Update app.py

app.py

@@ -414,7 +414,7 @@ def timegpt_fcst():
 
     df = df.drop_duplicates(subset=['ds']).reset_index(drop=True)
     
-    
+    plot_type = st.sidebar.selectbox("Select Visualization", ["Matplotlib", "Plotly"])
     if st.sidebar.button("Submit"):
         start_time = time.time()
         forecast_df = nixtla_client.forecast(
@@ -428,7 +428,17 @@ def timegpt_fcst():
 
         if 'forecast_df' in st.session_state:
             forecast_df = st.session_state.forecast_df
-            st.pyplot(nixtla_client.plot(df, forecast_df, level=[90], engine='plotly'))
+            
+            if plot_type == "Matplotlib":
+                # Convert the Plotly figure to a Matplotlib figure if needed
+                # Note: You may need to handle this conversion depending on your specific use case
+                # For now, this example assumes that you are using a Matplotlib figure
+                fig = nixtla_client.plot(df, forecast_df, level=[90], engine='matplotlib')
+                st.pyplot(fig)
+            elif plot_type == "Plotly":
+                # Plotly figure directly
+                fig = nixtla_client.plot(df, forecast_df, level=[90], engine='plotly')
+                st.plotly_chart(fig)
     
             end_time = time.time()  # End timing
             time_taken = end_time - start_time
@@ -483,6 +493,8 @@ def timegpt_anom():
     freq = determine_frequency(df)
 
     df = df.drop_duplicates(subset=['ds']).reset_index(drop=True)
+    
+    plot_type = st.sidebar.selectbox("Select Visualization", ["Matplotlib", "Plotly"])
     if st.sidebar.button("Submit"):
         start_time=time.time()
         anom_df = nixtla_client.detect_anomalies(
@@ -494,7 +506,17 @@ def timegpt_anom():
 
         if 'anom_df' in st.session_state:
             anom_df = st.session_state.anom_df
-            st.pyplot(nixtla_client.plot(df, anom_df, engine='plotly'))
+            
+            if plot_type == "Matplotlib":
+                # Convert the Plotly figure to a Matplotlib figure if needed
+                # Note: You may need to handle this conversion depending on your specific use case
+                # For now, this example assumes that you are using a Matplotlib figure
+                fig = nixtla_client.plot(df, forecast_df, level=[90], engine='matplotlib')
+                st.pyplot(fig)
+            elif plot_type == "Plotly":
+                # Plotly figure directly
+                fig = nixtla_client.plot(df, forecast_df, level=[90], engine='plotly')
+                st.plotly_chart(fig)
     
             end_time = time.time()  # End timing
             time_taken = end_time - start_time