Update app.py

app.py

@@ -1,111 +1,111 @@
-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,
-    )
+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=["https://nasa-space-app-2025-nine.vercel.app"],
+    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,
+    )