numpy as np

app.py

@@ -64,10 +64,12 @@ def prepare_input_sequence(df, x_scaler, seq_len=90):
 def prepare_unified_input_sequence(df, x_scaler, seq_len, ticker_idx, num_tickers):
     features = df[["Open", "High", "Low", "Close", "Volume"]].values
     X_scaled = x_scaler.transform(features)
-    onehot = torch.eye(num_tickers)[ticker_idx].numpy()
+    onehot = np.eye(num_tickers)[ticker_idx]
     onehot_seq = np.repeat(onehot.reshape(1, -1), seq_len, axis=0)
     X_full = np.hstack([X_scaled[-seq_len:], onehot_seq])
-    return torch.tensor(X_full, dtype=torch.float32).unsqueeze(0).to(DEVICE)
+    X_tensor = torch.tensor(X_full, dtype=torch.float32).unsqueeze(0).to(DEVICE)
+    return X_tensor
+
 
 # ------------------------
 # Load Models