import gradio as gr import pandas as pd import matplotlib.pyplot as plt import datetime import warnings import os import tempfile from cachetools import cached, TTLCache warnings.filterwarnings("ignore", category=FutureWarning, module="seaborn") # ------------------------------------------------------------------ # 1) Load CSV data once # ------------------------------------------------------------------ csv_data = None def load_csv_data(): global csv_data # Optional: specify column dtypes if known; adjust as necessary dtype_dict = { "order_id": "Int64", "customer_id": "Int64", "product_id": "Int64", "quantity": "Int64", "price": "float", "total": "float", "customer_name": "string", "product_names": "string", "categories": "string" } csv_data = pd.read_csv( "sales_data.csv", parse_dates=["order_date"], dayfirst=True, # if your dates are DD/MM/YYYY format low_memory=False, dtype=dtype_dict ) load_csv_data() cache = TTLCache(maxsize=128, ttl=300) @cached(cache) def get_unique_categories(): global csv_data if csv_data is None: return [] cats = sorted(csv_data['categories'].dropna().unique().tolist()) cats = [cat.capitalize() for cat in cats] return cats def get_date_range(): global csv_data if csv_data is None or csv_data.empty: return None, None return csv_data['order_date'].min(), csv_data['order_date'].max() def filter_data(start_date, end_date, category): global csv_data if isinstance(start_date, str): start_date = datetime.datetime.strptime(start_date, '%Y-%m-%d').date() if isinstance(end_date, str): end_date = datetime.datetime.strptime(end_date, '%Y-%m-%d').date() df = csv_data.loc[ (csv_data['order_date'] >= pd.to_datetime(start_date)) & (csv_data['order_date'] <= pd.to_datetime(end_date)) ].copy() if category != "All Categories": df = df.loc[df['categories'].str.capitalize() == category].copy() return df def get_dashboard_stats(start_date, end_date, category): df = filter_data(start_date, end_date, category) if df.empty: return (0, 0, 0, "N/A") df['revenue'] = df['price'] * df['quantity'] total_revenue = df['revenue'].sum() total_orders = df['order_id'].nunique() avg_order_value = total_revenue / total_orders if total_orders else 0 cat_revenues = df.groupby('categories')['revenue'].sum().sort_values(ascending=False) top_category = cat_revenues.index[0] if not cat_revenues.empty else "N/A" return (total_revenue, total_orders, avg_order_value, top_category.capitalize()) def get_data_for_table(start_date, end_date, category): df = filter_data(start_date, end_date, category) if df.empty: return pd.DataFrame() df = df.sort_values(by=["order_id", "order_date"], ascending=[True, False]).copy() columns_order = [ "order_id", "order_date", "customer_id", "customer_name", "product_id", "product_names", "categories", "quantity", "price", "total" ] columns_order = [col for col in columns_order if col in df.columns] df = df[columns_order].copy() df['revenue'] = df['price'] * df['quantity'] return df def get_plot_data(start_date, end_date, category): df = filter_data(start_date, end_date, category) if df.empty: return pd.DataFrame() df['revenue'] = df['price'] * df['quantity'] plot_data = df.groupby(df['order_date'].dt.date)['revenue'].sum().reset_index() plot_data.rename(columns={'order_date': 'date'}, inplace=True) return plot_data def get_revenue_by_category(start_date, end_date, category): df = filter_data(start_date, end_date, category) if df.empty: return pd.DataFrame() df['revenue'] = df['price'] * df['quantity'] cat_data = df.groupby('categories')['revenue'].sum().reset_index() cat_data = cat_data.sort_values(by='revenue', ascending=False) return cat_data def get_top_products(start_date, end_date, category): df = filter_data(start_date, end_date, category) if df.empty: return pd.DataFrame() df['revenue'] = df['price'] * df['quantity'] prod_data = df.groupby('product_names')['revenue'].sum().reset_index() prod_data = prod_data.sort_values(by='revenue', ascending=False).head(10) return prod_data def create_matplotlib_figure(data, x_col, y_col, title, xlabel, ylabel, orientation='v'): plt.figure(figsize=(10, 6)) if data.empty: plt.text(0.5, 0.5, 'No data available', ha='center', va='center') else: if orientation == 'v': plt.bar(data[x_col], data[y_col]) plt.xticks(rotation=45, ha='right') else: plt.barh(data[x_col], data[y_col]) plt.gca().invert_yaxis() plt.title(title) plt.xlabel(xlabel) plt.ylabel(ylabel) plt.tight_layout() with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmpfile: plt.savefig(tmpfile.name) plt.close() return tmpfile.name def update_dashboard(start_date, end_date, category): total_revenue, total_orders, avg_order_value, top_category = get_dashboard_stats(start_date, end_date, category) # Generate plots revenue_data = get_plot_data(start_date, end_date, category) category_data = get_revenue_by_category(start_date, end_date, category) top_products_data = get_top_products(start_date, end_date, category) revenue_over_time_path = create_matplotlib_figure( revenue_data, 'date', 'revenue', "Revenue Over Time", "Date", "Revenue" ) revenue_by_category_path = create_matplotlib_figure( category_data, 'categories', 'revenue', "Revenue by Category", "Category", "Revenue" ) top_products_path = create_matplotlib_figure( top_products_data, 'product_names', 'revenue', "Top Products", "Revenue", "Product Name", orientation='h' ) # Data table table_data = get_data_for_table(start_date, end_date, category) return ( revenue_over_time_path, revenue_by_category_path, top_products_path, table_data, total_revenue, total_orders, avg_order_value, top_category ) def create_dashboard(): min_date, max_date = get_date_range() if min_date is None or max_date is None: min_date = datetime.datetime.now() max_date = datetime.datetime.now() default_start_date = min_date default_end_date = max_date with gr.Blocks(css=""" footer {display: none !important;} .tabs {border: none !important;} .gr-plot {border: none !important; box-shadow: none !important;} """) as dashboard: gr.Markdown("# Sales Performance Dashboard") # Filters row with gr.Row(): start_date = gr.DateTime( label="Start Date", value=default_start_date.strftime('%Y-%m-%d'), include_time=False, type="datetime" ) end_date = gr.DateTime( label="End Date", value=default_end_date.strftime('%Y-%m-%d'), include_time=False, type="datetime" ) category_filter = gr.Dropdown( choices=["All Categories"] + get_unique_categories(), label="Category", value="All Categories" ) gr.Markdown("# Key Metrics") # Stats row with gr.Row(): total_revenue = gr.Number(label="Total Revenue", value=0) total_orders = gr.Number(label="Total Orders", value=0) avg_order_value = gr.Number(label="Average Order Value", value=0) top_category = gr.Textbox(label="Top Category", value="N/A") gr.Markdown("# Visualisations") # Tabs for Plots with gr.Tabs(): with gr.Tab("Revenue Over Time"): revenue_over_time_image = gr.Image(label="Revenue Over Time", container=False) with gr.Tab("Revenue by Category"): revenue_by_category_image = gr.Image(label="Revenue by Category", container=False) with gr.Tab("Top Products"): top_products_image = gr.Image(label="Top Products", container=False) gr.Markdown("# Raw Data") # Data Table (below the plots) data_table = gr.DataFrame( label="Sales Data", type="pandas", interactive=False ) # When filters change, update everything for f in [start_date, end_date, category_filter]: f.change( fn=lambda s, e, c: update_dashboard(s, e, c), inputs=[start_date, end_date, category_filter], outputs=[ revenue_over_time_image, revenue_by_category_image, top_products_image, data_table, total_revenue, total_orders, avg_order_value, top_category ] ) # Initial load dashboard.load( fn=lambda: update_dashboard(default_start_date, default_end_date, "All Categories"), outputs=[ revenue_over_time_image, revenue_by_category_image, top_products_image, data_table, total_revenue, total_orders, avg_order_value, top_category ] ) return dashboard if __name__ == "__main__": dashboard = create_dashboard() dashboard.launch(share=True)