Update app.py

app.py

@@ -77,12 +77,8 @@ def main():
         st.plotly_chart(fig2)
 
         st.subheader('Performance Metrics')
-       st.write("""
-        The metrics below provide a quantitative measure of the model’s accuracy:
-        - **Mean Absolute Error (MAE)**: A lower value indicates better performance.
-        - **Mean Squared Error (MSE)**: A lower value indicates better performance, and it penalizes larger errors more than MAE.
-        - **Root Mean Squared Error (RMSE)**: A lower value indicates better performance, similar to MSE, but in the same units as the target variable.
-        """)
+       st.write('The metrics below provide a quantitative measure of the model’s accuracy: - **Mean Absolute Error (MAE)**: A lower value indicates better performance. - **Mean Squared Error (MSE)**: A lower value indicates better performance, and it penalizes larger errors more than MAE.- **Root Mean Squared Error (RMSE)**: A lower value indicates better performance, similar to MSE, but in the same units as the target variable.
+        ')
         actual = df['y']
         predicted = forecast['yhat'][:len(df)]
         metrics = calculate_performance_metrics(actual, predicted)