Update inference.py

inference.py

@@ -2,7 +2,6 @@ import pandas as pd
 import numpy as np
 from statsmodels.tsa.arima.model import ARIMA
 from sklearn.preprocessing import MinMaxScaler
-from sklearn.metrics import mean_squared_error
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import LSTM, Dense
 
@@ -56,12 +55,15 @@ def lstm_forecast(ts_data, look_back=60, steps=5, epochs=20):
     model.fit(X, y, epochs=epochs, batch_size=32, verbose=0)
 
     # Forecast future steps
-    last_seq = scaled_data[-look_back:].reshape(1, look_back,1)
+    last_seq = scaled_data[-look_back:].reshape(1, look_back, 1)
     predictions = []
+
     for _ in range(steps):
         pred = model.predict(last_seq, verbose=0)
         predictions.append(pred[0,0])
-        last_seq = np.append(last_seq[:,1:,:], [[pred]], axis=1)
+        # Fix: reshape pred to 3D and append to sequence
+        pred_reshaped = pred.reshape(1, 1, 1)
+        last_seq = np.concatenate([last_seq[:,1:,:], pred_reshaped], axis=1)
 
     predictions = scaler.inverse_transform(np.array(predictions).reshape(-1,1))
     return predictions.flatten().tolist()