BeyondCXAnalytics-Demo/backend/beyond_api/api/analysis.py

from __future__ import annotations

import os
from pathlib import Path
import json
import math
from uuid import uuid4
from typing import Optional, Any, Literal

from fastapi import APIRouter, UploadFile, File, Form, HTTPException, Depends, Header
from fastapi.responses import JSONResponse

from beyond_api.security import get_current_user
from beyond_api.services.analysis_service import run_analysis_collect_json
from beyond_api.i18n import t, get_language_from_header

# Cache paths - same as in cache.py
CACHE_DIR = Path(os.getenv("CACHE_DIR", "/data/cache"))
CACHED_FILE = CACHE_DIR / "cached_data.csv"

router = APIRouter(
    prefix="",
    tags=["analysis"],
)


def sanitize_for_json(obj: Any) -> Any:
    """
    Recorre un objeto (dict/list/escalares) y convierte:
    - NaN, +inf, -inf -> None
    para que sea JSON-compliant.
    """
    if isinstance(obj, float):
        if math.isnan(obj) or math.isinf(obj):
            return None
        return obj

    if obj is None or isinstance(obj, (str, int, bool)):
        return obj

    if isinstance(obj, dict):
        return {k: sanitize_for_json(v) for k, v in obj.items()}

    if isinstance(obj, (list, tuple)):
        return [sanitize_for_json(v) for v in obj]

    return str(obj)


@router.post("/analysis")
async def analysis_endpoint(
    csv_file: UploadFile = File(...),
    economy_json: Optional[str] = Form(default=None),
    analysis: Literal["basic", "premium"] = Form(default="premium"),
    current_user: str = Depends(get_current_user),
    accept_language: Optional[str] = Header(None),
):
    """
    Ejecuta el pipeline sobre un CSV subido (multipart/form-data) y devuelve
    ÚNICAMENTE un JSON con todos los resultados (incluyendo agentic_readiness).

    Parámetro `analysis`:
    - "basic":   usa una configuración reducida (p.ej. configs/basic.json)
    - "premium": usa la configuración completa por defecto
                 (p.ej. beyond_metrics_config.json), sin romper lo existente.
    """
    lang = get_language_from_header(accept_language)

    # Validar `analysis` (por si llega algo raro)
    if analysis not in {"basic", "premium"}:
        raise HTTPException(
            status_code=400,
            detail=t("analysis.invalid_type", lang),
        )

    # 1) Parseo de economía (si viene)
    economy_data = None
    if economy_json:
        try:
            economy_data = json.loads(economy_json)
        except json.JSONDecodeError:
            raise HTTPException(
                status_code=400,
                detail=t("analysis.invalid_economy_json", lang),
            )

    # 2) Guardar el CSV subido en una carpeta de trabajo
    base_input_dir = Path("data/input")
    base_input_dir.mkdir(parents=True, exist_ok=True)

    original_name = csv_file.filename or f"input_{uuid4().hex}.csv"
    safe_name = Path(original_name).name  # evita rutas con ../
    input_path = base_input_dir / safe_name

    with input_path.open("wb") as f:
        while True:
            chunk = await csv_file.read(1024 * 1024)  # 1 MB
            if not chunk:
                break
            f.write(chunk)

    try:
        # 3) Ejecutar el análisis y obtener el JSON en memoria
        results_json = run_analysis_collect_json(
            input_path=input_path,
            economy_data=economy_data,
            analysis=analysis,      # "basic" o "premium"
            company_folder=None,
        )
    finally:
        # 3b) Limpiar el CSV temporal
        try:
            input_path.unlink(missing_ok=True)
        except Exception:
            # No queremos romper la respuesta si falla el borrado
            pass

    # 4) Limpiar NaN/inf para que el JSON sea válido
    safe_results = sanitize_for_json(results_json)

    # 5) Devolver SOLO JSON
    return JSONResponse(
        content={
            "user": current_user,
            "results": safe_results,
        }
    )


def extract_date_range_from_csv(file_path: Path) -> dict:
    """Extrae el rango de fechas del CSV."""
    import pandas as pd
    try:
        # Leer solo la columna de fecha para eficiencia
        df = pd.read_csv(file_path, usecols=['datetime_start'], parse_dates=['datetime_start'])
        if 'datetime_start' in df.columns and len(df) > 0:
            min_date = df['datetime_start'].min()
            max_date = df['datetime_start'].max()
            return {
                "min": min_date.strftime('%Y-%m-%d') if pd.notna(min_date) else None,
                "max": max_date.strftime('%Y-%m-%d') if pd.notna(max_date) else None,
            }
    except Exception as e:
        print(f"Error extracting date range: {e}")
    return {"min": None, "max": None}


def count_unique_queues_from_csv(file_path: Path) -> int:
    """Cuenta las colas únicas en el CSV."""
    import pandas as pd
    try:
        df = pd.read_csv(file_path, usecols=['queue_skill'])
        if 'queue_skill' in df.columns:
            return df['queue_skill'].nunique()
    except Exception as e:
        print(f"Error counting queues: {e}")
    return 0


@router.post("/analysis/cached")
async def analysis_cached_endpoint(
    economy_json: Optional[str] = Form(default=None),
    analysis: Literal["basic", "premium"] = Form(default="premium"),
    current_user: str = Depends(get_current_user),
    accept_language: Optional[str] = Header(None),
):
    """
    Ejecuta el pipeline sobre el archivo CSV cacheado en el servidor.
    Útil para re-analizar sin tener que subir el archivo de nuevo.
    """
    lang = get_language_from_header(accept_language)

    # Validar que existe el archivo cacheado
    if not CACHED_FILE.exists():
        raise HTTPException(
            status_code=404,
            detail=t("analysis.no_cached_file", lang),
        )

    # Validar `analysis`
    if analysis not in {"basic", "premium"}:
        raise HTTPException(
            status_code=400,
            detail=t("analysis.invalid_type", lang),
        )

    # Parseo de economía (si viene)
    economy_data = None
    if economy_json:
        try:
            economy_data = json.loads(economy_json)
        except json.JSONDecodeError:
            raise HTTPException(
                status_code=400,
                detail=t("analysis.invalid_economy_json", lang),
            )

    # Extraer metadatos del CSV
    date_range = extract_date_range_from_csv(CACHED_FILE)
    unique_queues = count_unique_queues_from_csv(CACHED_FILE)

    try:
        # Ejecutar el análisis sobre el archivo cacheado
        results_json = run_analysis_collect_json(
            input_path=CACHED_FILE,
            economy_data=economy_data,
            analysis=analysis,
            company_folder=None,
        )
    except Exception as e:
        raise HTTPException(
            status_code=500,
            detail=t("analysis.execution_error", lang, error=str(e)),
        )

    # Limpiar NaN/inf para que el JSON sea válido
    safe_results = sanitize_for_json(results_json)

    return JSONResponse(
        content={
            "user": current_user,
            "results": safe_results,
            "source": "cached",
            "dateRange": date_range,
            "uniqueQueues": unique_queues,
        }
    )