Update app.py

app.py

@@ -86,7 +86,7 @@ def determine_frequency(df):
         
     return freq
 
-def plot_forecasts(forecast_df, train_df, title):
+def plot_forecasts_matplotlib(forecast_df, train_df, title):
     fig, ax = plt.subplots(1, 1, figsize=(20, 7))
     plot_df = pd.concat([train_df, forecast_df]).set_index('ds')
     historical_col = 'y'
@@ -112,6 +112,61 @@ def plot_forecasts(forecast_df, train_df, title):
     ax.grid()
     st.pyplot(fig)
 
+import plotly.graph_objects as go
+
+def plot_forecasts(forecast_df, train_df, title):
+    # Combine historical and forecast data
+    plot_df = pd.concat([train_df, forecast_df]).set_index('ds')
+    
+    # Find relevant columns
+    historical_col = 'y'
+    forecast_col = next((col for col in plot_df.columns if 'median' in col), None)
+    lo_col = next((col for col in plot_df.columns if 'lo-90' in col), None)
+    hi_col = next((col for col in plot_df.columns if 'hi-90' in col), None)
+    
+    if forecast_col is None:
+        raise KeyError("No forecast column found in the data.")
+    
+    # Create Plotly figure
+    fig = go.Figure()
+    
+    # Add historical data
+    fig.add_trace(go.Scatter(x=plot_df.index, y=plot_df[historical_col], mode='lines', name='Historical'))
+    
+    # Add forecast data
+    fig.add_trace(go.Scatter(x=plot_df.index, y=plot_df[forecast_col], mode='lines', name='Forecast'))
+    
+    # Add confidence interval if available
+    if lo_col and hi_col:
+        fig.add_trace(go.Scatter(
+            x=plot_df.index,
+            y=plot_df[lo_col],
+            mode='lines',
+            fill='tozeroy',
+            fillcolor='rgba(0,100,80,0.2)',
+            line=dict(color='rgba(0,100,80,0.2)'),
+            name='90% Confidence Interval'
+        ))
+        fig.add_trace(go.Scatter(
+            x=plot_df.index,
+            y=plot_df[hi_col],
+            mode='lines',
+            line=dict(color='rgba(0,100,80,0.2)'),
+            showlegend=False
+        ))
+    
+    # Update layout
+    fig.update_layout(
+        title=title,
+        xaxis_title='Timestamp [t]',
+        yaxis_title='Value',
+        template='plotly_white'
+    )
+    
+    # Display the plot
+    st.plotly_chart(fig)
+
+
 def select_model_based_on_frequency(freq, nhits_models, timesnet_models, lstm_models, tft_models):
     if freq == 'D':
         return nhits_models['D'], timesnet_models['D'], lstm_models['D'], tft_models['D']