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.github/workflows/update_space.yml

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+name: Run Python script
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+
+    - name: Install Gradio
+      run: python -m pip install gradio
+
+    - name: Log in to Hugging Face
+      run: python -c 'import huggingface_hub; huggingface_hub.login(token="${{ secrets.hf_token }}")'
+
+    - name: Deploy to Spaces
+      run: gradio deploy

README.md

@@ -1,12 +1,6 @@
 ---
-title: Data Dashboard
-emoji: 🐨
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 5.19.0
+title: data-dashboard
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 5.9.1
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+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=False)

requirements.txt

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+gradio 
+pandas 
+matplotlib