feat: initial implementation taller-wox.fitlabs.dev

Portal FastAPI + 5 endpoints REST para Bootcamp Agentic AI con
watsonx Orchestrate (FactorIT). Single container, Coolify-ready.

- Landing brandeado FIT con formulario de registro (honeypot anti-bot)
- Tokens itsdangerous para descargas (24h expiry)
- 5 endpoints API: historical/available procedures, member-insights,
  schedule, generate-report (Jinja2 + Plotly)
- SQLite con upsert-on-email para leads + log de descargas
- Admin endpoints (HTTP Basic): leads.json, leads.csv, stats
- 23 tests pytest pasando
- Dockerfile listo para Coolify con volúmenes persistentes
  (/app/leads.db, /app/app/data/reports_output, /app/material)

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

app/benefits_api.py

@@ -0,0 +1,84 @@
+import json
+from pathlib import Path
+
+import pandas as pd
+from fastapi import APIRouter, HTTPException
+
+router = APIRouter(prefix="/api", tags=["benefits"])
+
+_DATA_DIR = Path(__file__).parent / "data"
+
+with (_DATA_DIR / "member_insights.json").open() as f:
+    _MEMBER_INSIGHTS = json.load(f)
+
+_SCHEDULE_TEXT = (_DATA_DIR / "schedule_response.txt").read_text(encoding="utf-8").strip()
+
+_HISTORICAL = pd.read_csv(_DATA_DIR / "historical_procedures.csv")
+_AVAILABLE = pd.read_csv(_DATA_DIR / "available_procedures.csv")
+
+
+_OPS = {
+    "equals": lambda s, v: s == v,
+    "ne": lambda s, v: s != v,
+    "contains": lambda s, v: s.astype(str).str.contains(str(v), case=False, na=False),
+    "gt": lambda s, v: s > v,
+    "lt": lambda s, v: s < v,
+    "ge": lambda s, v: s >= v,
+    "le": lambda s, v: s <= v,
+}
+
+
+def _apply_filters_and_group(df: pd.DataFrame, filters_raw, group_by_raw) -> list[dict]:
+    try:
+        filters = json.loads(filters_raw) if isinstance(filters_raw, str) else (filters_raw or [])
+        group_by = json.loads(group_by_raw) if isinstance(group_by_raw, str) else (group_by_raw or [])
+    except json.JSONDecodeError as exc:
+        raise HTTPException(status_code=400, detail=f"Invalid JSON in filters or group_by: {exc}")
+
+    result = df.copy()
+    for f in filters:
+        col, op, val = f.get("column"), f.get("operator"), f.get("value")
+        if col not in result.columns:
+            raise HTTPException(status_code=400, detail=f"Unknown column: {col}")
+        if op not in _OPS:
+            raise HTTPException(status_code=400, detail=f"Unsupported operator: {op}")
+        result = result[_OPS[op](result[col], val)]
+
+    if group_by:
+        missing = [c for c in group_by if c not in result.columns]
+        if missing:
+            raise HTTPException(status_code=400, detail=f"Unknown group_by columns: {missing}")
+        numeric_cols = result.select_dtypes(include="number").columns.tolist()
+        result = result.groupby(group_by)[numeric_cols].mean().reset_index()
+
+    return result.to_dict(orient="records")
+
+
+@router.get("/member-insights")
+def member_insights():
+    return _MEMBER_INSIGHTS
+
+
+@router.get("/schedule")
+def schedule():
+    return {"result": _SCHEDULE_TEXT}
+
+
+@router.post("/historical-procedures")
+def historical_procedures(payload: dict):
+    rows = _apply_filters_and_group(
+        _HISTORICAL,
+        payload.get("filters", "[]"),
+        payload.get("group_by", "[]"),
+    )
+    return {"result": rows}
+
+
+@router.post("/available-procedures")
+def available_procedures(payload: dict):
+    rows = _apply_filters_and_group(
+        _AVAILABLE,
+        payload.get("filters", "[]"),
+        payload.get("group_by", "[]"),
+    )
+    return {"result": rows}