farentsen
a062b45c51
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>
85 lines
2.7 KiB
Python
85 lines
2.7 KiB
Python
import json
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from pathlib import Path
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import pandas as pd
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from fastapi import APIRouter, HTTPException
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router = APIRouter(prefix="/api", tags=["benefits"])
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_DATA_DIR = Path(__file__).parent / "data"
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with (_DATA_DIR / "member_insights.json").open() as f:
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_MEMBER_INSIGHTS = json.load(f)
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_SCHEDULE_TEXT = (_DATA_DIR / "schedule_response.txt").read_text(encoding="utf-8").strip()
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_HISTORICAL = pd.read_csv(_DATA_DIR / "historical_procedures.csv")
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_AVAILABLE = pd.read_csv(_DATA_DIR / "available_procedures.csv")
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_OPS = {
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"equals": lambda s, v: s == v,
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"ne": lambda s, v: s != v,
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"contains": lambda s, v: s.astype(str).str.contains(str(v), case=False, na=False),
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"gt": lambda s, v: s > v,
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"lt": lambda s, v: s < v,
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"ge": lambda s, v: s >= v,
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"le": lambda s, v: s <= v,
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}
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def _apply_filters_and_group(df: pd.DataFrame, filters_raw, group_by_raw) -> list[dict]:
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try:
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filters = json.loads(filters_raw) if isinstance(filters_raw, str) else (filters_raw or [])
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group_by = json.loads(group_by_raw) if isinstance(group_by_raw, str) else (group_by_raw or [])
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except json.JSONDecodeError as exc:
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raise HTTPException(status_code=400, detail=f"Invalid JSON in filters or group_by: {exc}")
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result = df.copy()
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for f in filters:
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col, op, val = f.get("column"), f.get("operator"), f.get("value")
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if col not in result.columns:
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raise HTTPException(status_code=400, detail=f"Unknown column: {col}")
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if op not in _OPS:
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raise HTTPException(status_code=400, detail=f"Unsupported operator: {op}")
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result = result[_OPS[op](result[col], val)]
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if group_by:
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missing = [c for c in group_by if c not in result.columns]
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if missing:
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raise HTTPException(status_code=400, detail=f"Unknown group_by columns: {missing}")
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numeric_cols = result.select_dtypes(include="number").columns.tolist()
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result = result.groupby(group_by)[numeric_cols].mean().reset_index()
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return result.to_dict(orient="records")
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@router.get("/member-insights")
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def member_insights():
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return _MEMBER_INSIGHTS
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@router.get("/schedule")
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def schedule():
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return {"result": _SCHEDULE_TEXT}
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@router.post("/historical-procedures")
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def historical_procedures(payload: dict):
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rows = _apply_filters_and_group(
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_HISTORICAL,
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payload.get("filters", "[]"),
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payload.get("group_by", "[]"),
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)
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return {"result": rows}
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@router.post("/available-procedures")
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def available_procedures(payload: dict):
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rows = _apply_filters_and_group(
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_AVAILABLE,
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payload.get("filters", "[]"),
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payload.get("group_by", "[]"),
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)
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return {"result": rows}
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