Inicializar backend FastAPI con Dockerfile

app.py

@@ -1,5 +1,5 @@
 # ============================================================
-# 📈 app.py — Backend FastAPI para predicciones BBVA
+# 📈 app.py — Backend FastAPI para predicciones BBVA y SANTANDER
 # ============================================================
 
 from fastapi import FastAPI
@@ -14,9 +14,8 @@ import pandas as pd
 # ============================================================
 # 1️⃣ Configuración base de FastAPI
 # ============================================================
-app = FastAPI(title="Predicción BBVA API")
+app = FastAPI(title="Predicciones BBVA y SANTANDER API")
 
-# Permitir acceso desde cualquier dominio (para GitHub Pages luego)
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -26,7 +25,7 @@ app.add_middleware(
 )
 
 # ============================================================
-# 2️⃣ Definición del modelo LSTM (idéntico al entrenamiento)
+# 2️⃣ Definir ambos modelos (LSTM y GRU)
 # ============================================================
 class LSTMModel(nn.Module):
     def __init__(self, n_features, hidden_size=64):
@@ -39,91 +38,132 @@ class LSTMModel(nn.Module):
         out = self.fc(out)
         return out.squeeze()
 
+
+class GRUModel(nn.Module):
+    def __init__(self, n_features, hidden_size=64, num_layers=1):
+        super().__init__()
+        self.gru = nn.GRU(
+            input_size=n_features,
+            hidden_size=hidden_size,
+            num_layers=num_layers,
+            batch_first=True
+        )
+        self.fc = nn.Linear(hidden_size, 1)
+    def forward(self, x):
+        out, _ = self.gru(x)
+        out = out[:, -1, :]
+        out = self.fc(out)
+        return out.squeeze()
+
 # ============================================================
-# 3️⃣ Cargar el modelo y los scalers
+# 3️⃣ Cargar ambos modelos y scalers
 # ============================================================
-MODEL_PATH = "models_bbva/bbva_lstm_model.pth"
-SCALER_FULL_PATH  = "models_bbva/scaler_features_full.pkl"
-SCALER_TARGET_PATH = "models_bbva/scaler_target.pkl"
-
-n_features = 5
-hidden_size = 64
 device = torch.device("cpu")
 
-# Cargar modelo y scalers
-model = LSTMModel(n_features, hidden_size)
-model.load_state_dict(torch.load(MODEL_PATH, map_location=device))
-model.to(device)
-model.eval()
-
-scaler_features = joblib.load(SCALER_FULL_PATH)
-scaler_target = joblib.load(SCALER_TARGET_PATH)
+# --- BBVA ---
+bbva_model = LSTMModel(n_features=5, hidden_size=64)
+bbva_model.load_state_dict(torch.load("models_bbva/bbva_lstm_model.pth", map_location=device))
+bbva_model.to(device)
+bbva_model.eval()
+bbva_scaler_features = joblib.load("models_bbva/scaler_features_full.pkl")
+bbva_scaler_target = joblib.load("models_bbva/scaler_target.pkl")
+
+# --- SANTANDER ---
+santander_model = GRUModel(n_features=5, hidden_size=64)
+santander_model.load_state_dict(torch.load("models_santander/santander_gru_model.pth", map_location=device))
+santander_model.to(device)
+santander_model.eval()
+santander_scaler_features = joblib.load("models_santander/santander_scaler_features_full.pkl")
+santander_scaler_target = joblib.load("models_santander/santander_scaler_target.pkl")
 
 # ============================================================
-# 4️⃣ Modelo de entrada (JSON)
+# 4️⃣ Modelo de entrada
 # ============================================================
 class PredictRequest(BaseModel):
     n_future: int = 3
 
 # ============================================================
-# 5️⃣ Endpoint raíz de prueba
+# 5️⃣ Función genérica para predicción
 # ============================================================
-@app.get("/")
-def home():
-    return {"message": "✅ Backend BBVA activo y listo para predecir"}
-
-# ============================================================
-# 6️⃣ Endpoint de predicción
-# ============================================================
-@app.post("/predict/bbva")
-def predict_bbva(req: PredictRequest):
-    # === Cargar dataset base ===
-    PATH_BBVA = "data/bbva_pred.csv"  # opcional, puedes cambiarlo
-    try:
-        bbva = pd.read_csv(PATH_BBVA)
-    except Exception:
-        return {"error": "No se encontró el archivo de datos base (data/bbva_pred.csv)"}
-
-    bbva["Date"] = pd.to_datetime(bbva["Date"])
-    bbva = bbva.set_index("Date")
-    start_date = "2021-01-01"
-    end_date = "2025-10-31"
-    bbva = bbva.loc[(bbva.index >= start_date) & (bbva.index <= end_date)].copy()
-
-    # === Columnas del modelo ===
-    cols_to_scale = [
-        'Open', 'High', 'Low', 'Close', 'Adj Close',
-        'ma_5', 'close_lag1', 'close_lag2', 'close_lag3',
-        'Volume', 'ibex_momentum_5d'
-    ]
-    features = ['Open', 'High', 'Low', 'return_1d', 'return_3d']
-    window_size = 3
-    n_future = req.n_future
-
-    # === Escalar ===
-    bbva_scaled = bbva.copy()
-    bbva_scaled[cols_to_scale] = scaler_features.transform(bbva[cols_to_scale])
+def generar_prediccion(df, model, scaler_features, scaler_target, features, cols_to_scale, window_size, n_future):
+    # Escalar
+    df_scaled = df.copy()
+    df_scaled[cols_to_scale] = scaler_features.transform(df[cols_to_scale])
 
-    # === Última ventana ===
-    last_window = bbva_scaled[features].iloc[-window_size:].values
+    # Última ventana
+    last_window = df_scaled[features].iloc[-window_size:].values
 
-    # === Predicción iterativa ===
     preds_scaled = []
     window = last_window.copy()
     for _ in range(n_future):
-        X_input = torch.tensor(window, dtype=torch.float32).unsqueeze(0).to(device)
+        X_input = torch.tensor(window, dtype=torch.float32).unsqueeze(0)
         with torch.no_grad():
             pred_scaled = model(X_input).cpu().numpy().flatten()[0]
         preds_scaled.append(pred_scaled)
         next_day = window[-1].copy()
         window = np.vstack([window[1:], next_day])
 
-    # === Desescalar ===
+    # Desescalar
     preds_real = scaler_target.inverse_transform(np.array(preds_scaled).reshape(-1, 1)).flatten().tolist()
 
-    # === Fechas futuras ===
-    last_date = bbva.index[-1]
+    # Fechas futuras
+    last_date = df.index[-1]
     future_dates = pd.date_range(last_date + pd.Timedelta(days=1), periods=n_future, freq="B")
     fechas = [str(d.date()) for d in future_dates]
 
-    return {"fechas": fechas, "predicciones": preds_real}
+    return fechas, preds_real
+
+# ============================================================
+# 6️⃣ Endpoint raíz
+# ============================================================
+@app.get("/")
+def home():
+    return {"message": "✅ API de predicciones BBVA y Santander operativa"}
+
+# ============================================================
+# 7️⃣ Endpoint principal
+# ============================================================
+@app.post("/predict/{banco}")
+def predict(banco: str, req: PredictRequest):
+    banco = banco.lower()
+    n_future = req.n_future
+
+    # === BBVA ===
+    if banco == "bbva":
+        path = "data/bbva_pred.csv"
+        df = pd.read_csv(path)
+        df["Date"] = pd.to_datetime(df["Date"])
+        df = df.set_index("Date")
+        df = df.loc["2021-01-01":"2025-10-31"]
+
+        cols_to_scale = [
+            'Open','High','Low','Close','Adj Close',
+            'ma_5','close_lag1','close_lag2','close_lag3',
+            'Volume','ibex_momentum_5d'
+        ]
+        features = ['Open','High','Low','return_1d','return_3d']
+
+        fechas, preds = generar_prediccion(df, bbva_model, bbva_scaler_features, bbva_scaler_target, features, cols_to_scale, 3, n_future)
+        return {"banco": "BBVA", "fechas": fechas, "predicciones": preds}
+
+    # === SANTANDER ===
+    elif banco == "santander":
+        path = "data/santander_pred.csv"
+        df = pd.read_csv(path)
+        df["Date"] = pd.to_datetime(df["Date"])
+        df = df.set_index("Date")
+        df = df.loc["2021-01-01":"2025-10-31"]
+
+        cols_to_scale = [
+            'Open','High','Low','Close','Adj Close',
+            'ma_5','close_lag1','close_lag2','close_lag3',
+            'Volume','ibex_momentum_5d'
+        ]
+        features = ['Open','High','Low','close_lag1','close_lag2']
+
+        fechas, preds = generar_prediccion(df, santander_model, santander_scaler_features, santander_scaler_target, features, cols_to_scale, 3, n_future)
+        return {"banco": "Santander", "fechas": fechas, "predicciones": preds}
+
+    # === Otro banco no válido ===
+    else:
+        return {"error": "Banco no reconocido. Usa 'bbva' o 'santander'."}

models_santader/santander_gru_model.pth

@@ -0,0 +1,3 @@
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+oid sha256:9f2bb2bfe7b4da753119b7683ddd0388741bdfa196c180154b7ed32cbf1750fb
+size 57357

models_santader/santander_scaler_features_full.pkl

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+oid sha256:d11143dd74d2817061746a0c07dec85b46ffaf90da5d205b5850b2b11fa31773
+size 1487

models_santader/santander_scaler_features_train.pkl

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+oid sha256:6788dbc73e8c87885bb50c84627d7c5223bd48b311067abb6f5c516b76eee780
+size 1487

models_santader/santander_scaler_target.pkl

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+oid sha256:3029f5f5a1fd50ca84d134fc652263d0c9aa9cfa9f63e54e96c8c84716c7398f
+size 975