Update app.py

app.py

@@ -8,6 +8,9 @@ import time
 import pandas as pd
 import matplotlib.pyplot as plt
 import numpy as np
+import math
+
+import matplotlib.ticker as ticker
 
 import torch
 from chronos import ChronosPipeline
@@ -68,9 +71,6 @@ class Seafoam(Base):
 seafoam = Seafoam()
 
 
-import numpy as np
-import matplotlib.ticker as ticker
-
 def process_data(csv_file):
     try:
         # Read the CSV file
@@ -96,6 +96,48 @@ def process_data(csv_file):
         forecast_index = range(len(monthly_sales), len(monthly_sales) + prediction_length)
         low, median, high = np.quantile(forecast[0].numpy(), [0.1, 0.5, 0.9], axis=0)
 
+        df['month_name'] = df['date'].dt.month_name()
+        month_order = [
+            'January', 'February', 'March', 'April', 'May', 'June',
+            'July', 'August', 'September', 'October', 'November', 'December'
+        ]
+        df['month_name'] = pd.Categorical(df['month_name'], categories=month_order, ordered=True)
+
+        expanded_df = df.copy()
+        year_month_sum = expanded_df.groupby(['year', 'month_name'])['sold_qty'].sum().reset_index()
+
+        # Create a pivot table: sum of units sold per year and month
+        pivot_table = year_month_sum.pivot(index='year', columns='month_name', values='sold_qty')
+
+        new_data_list = [math.ceil(x) for x in median]
+
+        # Add the new data list for the next year (incrementing the year by 1)
+        next_year = pivot_table.index[-1] + 1  # Increment the year by 1
+        pivot_table.loc[next_year] = new_data_list  # Add the new row for the next year
+
+        # Visualization: Pivot Table Data (Second Plot)
+        fig3, ax3 = plt.subplots(figsize=(18, 6))
+
+        # Create a table inside the plot
+        ax3.axis('off')  # Turn off the axis
+        table = ax3.table(cellText=pivot_table.values, colLabels=pivot_table.columns, rowLabels=pivot_table.index, loc='center', cellLoc='center')
+
+        # Style the table
+        table.auto_set_font_size(False)
+        table.set_fontsize(12)
+        table.scale(1.2, 1.2)  # Scale the table for better visibility
+
+        # Adjust table colors (optional)
+        for (i, j), cell in table.get_celld().items():
+            if i == 0:
+                cell.set_text_props(weight='bold')
+                cell.set_facecolor('#f2f2f2')
+            elif j == 0:
+                cell.set_text_props(weight='bold')
+                cell.set_facecolor('#f2f2f2')
+            else:
+                cell.set_facecolor('white')
+
         # Visualization
         plt.figure(figsize=(30, 10))
         plt.plot(monthly_sales["y"], color="royalblue", label="Historical Data", linewidth=2)
@@ -117,7 +159,7 @@ def process_data(csv_file):
         plt.grid(linestyle='--', linewidth=1.2, color='gray', alpha=0.7)
         plt.tight_layout()
 
-        return plt.gcf()
+        return plt.gcf(), fig3
 
     except Exception as e:
         print(f"Error: {str(e)}")
@@ -125,7 +167,7 @@ def process_data(csv_file):
 
 # Create Gradio interface
 with gr.Blocks(theme=seafoam) as demo:
-    gr.Markdown("# Chronos Forecasting - Tops Infosolution Pvt. Ltd")
+    gr.Markdown("# Chronos Forecasting - Tops infosolutions Pvt Ltd")
     gr.Markdown("Upload a CSV file and click 'Forecast' to generate sales forecast for next 12 months .")
 
     with gr.Row():
@@ -138,12 +180,12 @@ with gr.Blocks(theme=seafoam) as demo:
         plot_output = gr.Plot(label="Chronos Forecasting Visualization")
 
     with gr.Row():
-        plot_output = gr.Plot(label="Chronos Forecasting Visualization")
+        pivot_plot_output = gr.Plot(label="Monthly Sales Pivot Table")
 
     visualize_btn.click(
         fn=process_data,
         inputs=[file_input],
-        outputs=[plot_output]
+        outputs=[plot_output, pivot_plot_output]
     )
 
 # Launch the app