Initial commit: frontend + backend integration

backend/beyond_api/__init__.py

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+# vacío o con un pequeño comentario
+"""
+Paquete de API para BeyondCX Heatmap.
+"""

backend/beyond_api/api/__init__.py

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+from .analysis import router
+
+__all__ = ["router"]

backend/beyond_api/api/analysis.py

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+from __future__ import annotations
+
+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
+from fastapi.responses import JSONResponse
+
+from beyond_api.security import get_current_user
+from beyond_api.services.analysis_service import run_analysis_collect_json
+
+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),
+):
+    """
+    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.
+    """
+
+    # Validar `analysis` (por si llega algo raro)
+    if analysis not in {"basic", "premium"}:
+        raise HTTPException(
+            status_code=400,
+            detail="analysis debe ser 'basic' o 'premium'.",
+        )
+
+    # 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="economy_json no es un JSON válido.",
+            )
+
+    # 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,
+        }
+    )

backend/beyond_api/main.py

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+import logging
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+
+# importa tus routers
+from beyond_api.api.analysis import router as analysis_router
+
+def setup_basic_logging() -> None:
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s %(levelname)s [%(name)s] %(message)s",
+    )
+
+setup_basic_logging()
+
+app = FastAPI()
+
+origins = [
+    "http://localhost:3000",
+    "http://127.0.0.1:3000",
+]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+app.include_router(analysis_router)

backend/beyond_api/security.py

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+from __future__ import annotations
+
+import os
+import secrets
+from fastapi import Depends, HTTPException, status
+from fastapi.security import HTTPBasic, HTTPBasicCredentials
+
+security = HTTPBasic()
+
+# En producción: export BASIC_AUTH_USERNAME y BASIC_AUTH_PASSWORD.
+BASIC_USER = os.getenv("BASIC_AUTH_USERNAME", "beyond")
+BASIC_PASS = os.getenv("BASIC_AUTH_PASSWORD", "beyond2026")
+
+
+def get_current_user(credentials: HTTPBasicCredentials = Depends(security)) -> str:
+    """
+    Valida el usuario/contraseña vía HTTP Basic.
+    """
+    correct_username = secrets.compare_digest(credentials.username, BASIC_USER)
+    correct_password = secrets.compare_digest(credentials.password, BASIC_PASS)
+
+    if not (correct_username and correct_password):
+        # Importante devolver el header WWW-Authenticate para que el navegador saque el prompt
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Credenciales incorrectas",
+            headers={"WWW-Authenticate": "Basic"},
+        )
+
+    return credentials.username

backend/beyond_api/services/analysis_service.py

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+from __future__ import annotations
+
+from pathlib import Path
+from uuid import uuid4
+from datetime import datetime
+from typing import Optional, Literal
+import json
+import zipfile
+
+from beyond_metrics.io import LocalDataSource, LocalResultsSink, ResultsSink
+from beyond_metrics.pipeline import build_pipeline
+from beyond_metrics.dimensions.EconomyCost import EconomyConfig
+from beyond_flows.scorers import AgenticScorer
+
+from typing import Any, Mapping, Optional, Dict
+
+
+def _build_economy_config(economy_data: Optional[Mapping[str, Any]]) -> EconomyConfig:
+    """
+    Construye EconomyConfig validando tipos y evitando que el type checker
+    mezcle floats y dicts en un solo diccionario.
+    """
+
+    # Valores por defecto
+    default_customer_segments: Dict[str, str] = {
+        "VIP": "high",
+        "Premium": "high",
+        "Soporte_General": "medium",
+        "Ventas": "medium",
+        "Basico": "low",
+    }
+
+    if economy_data is None:
+        return EconomyConfig(
+            labor_cost_per_hour=20.0,
+            overhead_rate=0.10,
+            tech_costs_annual=5000.0,
+            automation_cpi=0.20,
+            automation_volume_share=0.5,
+            automation_success_rate=0.6,
+            customer_segments=default_customer_segments,
+        )
+
+    def _get_float(field: str, default: float) -> float:
+        value = economy_data.get(field, default)
+        if isinstance(value, (int, float)):
+            return float(value)
+        raise ValueError(f"El campo '{field}' debe ser numérico (float). Valor recibido: {value!r}")
+
+    # Campos escalares
+    labor_cost_per_hour = _get_float("labor_cost_per_hour", 20.0)
+    overhead_rate = _get_float("overhead_rate", 0.10)
+    tech_costs_annual = _get_float("tech_costs_annual", 5000.0)
+    automation_cpi = _get_float("automation_cpi", 0.20)
+    automation_volume_share = _get_float("automation_volume_share", 0.5)
+    automation_success_rate = _get_float("automation_success_rate", 0.6)
+
+    # customer_segments puede venir o no; si viene, validarlo
+    customer_segments: Dict[str, str] = dict(default_customer_segments)
+    if "customer_segments" in economy_data and economy_data["customer_segments"] is not None:
+        cs = economy_data["customer_segments"]
+        if not isinstance(cs, Mapping):
+            raise ValueError("customer_segments debe ser un diccionario {segment: level}")
+        for k, v in cs.items():
+            if not isinstance(v, str):
+                raise ValueError(
+                    f"El valor de customer_segments['{k}'] debe ser str. Valor recibido: {v!r}"
+                )
+            customer_segments[str(k)] = v
+
+    return EconomyConfig(
+        labor_cost_per_hour=labor_cost_per_hour,
+        overhead_rate=overhead_rate,
+        tech_costs_annual=tech_costs_annual,
+        automation_cpi=automation_cpi,
+        automation_volume_share=automation_volume_share,
+        automation_success_rate=automation_success_rate,
+        customer_segments=customer_segments,
+    )
+
+
+def run_analysis(
+    input_path: Path,
+    economy_data: Optional[dict] = None,
+    return_type: Literal["path", "zip"] = "path",
+    company_folder: Optional[str] = None,
+) -> tuple[Path, Optional[Path]]:
+    """
+    Ejecuta el pipeline sobre un CSV y devuelve:
+    - (results_dir, None) si return_type == "path"
+    - (results_dir, zip_path) si return_type == "zip"
+
+    input_path puede ser absoluto o relativo, pero los resultados
+    se escribirán SIEMPRE en la carpeta del CSV, dentro de una
+    subcarpeta con nombre = timestamp (y opcionalmente prefijada
+    por company_folder).
+    """
+
+    input_path = input_path.resolve()
+
+    if not input_path.exists():
+        raise FileNotFoundError(f"El CSV no existe: {input_path}")
+    if not input_path.is_file():
+        raise ValueError(f"La ruta no apunta a un fichero CSV: {input_path}")
+
+    # Carpeta donde está el CSV
+    csv_dir = input_path.parent
+
+    # DataSource y ResultsSink apuntan a ESA carpeta
+    datasource = LocalDataSource(base_dir=str(csv_dir))
+    sink = LocalResultsSink(base_dir=str(csv_dir))
+
+    # Config de economía
+    economy_cfg = _build_economy_config(economy_data)
+
+    dimension_params: Dict[str, Mapping[str, Any]] = {
+        "economy_costs": {
+            "config": economy_cfg,
+        }
+    }
+
+    # Callback de scoring
+    def agentic_post_run(results: Dict[str, Any], run_base: str, sink_: ResultsSink) -> None:
+        scorer = AgenticScorer()
+        try:
+            agentic = scorer.compute_and_return(results)
+        except Exception as e:
+            # No rompemos toda la ejecución si el scorer falla
+            agentic = {
+                "error": f"{type(e).__name__}: {e}",
+            }
+        sink_.write_json(f"{run_base}/agentic_readiness.json", agentic)
+
+    pipeline = build_pipeline(
+        dimensions_config_path="beyond_metrics/configs/beyond_metrics_config.json",
+        datasource=datasource,
+        sink=sink,
+        dimension_params=dimension_params,
+        post_run=[agentic_post_run],
+    )
+
+    # Timestamp de ejecución (nombre de la carpeta de resultados)
+    timestamp = datetime.utcnow().strftime("%Y%m%d-%H%M%S")
+
+    # Ruta lógica de resultados (RELATIVA al base_dir del sink)
+    if company_folder:
+        # Ej: "Cliente_X/20251208-153045"
+        run_dir_rel = f"{company_folder.rstrip('/')}/{timestamp}"
+    else:
+        # Ej: "20251208-153045"
+        run_dir_rel = timestamp
+
+    # Ejecutar pipeline: el CSV se pasa relativo a csv_dir
+    pipeline.run(
+        input_path=input_path.name,
+        run_dir=run_dir_rel,
+    )
+
+    # Carpeta real con los resultados
+    results_dir = csv_dir / run_dir_rel
+
+    if return_type == "path":
+        return results_dir, None
+
+    # --- ZIP de resultados -------------------------------------------------
+    # Creamos el ZIP en la MISMA carpeta del CSV, con nombre basado en run_dir
+    zip_name = f"{run_dir_rel.replace('/', '_')}.zip"
+    zip_path = csv_dir / zip_name
+
+    with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as zipf:
+        for file in results_dir.rglob("*"):
+            if file.is_file():
+                # Lo guardamos relativo a la carpeta de resultados
+                arcname = file.relative_to(results_dir.parent)
+                zipf.write(file, arcname)
+
+    return results_dir, zip_path
+
+
+from typing import Any, Mapping, Dict  # asegúrate de tener estos imports arriba
+
+
+def run_analysis_collect_json(
+    input_path: Path,
+    economy_data: Optional[dict] = None,
+    analysis: Literal["basic", "premium"] = "premium",
+    company_folder: Optional[str] = None,
+) -> Dict[str, Any]:
+    """
+    Ejecuta el pipeline y devuelve un único JSON con todos los resultados.
+
+    A diferencia de run_analysis:
+    - NO escribe results.json
+    - NO escribe agentic_readiness.json
+    - agentic_readiness se incrusta en el dict de resultados
+
+    El parámetro `analysis` permite elegir el nivel de análisis:
+    - "basic" -> beyond_metrics/configs/basic.json
+    - "premium"  -> beyond_metrics/configs/beyond_metrics_config.json
+    """
+
+    # Normalizamos y validamos la ruta del CSV
+    input_path = input_path.resolve()
+    if not input_path.exists():
+        raise FileNotFoundError(f"El CSV no existe: {input_path}")
+    if not input_path.is_file():
+        raise ValueError(f"La ruta no apunta a un fichero CSV: {input_path}")
+
+    # Carpeta donde está el CSV
+    csv_dir = input_path.parent
+
+    # DataSource y ResultsSink apuntan a ESA carpeta
+    datasource = LocalDataSource(base_dir=str(csv_dir))
+    sink = LocalResultsSink(base_dir=str(csv_dir))
+
+    # Config de economía
+    economy_cfg = _build_economy_config(economy_data)
+
+    dimension_params: Dict[str, Mapping[str, Any]] = {
+        "economy_costs": {
+            "config": economy_cfg,
+        }
+    }
+
+    # Elegimos el fichero de configuración de dimensiones según `analysis`
+    if analysis == "basic":
+        dimensions_config_path = "beyond_metrics/configs/basic.json"
+    else: 
+        dimensions_config_path = "beyond_metrics/configs/beyond_metrics_config.json"
+
+    # Callback post-run: añadir agentic_readiness al JSON final (sin escribir ficheros)
+    def agentic_post_run(results: Dict[str, Any], run_base: str, sink_: ResultsSink) -> None:
+        scorer = AgenticScorer()
+        try:
+            agentic = scorer.compute_and_return(results)
+        except Exception as e:
+            agentic = {"error": f"{type(e).__name__}: {e}"}
+        results["agentic_readiness"] = agentic
+
+    pipeline = build_pipeline(
+        dimensions_config_path=dimensions_config_path,
+        datasource=datasource,
+        sink=sink,
+        dimension_params=dimension_params,
+        post_run=[agentic_post_run],
+    )
+
+    # Timestamp de ejecución (para separar posibles artefactos como plots)
+    timestamp = datetime.utcnow().strftime("%Y%m%d-%H%M%S")
+    if company_folder:
+        run_dir_rel = f"{company_folder.rstrip('/')}/{timestamp}"
+    else:
+        run_dir_rel = timestamp
+
+    # Ejecutar pipeline sin escribir results.json
+    results = pipeline.run(
+        input_path=input_path.name,
+        run_dir=run_dir_rel,
+        write_results_json=False,
+    )
+
+    return results