Upload app.py

app.py

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+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from datetime import datetime
+from contextlib import asynccontextmanager
+import os
+from ExoMACModel import ExoMACModel
+from models.requests import PredictRequest
+from models.responses import PredictResponse
+from typing import Optional
+from fastapi import HTTPException
+import pandas as pd
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    model = ExoMACModel(
+        repo_id=os.getenv("EXOMAC_REPO", "ZapatoProgramming/ExoMAC-KKT"),
+        local_dir=os.getenv("EXOMAC_LOCAL_DIR", "ExoMACModel/ExoMAC-KKT"),
+        prefer_snapshot=True,         
+        always_download=False,         
+        verbose=True,
+    )
+    app.state.model = model
+    yield
+
+app = FastAPI(
+    title="NASA SpaceApp API",
+    description="API para el proyecto NASA SpaceApp 2025",
+    version="1.0.0",
+    lifespan=lifespan
+)
+
+# Configurar CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@app.get("/")
+async def root():
+    """Endpoint raíz de la API"""
+    return {
+        "message": "Bienvenido a NASA SpaceApp API",
+        "version": "1.0.0",
+        "docs": "/docs"
+    }
+
+@app.get("/health")
+async def health():
+    """Endpoint de health check"""
+    return {
+        "status": "healthy",
+        "timestamp": datetime.now().isoformat(),
+        "service": "NASA SpaceApp API"
+    }
+
+
+@app.get("/test/predict")
+async def test_predict():
+    """Endpoint de prueba para la predicción"""
+    return {
+        "status": 200,
+        "message": "SOY EL CHESTNUT",
+    }
+
+@app.post("/predict", response_model=PredictResponse)
+def predict(
+    req: PredictRequest,
+):
+    m: Optional[ExoMACModel] = getattr(app.state, "model", None)
+    if m is None:
+        raise HTTPException(503, "Model not loaded")
+
+    data = dict(req.features)
+
+    try:
+        label, probabilities = m.predict(
+            data,
+            return_proba=True,
+            compute_engineered_if_missing=True,
+        )
+    except Exception as e:
+        raise HTTPException(500, f"Prediction error")
+    
+    cols = m.feature_columns
+    recognized = [c for c in cols if c in data]
+    unknown = [k for k in data.keys() if k not in cols]
+
+    used = m._ensure_engineered_features(dict(data))
+    X = pd.DataFrame([used], dtype=float).reindex(columns=cols)
+    missing = X.columns[X.iloc[0].isna()].tolist()
+
+    # Engineered features: those added beyond the original input keys
+    engineered_only = {k: used.get(k) for k in used.keys() if k not in data}
+    # JSON-safe (convert NaN to None and numpy floats to float)
+    engineered_json = {
+        k: (None if pd.isna(v) else float(v)) if isinstance(v, (int, float)) or hasattr(v, "__float__") else None
+        for k, v in engineered_only.items()
+    }
+
+    return PredictResponse(
+        label=label,
+        probabilities=probabilities,
+        recognized=recognized,
+        unknown=unknown,
+        missing=missing,
+        feature_order=cols,
+        engineered=engineered_json,
+    )