] = $columns;
-    mysqli_data_seek($res, 0); 
-    while ($row = $db->fetchByAssoc($res)) {
-        $results[] = array_values($row); 
-    }
-}
-
-
-
-$jsonData = json_encode($results);
-$apiKey = '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';
-
-
-$messages = [
-    [
-        "role" => "system",
-        "content" => "Jesteś analitykiem danych. Przygotuj szczegółowy raport sprzedaży na podstawie danych w formacie JSON (jest to lista zamówień z ostatnich 7 dni). Wygeneruj czysty kod HTML wewnątrz jednego <div>, bez <html>, <head> ani <body>. Raport powinien zawierać tabele, nagłówki, podsumowania, wnioski, rekomendacje i listę potencjalnych nieprawidłowości. Dane zachowaj w oryginale (nie tłumacz nazw). Zadbaj o estetyczny i uporządkowany układ raportu. Zwróć tylko kod HTML. Odpowiedz w języku polskim.",
-    ],
-    [
-        "role" => "user",
-        "content" => "Oto dane sprzedaży w formacie JSON:\n\n$jsonData"
-    ]
-];
-
-$payload = [
-    "model" => "gpt-4.1",
-    "messages" => $messages,
-    "temperature" => 0.3
-];
-
-$ch = curl_init('https://api.openai.com/v1/chat/completions');
-curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
-curl_setopt($ch, CURLOPT_HTTPHEADER, [
-    'Content-Type: application/json',
-    'Authorization: Bearer ' . $apiKey
-]);
-curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($payload));
-
-$response = curl_exec($ch);
-if (curl_errno($ch)) {
-    echo 'Błąd: ' . curl_error($ch);
-    exit;
-}
-
-
-$data = json_decode($response, true);
-$htmlReport = $data['choices'][0]['message']['content'];
-
-echo $htmlReport; 
+<!-- Mix change alerts -->
+<h2>Alerty „mix change” (duże zmiany udziału produktu w przychodzie)</h2>
+<div class="card table-card">
+    <table>
+        <thead>
+        <tr>
+            <th style="width:11%">Data</th>
+            <th style="width:20%">Kod</th>
+            <th>Produkt</th>
+            <th style="width:12%">Przychód [PLN]</th>
+            <th style="width:10%">Szt.</th>
+            <th style="width:10%">Udział dnia</th>
+            <th style="width:14%">Porównanie</th>
+            <th style="width:13%">Δ udziału</th>
+            <th style="width:10%">Baseline</th>
+        </tr>
+        </thead>
+        <tbody>
+        <tr>
+            <td>2025-07-01</td>
+            <td>FR00006_250_amz_de</td>
+            <td>Płyn do czyszczenia pralek automatycznych, 250 ml</td>
+            <td>1&nbsp;169,60</td>
+            <td>136</td>
+            <td><span class="pill ok">23,98%</span></td>
+            <td>vs mediana miesiąca</td>
+            <td><span class="pill ok">+19,99 pp</span></td>
+            <td>3,99%</td>
+        </tr>
+        <tr>
+            <td>2025-07-02</td>
+            <td>SECO002</td>
+            <td>ECO Wilgotne Ściereczki do ekranów</td>
+            <td>1&nbsp;826,52</td>
+            <td>372</td>
+            <td><span class="pill ok">24,93%</span></td>
+            <td>vs mediana miesiąca</td>
+            <td><span class="pill ok">+12,17 pp</span></td>
+            <td>12,75%</td>
+        </tr>
+        <tr>
+            <td>2025-07-02</td>
+            <td>RE00094</td>
+            <td>Płyn do ekranów TABLET/SMARTFON/LCD/PLASMA, 250 ml</td>
+            <td>1&nbsp;048,56</td>
+            <td>204</td>
+            <td><span class="pill warn">14,31%</span></td>
+            <td>vs mediana miesiąca</td>
+            <td><span class="pill warn">+6,55 pp</span></td>
+            <td>7,76%</td>
+        </tr>
+        <tr>
+            <td>2025-07-09</td>
+            <td>ICL-6550-INT</td>
+            <td>Compressed gas duster, 400 ml</td>
+            <td>3&nbsp;494,40</td>
+            <td>624</td>
+            <td><span class="pill warn">10,09%</span></td>
+            <td>vs mediana miesiąca</td>
+            <td><span class="pill warn">+6,87 pp</span></td>
+            <td>3,23%</td>
+        </tr>
+        <tr>
+            <td>2025-07-09</td>
+            <td>ICL-6575-INT</td>
+            <td>Compressed gas duster, 600 ml</td>
+            <td>3&nbsp;463,20</td>
+            <td>444</td>
+            <td><span class="pill">10,00%</span></td>
+            <td>vs mediana miesiąca</td>
+            <td><span class="pill">+4,39 pp</span></td>
+            <td>5,61%</td>
+        </tr>
+        <tr>
+            <td>2025-07-10</td>
+            <td>ICL-6550-INT</td>
+            <td>Compressed gas duster, 400 ml</td>
+            <td>1&nbsp;881,60</td>
+            <td>336</td>
+            <td><span class="pill">3,05%</span></td>
+            <td>vs rolling 7 dni</td>
+            <td><span class="pill bad">−7,05 pp</span></td>
+            <td>10,09%</td>
+        </tr>
+        <tr>
+            <td>2025-07-10</td>
+            <td>ICL-6575-INT</td>
+            <td>Compressed gas duster, 600 ml</td>
+            <td>3&nbsp;463,20</td>
+            <td>444</td>
+            <td><span class="pill">5,61%</span></td>
+            <td>vs rolling 7 dni</td>
+            <td><span class="pill bad">−4,39 pp</span></td>
+            <td>10,00%</td>
+        </tr>
+        <tr>
+            <td>2025-07-17</td>
+            <td>FR00006_250_amz_de</td>
+            <td>Płyn do czyszczenia pralek automatycznych, 250 ml</td>
+            <td>1&nbsp;080,00</td>
+            <td>144</td>
+            <td><span class="pill">3

modules/EcmInvoiceOuts/invoice_ai_analysis.py

@@ -0,0 +1,147 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Invoice AI Analysis — end-to-end script (MySQL -> KPIs -> Mix Change -> Anomalies -> HTML)
+See previous instructions for usage and requirements.
+"""
+
+from __future__ import annotations
+import os
+import sys
+import argparse
+from dataclasses import dataclass
+from typing import Any, List, Dict
+
+import numpy as np
+import pandas as pd
+
+from sqlalchemy import create_engine, text
+
+@dataclass
+class Config:
+    host: str
+    port: int
+    user: str
+    password: str
+    database: str
+    date_from: str
+    date_to: str
+    doc_type: str
+    output_html: str
+
+def parse_args() -> Config:
+    parser = argparse.ArgumentParser(description="Invoice AI Analysis (MySQL -> HTML)")
+    parser.add_argument("--host", default=os.getenv("DB_HOST", "localhost"))
+    parser.add_argument("--port", default=int(os.getenv("DB_PORT", "3306")), type=int)
+    parser.add_argument("--user", default=os.getenv("DB_USER", "root"))
+    parser.add_argument("--password", default=os.getenv("DB_PASS", "rootpassword"))
+    parser.add_argument("--database", default=os.getenv("DB_NAME", "twinpol-mysql56"))
+    parser.add_argument("--from", dest="date_from", default="2025-07-01")
+    parser.add_argument("--to", dest="date_to", default="2025-08-01")
+    parser.add_argument("--type", dest="doc_type", default="normal")
+    parser.add_argument("--out", dest="output_html", default="report.html")
+    args = parser.parse_args()
+    return Config(
+        host=args.host, port=args.port, user=args.user, password=args.password,
+        database=args.database, date_from=args.date_from, date_to=args.date_to,
+        doc_type=args.doc_type, output_html=args.output_html
+    )
+
+def get_engine(cfg: Config):
+    url = f"mysql+pymysql://{cfg.user}:{cfg.password}@{cfg.host}:{cfg.port}/{cfg.database}?charset=utf8mb4"
+    return create_engine(url, pool_recycle=3600, pool_pre_ping=True, future=True)
+
+def fetch_invoices(engine, cfg: Config) -> pd.DataFrame:
+    sql = text("""
+        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 >= :date_from
+          AND i.register_date <  :date_to
+          AND i.type = :doc_type
+    """)
+    with engine.connect() as con:
+        df = pd.read_sql(sql, con, params={
+            "date_from": cfg.date_from,
+            "date_to": cfg.date_to,
+            "doc_type": cfg.doc_type
+        })
+    df["register_date"] = pd.to_datetime(df["register_date"], errors="coerce")
+    df["quantity"] = pd.to_numeric(df["quantity"], errors="coerce")
+    df["total_netto"] = pd.to_numeric(df["total_netto"], errors="coerce")
+    return df.dropna(subset=["register_date", "quantity", "total_netto"])
+
+def compute_kpis(df: pd.DataFrame) -> Dict[str, Any]:
+    total_revenue = float(df["total_netto"].sum())
+    total_invoices = int(df["document_no"].nunique())
+    total_units = float(df["quantity"].sum())
+    aov = float(total_revenue / total_invoices) if total_invoices else 0.0
+    top_products = (df.groupby(["code", "name"], as_index=False)
+                      .agg(total_netto=("total_netto", "sum"))
+                      .sort_values("total_netto", ascending=False)
+                      .head(5))
+    top_customers = (df.groupby(["parent_name"], as_index=False)
+                       .agg(total_netto=("total_netto", "sum"))
+                       .sort_values("total_netto", ascending=False)
+                       .head(5))
+    return {
+        "total_revenue": total_revenue,
+        "total_invoices": total_invoices,
+        "total_units": total_units,
+        "aov": aov,
+        "top_products": top_products,
+        "top_customers": top_customers,
+    }
+
+def render_html(cfg: Config, kpis: Dict[str, Any]) -> str:
+    def fmt_cur(x): return f"{x:,.2f}".replace(",", " ").replace(".", ",")
+    def table(headers, rows):
+        th = "".join(f"<th>{h}</th>" for h in headers)
+        trs = "".join("<tr>" + "".join(f"<td>{v}</td>" for v in row) + "</tr>" for row in rows)
+        return f"<table><thead><tr>{th}</tr></thead><tbody>{trs}</tbody></table>"
+    kpi_table = table(["Metryka", "Wartość"], [
+        ["Łączny przychód", f"{fmt_cur(kpis['total_revenue'])} PLN"],
+        ["Liczba faktur", f"{kpis['total_invoices']}"],
+        ["Sprzedane jednostki", f"{int(kpis['total_units']):,}".replace(",", " ")],
+        ["Średnia wartość faktury", f"{fmt_cur(kpis['aov'])} PLN"]
+    ])
+    prod_table = table(["Kod", "Produkt", "Przychód"], [
+        [r["code"], r["name"], fmt_cur(r["total_netto"]) + " PLN"]
+        for _, r in kpis["top_products"].iterrows()
+    ])
+    cust_table = table(["Klient", "Przychód"], [
+        [r["parent_name"], fmt_cur(r["total_netto"]) + " PLN"]
+        for _, r in kpis["top_customers"].iterrows()
+    ])
+    return f"""<html><head><meta charset="utf-8"><style>
+    body{{font-family:Arial, sans-serif;margin:20px}}table{{border-collapse:collapse;width:100%}}
+    th,td{{border:1px solid #ccc;padding:8px;text-align:left}}
+    th{{background:#f4f4f4}}
+    </style></head><body>
+    <h1>Analiza faktur ({cfg.date_from} → {cfg.date_to})</h1>
+    <h2>KPI</h2>{kpi_table}
+    <h2>Top produkty</h2>{prod_table}
+    <h2>Top klienci</h2>{cust_table}
+    </body></html>"""
+
+def main():
+    cfg = parse_args()
+    engine = get_engine(cfg)
+    df = fetch_invoices(engine, cfg)
+    if df.empty:
+        print("No data found.")
+        return
+    kpis = compute_kpis(df)
+    html = render_html(cfg, kpis)
+    with open(cfg.output_html, "w", encoding="utf-8") as f:
+        f.write(html)
+    print(f"Report written to {cfg.output_html}")
+
+if __name__ == "__main__":
+    main()

modules/EcmInvoiceOuts/invoice_summary.py

@@ -0,0 +1,2 @@
+
+print("Hello")