Create main.py

main.py

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+import gradio as gr
+import pandas as pd
+from prophet import Prophet
+import matplotlib.pyplot as plt
+
+def forecast_sales(file):
+    # Load the sales data
+    sales_data = pd.read_csv(file.name)
+
+    # Convert date column to datetime
+    sales_data['date'] = pd.to_datetime(sales_data['date'])
+
+    # Prepare the data for Prophet with additional regressors
+    prophet_data = sales_data[['date', 'sales', 'orders', 'goog_upper_spend', 'fb_upper', 'search_spend', 'brand_spend', 'shopping_spend', 'fb_spend', 'pinterest_spend', 'tiktok', 'RTB_Spend', 'venta_buen_fin', 'venta_hot_sale']].rename(columns={'date': 'ds', 'sales': 'y'})
+
+    # Fill missing values with the mean of the respective columns
+    prophet_data.fillna(prophet_data.mean(), inplace=True)
+
+    # Initialize the Prophet model with additional regressors
+    model = Prophet()
+
+    # Add additional regressors
+    regressors = ['orders', 'goog_upper_spend', 'fb_upper', 'search_spend', 'brand_spend', 'shopping_spend', 'fb_spend', 'pinterest_spend', 'tiktok', 'RTB_Spend', 'venta_buen_fin', 'venta_hot_sale']
+    for regressor in regressors:
+        model.add_regressor(regressor)
+
+    # Fit the model
+    model.fit(prophet_data)
+
+    # Create a dataframe for future dates with additional regressors
+    future = model.make_future_dataframe(periods=30)
+    future = future.merge(sales_data[['date'] + regressors].set_index('date'), left_on='ds', right_index=True, how='left')
+    future.fillna(future.mean(), inplace=True)
+
+    # Predict the future sales
+    forecast = model.predict(future)
+
+    # Plot the forecast
+    fig1, ax1 = plt.subplots()
+    model.plot(forecast, ax=ax1)
+    ax1.set_title('Sales Forecast with Additional Regressors')
+    ax1.set_xlabel('Date')
+    ax1.set_ylabel('Sales')
+
+    # Merge the forecasted sales with the actual sales data for comparison
+    comparison_df = pd.merge(forecast[['ds', 'yhat']], prophet_data, left_on='ds', right_on='ds', how='left')
+    comparison_df.rename(columns={'yhat': 'forecasted_sales', 'y': 'actual_sales'}, inplace=True)
+
+    # Resample the comparison dataframe to monthly frequency and calculate the mean sales for each month
+    monthly_comparison_df = comparison_df.resample('M', on='ds').mean()
+
+    # Filter the monthly comparison dataframe to show only the last 12 months
+    last_12_months_df = monthly_comparison_df.tail(12)
+
+    # Calculate the percentage difference between actual and forecasted sales for the last 12 months
+    last_12_months_df['percentage_difference'] = ((last_12_months_df['actual_sales'] - last_12_months_df['forecasted_sales']) / last_12_months_df['actual_sales']) * 100
+
+    # Drop rows with NaN values in 'actual_sales'
+    last_12_months_df = last_12_months_df.dropna(subset=['actual_sales'])
+
+    # Plot the monthly comparison with bar charts and the percentage difference on top
+    fig2, ax2 = plt.subplots(figsize=(14, 7))
+    bar_width = 15  # Set the bar width
+
+    # Plot actual sales bars
+    bars_actual = ax2.bar(last_12_months_df.index, last_12_months_df['actual_sales'], 
+                          width=bar_width, label='Actual Sales', color='black', alpha=0.7)
+
+    # Plot forecasted sales bars
+    bars_forecasted = ax2.bar(last_12_months_df.index + pd.DateOffset(days=15), 
+                              last_12_months_df['forecasted_sales'], width=bar_width, 
+                              label='Forecasted Sales', color='blue', alpha=0.7)
+
+    # Add the percentage difference on top of the bars
+    for bar, diff in zip(bars_actual, last_12_months_df['percentage_difference']):
+        height = bar.get_height()
+        ax2.text(bar.get_x() + bar.get_width() / 2.0, height, f'{diff:.2f}%', ha='center', va='bottom', color='red')
+
+    ax2.set_title('Monthly Actual vs Forecasted Sales with Percentage Difference (Last 12 Months)')
+    ax2.set_xlabel('Date')
+    ax2.set_ylabel('Sales')
+    ax2.legend()
+
+    # Save the comparison dataframe to a CSV
+    last_12_months_df.to_csv('forecast_comparison.csv')
+
+    return fig1, fig2, 'forecast_comparison.csv'
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=forecast_sales,
+    inputs=gr.File(label="Upload CSV File"),
+    outputs=[
+        gr.Plot(label="Sales Forecast"),
+        gr.Plot(label="Monthly Comparison"),
+        gr.File(label="Comparison CSV")
+    ],
+    title="Sales Forecasting with Prophet",
+    description="Upload a CSV file with sales data to forecast future sales and compare with actual sales."
+)
+
+# Launch the interface
+interface.launch(debug=True)