Update app.py

app.py

@@ -8,20 +8,30 @@ st.title('Sales Data Visualization App')
 # Display Sample Data Format
 st.subheader('Sample Data Format:')
 st.write("""
-The data should contain the following columns:
-
-| Customer Name | Date       | Product Name | Net Sales Value | Margin Amount | Cost  |
-|----------------|------------|--------------|------------------|---------------|-------|
-| John Doe       | 2024-01-01 | Product A    | 1000             | 300           | 700   |
-| Jane Smith     | 2024-01-02 | Product B    | 1500             | 400           | 1100  |
-
-### Key Columns:
-- **Customer Name**: Name of the customer
-- **Date**: Sale date (YYYY-MM-DD)
-- **Product Name**: Product sold
-- **Net Sales Value**: Sales value of the product
-- **Margin Amount**: Profit margin on the sale
-- **Cost**: Cost of the product sold
+The data should be in the following format with the listed columns:
+
+| Customer Name | Date       | City         | Country     | State       | Product Name   | Product Attribute 1 | Product Attribute 2 | Product Attribute 3 | Product Attribute 4 | Net Sales Value | Margin Amount | Cost  |
+|----------------|------------|--------------|-------------|-------------|-----------------|---------------------|---------------------|---------------------|---------------------|------------------|---------------|-------|
+| John Doe       | 2024-01-01 | New York     | USA         | NY          | Product A       | Attribute 1A         | Attribute 2A         | Attribute 3A         | Attribute 4A         | 1000             | 300           | 700   |
+| Jane Smith     | 2024-01-02 | Los Angeles  | USA         | CA          | Product B       | Attribute 1B         | Attribute 2B         | Attribute 3B         | Attribute 4B         | 1500             | 400           | 1100  |
+| Bob Johnson    | 2024-02-15 | Chicago      | USA         | IL          | Product A       | Attribute 1A         | Attribute 2A         | Attribute 3A         | Attribute 4A         | 1200             | 350           | 850   |
+| Alice Williams | 2024-03-10 | Miami        | USA         | FL          | Product C       | Attribute 1C         | Attribute 2C         | Attribute 3C         | Attribute 4C         | 2000             | 500           | 1500  |
+| Charlie Brown  | 2024-04-05 | Houston      | USA         | TX          | Product B       | Attribute 1B         | Attribute 2B         | Attribute 3B         | Attribute 4B         | 1800             | 450           | 1350  |
+
+### Description of Columns:
+- **Customer Name**: The name of the customer (e.g., John Doe).
+- **Date**: The date of the sale, formatted as `YYYY-MM-DD` (e.g., `2024-01-01`).
+- **City**: The city where the sale took place (e.g., New York).
+- **Country**: The country where the sale took place (e.g., USA).
+- **State**: The state where the sale took place (e.g., NY).
+- **Product Name**: The name of the product (e.g., Product A).
+- **Product Attribute 1**: Additional product attribute (e.g., Attribute 1A).
+- **Product Attribute 2**: Additional product attribute (e.g., Attribute 2A).
+- **Product Attribute 3**: Additional product attribute (e.g., Attribute 3A).
+- **Product Attribute 4**: Additional product attribute (e.g., Attribute 4A).
+- **Net Sales Value**: The net sales value for the transaction (e.g., `1000`).
+- **Margin Amount**: The margin for the transaction (e.g., `300`).
+- **Cost**: The cost of the product sold (e.g., `700`).
 """)
 
 # Upload File
@@ -76,8 +86,15 @@ if uploaded_file:
     st.write(f"Filtered Data: {len(filtered_df)} records found.")
     st.dataframe(filtered_df)
 
-    # Visualizations
+    # Key Metrics: High Margin Product and Highest Sales Month
     if not filtered_df.empty:
+        st.subheader("Key Metrics")
+        high_margin_product = filtered_df.loc[filtered_df['Margin Amount'].idxmax(), 'Product Name']
+        highest_sales_month = filtered_df.loc[filtered_df['Net Sales Value'].idxmax(), 'Month']
+        st.metric("High Margin Product", high_margin_product)
+        st.metric("Highest Sales Month", highest_sales_month)
+
+        # Visualizations
         st.subheader("Sales Trend Over Time")
         trend = filtered_df.groupby('Date')['Net Sales Value'].sum().reset_index()
         fig_trend = px.line(trend, x='Date', y='Net Sales Value', title='Sales Over Time')