sprzadaz analiza AI

2025-08-22 15:56:47 +02:00
parent ed05cdeb75
commit d1ff1fa0ac
6 changed files with 484 additions and 63 deletions
--- a/modules/EcmInvoiceOuts/ai/worker.py
+++ b/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()