added dataframe to demo

app.py

@@ -21,12 +21,15 @@ with gr.Blocks() as demo:
         periods = gr.Slider(minimum = 1, maximum = 12, step = 1, value = 3, label = 'Months')
         percent_change = gr.Slider(minimum = -100, maximum = 100, step = 5, value = -5, label = '% Change vs Last Year')
 
-    plot = gr.Plot()
+    with gr.Row():
+        dataframe = gr.Dataframe(label = 'Predicted values')
+        plot = gr.Plot()
 
     serie.change(get_forecast, [serie, periods, percent_change], plot, queue=False)
     periods.change(get_forecast, [serie, periods, percent_change], plot, queue=False)
     percent_change.change(get_forecast, [serie, periods, percent_change], plot, queue=False)    
     plot.change(get_forecast, [serie, periods, percent_change], plot, queue=False)    
+    dataframe.change(get_forecast, [serie, periods, percent_change], [plot, dataframe], queue=False)    
     demo.load(get_forecast, [serie, periods, percent_change], plot, queue=False)
 
     demo.launch(debug = False)

get_forecast.py

@@ -27,20 +27,25 @@ def get_forecast(serie: str, periods, percent_change: int):
     past_prices = history[history['ds'].isin(last_year_dates)]['precio_hl'].values
 
     # generate new_prices
-    
-    percent_change = percent_change / 100
-    
+    percent_change = percent_change / 100    
     new_prices = past_prices * (1 + percent_change)
-
     future['precio_hl'] = new_prices
 
+    # prediction
     forecast = model.predict(future)
 
     future_values = forecast[['ds', 'yhat']]
+    
+    # arrange dataframe
+    df_future = future_values.rename(columns = {'ds': 'Date', 'yhat': serie}).copy()
+    df_future['Date'] = df_future['Date'].apply(lambda x: x.date())
+    df_future[serie] = df_future[serie].apply(lambda x: round(x, 4))
 
+    # aux to plot
     last_obs = history.iloc[-1:][['ds', 'y']].rename(columns = {'y': 'yhat'})
     future_aux = pd.concat([last_obs, future_values])
 
+    # plot
     fig = plt.figure()
     #plt.plot(history['ds'], history['y'], label = 'Historic data', marker = '.', color = 'C0')
     plt.plot(future_aux['ds'], future_aux['yhat'], label = 'Forecast', marker = '.', color = 'C1')
@@ -51,4 +56,4 @@ def get_forecast(serie: str, periods, percent_change: int):
     plt.tight_layout()
     plt.legend()
     
-    return fig
+    return fig, df_future