Update app.py

app.py

@@ -57,20 +57,12 @@ else:
     temp_df = df[(df['Date'] >= pd.Timestamp(start_date))] #& (df['Date'] <= pd.Timestamp(end_date))]
     fig = go.Figure()
 
-    # Updated labels for each variable
-    variable_labels = {
-        'Price': 'Real Price',
-        'DNN': 'DNN Forecast',
-        'LEAR': 'LEAR Forecast',
-        'Persis': 'Persistence Forecast'
-    }
-
     for variable in selected_variables:
         fig.add_trace(go.Scatter(x=temp_df['Date'], y=temp_df[variable], mode='lines', name=variable))
 
     fig.update_layout(xaxis_title="Date", yaxis_title="Price [EUR/MWh]")
     st.plotly_chart(fig, use_container_width=True)
-    st.write("The graph presented here illustrates the day-ahead electricity price forecasts for Belgium, covering the period from one week ago up to tomorrow. It incorporates predictions from three distinct models: DNN (Deep Neural Networks), LEAR (Lasso Estimated AutoRegressive), and Persistence, alongside the actual electricity prices up until today.")
+    st.write("The graph presented here illustrates the day-ahead electricity price forecasts for Belgium, covering the period from one week ago up to tomorrow. It incorporates predictions from three distinct models: a Neural Network, a Regularized Linear Model, and Persistence, alongside the actual electricity prices up until today.")
 
 
 if not selected_variables: