Initial version

app.py

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+import gradio as gr
+import pandas as pd
+import pmdarima as pm
+from pmdarima import auto_arima
+import matplotlib.pyplot as plt
+
+def forecast_time_series(file):
+    # Load data
+    data = pd.read_csv(file.name, skiprows=2)
+    period_type = data.columns[0]
+    data[period_type] = pd.to_datetime(data[period_type])
+    data.set_index(period_type, inplace=True)
+
+    # Fit the SARIMAX model automatically
+    model = auto_arima(data)
+    
+    # Forecasting
+    n_periods = 24  # Number of periods to forecast
+    forecast, conf_int = model.predict(n_periods=n_periods, return_conf_int=True)
+    
+    # Create a DataFrame with the forecast and confidence intervals
+    forecast_index = pd.date_range(start=data.index[-1], periods=n_periods + 1, freq=data.index.inferred_freq)[1:]
+    forecast_df = pd.DataFrame(forecast, index=forecast_index, columns=['Forecast'])
+    conf_int_df = pd.DataFrame(conf_int, index=forecast_index, columns=['Lower CI', 'Upper CI'])
+    forecast_df = pd.concat([forecast_df, conf_int_df], axis=1)
+
+    # Calculate the YoY change
+    sum_last_12_original = data.iloc[-12:, 0].sum()
+    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()
+    
+    # Return plot file path and YoY change
+    return plot_file, f'Year-over-Year Change in Sum of Values: {yoy_change:.2%}'
+
+# Create Gradio interface
+interface = gr.Interface(
+    theme=gr.themes.Monochrome(),
+    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")
+    ],
+    title="Time Series Forecasting with SARIMAX",
+    description="Upload a CSV file with a time series to forecast the next 12 periods and see the YoY % change."
+)
+
+# Launch the interface
+interface.launch()

requirements.txt

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+gradio
+pandas
+pmdarima
+matplotlib