feat: new file

backend/app/routes/metrics.py

@@ -0,0 +1,56 @@
+from __future__ import annotations
+
+from datetime import datetime, timedelta
+
+import pandas as pd
+from fastapi import APIRouter, HTTPException, Query
+from sqlalchemy import text
+
+from ..db import get_engine
+from ..services.schema_discovery import discover_schema
+from ..services.timeseries import normalize_timeseries
+
+
+router = APIRouter()
+
+
+@router.get("/overview")
+def overview():
+    """
+    返回 BI 顶部卡片核心指标（尽量从现有表自动推断）。
+    """
+    engine = get_engine()
+    schema = discover_schema(engine)
+    if not schema.sales_table:
+        raise HTTPException(status_code=422, detail="未发现可用销量/订单明细表（需要至少包含 product_id + 时间 + 数量/金额）")
+
+    q = text(schema.overview_sql)
+    with engine.connect() as conn:
+        row = conn.execute(q).mappings().first()
+    return {"schema": schema.model_dump(), "metrics": dict(row) if row else {}}
+
+
+@router.get("/sales/timeseries")
+def sales_timeseries(
+    product_id: str = Query(..., min_length=1),
+    days: int = Query(30, ge=1, le=365),
+):
+    engine = get_engine()
+    schema = discover_schema(engine)
+    if not schema.sales_table:
+        raise HTTPException(status_code=422, detail="未发现可用销量/订单明细表")
+
+    since = datetime.utcnow() - timedelta(days=days)
+    q = text(schema.timeseries_sql)
+    with engine.connect() as conn:
+        df = pd.read_sql(
+            q,
+            conn,
+            params={"product_id": product_id, "since": since},
+        )
+    if df.empty:
+        return {"product_id": product_id, "points": []}
+
+    points = normalize_timeseries(df, ts_col="ds", value_cols=["units", "gmv"])
+    return {"product_id": product_id, "points": points}
+