Add forecast export button, add interactive Plotly plot

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import pandas as pd
 import pmdarima as pm
 from pmdarima import auto_arima
-import matplotlib.pyplot as plt
+import plotly.graph_objs as go
 
 def forecast_time_series(file):
     # Load data
@@ -29,26 +29,40 @@ def forecast_time_series(file):
     sum_first_12_forecast = forecast_df['Forecast'].iloc[:12].sum()
     yoy_change = (sum_first_12_forecast - sum_last_12_original) / sum_last_12_original
 
-    # Plot the original time series and forecast
-    plt.figure(figsize=(12, 6))
-    plt.plot(data.index, data.iloc[:, 0], label='Original Series')
-    plt.plot(forecast_df.index, forecast_df['Forecast'], color='red', label='Forecast')
-    plt.fill_between(forecast_df.index, 
-                     forecast_df['Lower CI'], 
-                     forecast_df['Upper CI'], 
-                     color='pink', alpha=0.3, label='Confidence Interval')
-    plt.xlabel('Date')
-    plt.ylabel('Values')
-    plt.title('Original Time Series and Forecast with Confidence Intervals')
-    plt.legend()
-    
-    # Save plot to a file
-    plot_file = 'forecast_plot.png'
-    plt.savefig(plot_file)
-    plt.close()
+    # Create an interactive plot with Plotly
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=data.index, y=data.iloc[:, 0], mode='lines', name='Original Series'))
+    fig.add_trace(go.Scatter(x=forecast_df.index, y=forecast_df['Forecast'], mode='lines', name='Forecast', line=dict(color='red')))
+    fig.add_trace(go.Scatter(
+        x=forecast_df.index,
+        y=forecast_df['Lower CI'],
+        fill=None,
+        mode='lines',
+        line=dict(color='pink'),
+        showlegend=False
+    ))
+    fig.add_trace(go.Scatter(
+        x=forecast_df.index,
+        y=forecast_df['Upper CI'],
+        fill='tonexty',
+        mode='lines',
+        line=dict(color='pink'),
+        name='Confidence Interval'
+    ))
+    fig.update_layout(
+        title='Original Time Series and Forecast with Confidence Intervals',
+        xaxis_title='Date',
+        yaxis_title='Values',
+        hovermode='x unified'
+    )
     
+    # Combine original data and forecast data into one DataFrame for export
+    combined_df = pd.concat([data, forecast_df], axis=0)
+    combined_file = 'combined_data.csv'
+    combined_df.to_csv(combined_file)
+
     # Return plot file path and YoY change
-    return plot_file, f'Year-over-Year Change in Sum of Values: {yoy_change:.2%}'
+    return fig, f'Year-over-Year Change in Sum of Values: {yoy_change:.2%}', combined_file
 
 # Create Gradio interface
 interface = gr.Interface(
@@ -56,12 +70,13 @@ interface = gr.Interface(
     fn=forecast_time_series,
     inputs=gr.File(label="Upload Time Series CSV"),
     outputs=[
-        gr.Image(label="Time Series + Forecast Chart"),
-        gr.Text(label="YoY % Change")
+        gr.Plot(label="Time Series + Forecast Chart"),
+        gr.Text(label="YoY % Change"),
+        gr.File(label="Download Combined Data CSV")
     ],
     title="Time Series Forecasting",
-    description="Upload a CSV file with a time series to forecast the next 24 periods and see the YoY % change."
+    description="Upload a CSV file with a time series to forecast the next 24 periods and see the YoY % change. Download the combined original and forecast data."
 )
 
 # Launch the interface
-interface.launch()
+interface.launch()

requirements.txt

@@ -1,4 +1,5 @@
 gradio
 pandas
 pmdarima
-matplotlib
+matplotlib
+plotly