120 lines
3.5 KiB
Python
120 lines
3.5 KiB
Python
from __future__ import annotations
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from pathlib import Path
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import json
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import math
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from uuid import uuid4
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from typing import Optional, Any, Literal
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from fastapi import APIRouter, UploadFile, File, Form, HTTPException, Depends
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from fastapi.responses import JSONResponse
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from beyond_api.security import get_current_user
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from beyond_api.services.analysis_service import run_analysis_collect_json
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router = APIRouter(
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prefix="",
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tags=["analysis"],
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)
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def sanitize_for_json(obj: Any) -> Any:
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"""
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Recorre un objeto (dict/list/escalares) y convierte:
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- NaN, +inf, -inf -> None
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para que sea JSON-compliant.
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"""
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if isinstance(obj, float):
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if math.isnan(obj) or math.isinf(obj):
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return None
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return obj
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if obj is None or isinstance(obj, (str, int, bool)):
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return obj
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if isinstance(obj, dict):
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return {k: sanitize_for_json(v) for k, v in obj.items()}
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if isinstance(obj, (list, tuple)):
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return [sanitize_for_json(v) for v in obj]
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return str(obj)
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@router.post("/analysis")
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async def analysis_endpoint(
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csv_file: UploadFile = File(...),
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economy_json: Optional[str] = Form(default=None),
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analysis: Literal["basic", "premium"] = Form(default="premium"),
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current_user: str = Depends(get_current_user),
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):
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"""
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Ejecuta el pipeline sobre un CSV subido (multipart/form-data) y devuelve
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ÚNICAMENTE un JSON con todos los resultados (incluyendo agentic_readiness).
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Parámetro `analysis`:
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- "basic": usa una configuración reducida (p.ej. configs/basic.json)
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- "premium": usa la configuración completa por defecto
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(p.ej. beyond_metrics_config.json), sin romper lo existente.
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"""
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# Validar `analysis` (por si llega algo raro)
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if analysis not in {"basic", "premium"}:
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raise HTTPException(
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status_code=400,
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detail="analysis debe ser 'basic' o 'premium'.",
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)
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# 1) Parseo de economía (si viene)
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economy_data = None
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if economy_json:
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try:
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economy_data = json.loads(economy_json)
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except json.JSONDecodeError:
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raise HTTPException(
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status_code=400,
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detail="economy_json no es un JSON válido.",
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)
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# 2) Guardar el CSV subido en una carpeta de trabajo
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base_input_dir = Path("data/input")
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base_input_dir.mkdir(parents=True, exist_ok=True)
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original_name = csv_file.filename or f"input_{uuid4().hex}.csv"
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safe_name = Path(original_name).name # evita rutas con ../
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input_path = base_input_dir / safe_name
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with input_path.open("wb") as f:
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while True:
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chunk = await csv_file.read(1024 * 1024) # 1 MB
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if not chunk:
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break
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f.write(chunk)
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try:
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# 3) Ejecutar el análisis y obtener el JSON en memoria
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results_json = run_analysis_collect_json(
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input_path=input_path,
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economy_data=economy_data,
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analysis=analysis, # "basic" o "premium"
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company_folder=None,
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)
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finally:
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# 3b) Limpiar el CSV temporal
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try:
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input_path.unlink(missing_ok=True)
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except Exception:
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# No queremos romper la respuesta si falla el borrado
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pass
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# 4) Limpiar NaN/inf para que el JSON sea válido
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safe_results = sanitize_for_json(results_json)
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# 5) Devolver SOLO JSON
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return JSONResponse(
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content={
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"user": current_user,
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"results": safe_results,
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}
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)
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