Update app.py

app.py

@@ -46,13 +46,13 @@ model = LSTM(INPUT_SIZE, HIDDEN_LAYER_SIZE, OUTPUT_SIZE)
 
 model.load_state_dict(torch.load(model_path))
 
-model.eval()
 
 def predict_and_plot(week4, week3, week2, week1):
     last_four_weeks = [week4, week3, week2, week1]
 
     custom_input = prepare_custom_input(last_four_weeks, seq_length, scaler)
 
+    model.eval()
     with torch.no_grad():
         model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),
                              torch.zeros(1, 1, model.hidden_layer_size))
@@ -63,7 +63,7 @@ def predict_and_plot(week4, week3, week2, week1):
     predicted_value = f"{predicted_kilometers[0][0]:.2f}"
 
     weeks = ['Week -4', 'Week -3', 'Week -2', 'Week -1', 'Predicted Week']
-    actual_values = [week4, week3, week2, week1, predicted_value]
+    actual_values = last_four_weeks + [predicted_value]
     
     plt.figure(figsize=(10, 6))
     plt.plot(weeks, actual_values, marker='o', label='Total Kilometers')
@@ -72,6 +72,7 @@ def predict_and_plot(week4, week3, week2, week1):
     plt.xlabel('Weeks')
     plt.ylabel('Total Kilometers')
     plt.grid()
+    plt.tight_layout()
     plt.legend()
 
     # Saving the plot to a BytesIO object