Update app.py

app.py

@@ -1,13 +1,9 @@
 import streamlit as st
 import pandas as pd
 import plotly.express as px
-import plotly.graph_objects as go
 import random
 from datetime import datetime, timedelta
 
-# Title of the App
-st.title('Sales Data Visualization App')
-
 # Sample Data Generation (You can remove this part when uploading your own file)
 def generate_sample_data():
     customers = ['Customer A', 'Customer B', 'Customer C', 'Customer D']
@@ -35,10 +31,6 @@ def generate_sample_data():
 # Load sample data
 df = generate_sample_data()
 
-# Display the first few rows of the dataframe to understand its structure
-st.write("Preview of the sample data:")
-st.dataframe(df.head())
-
 # Data Preprocessing
 df['Date'] = pd.to_datetime(df['Date'])
 df['Year'] = df['Date'].dt.year
@@ -84,6 +76,9 @@ if state_query:
 if country_query:
     filtered_df = filtered_df[filtered_df['Country'].str.contains(country_query, case=False, na=False)]
 
+# Add 'Net Sales Value - Cost' as a new column
+filtered_df['Net Sales Value - Cost'] = filtered_df['Net Sales Value'] - filtered_df['Cost']
+
 # Display Filtered Data
 st.write(f"Filtered Data: {len(filtered_df)} records found.")
 st.dataframe(filtered_df)
@@ -115,38 +110,11 @@ if not filtered_df.empty:
     )
     st.dataframe(matrix_data)
 
-    # Visualization 1: Sales by Customer (Bar chart)
-    st.subheader("Sales by Customer")
-    sales_by_customer = filtered_df.groupby('Customer Name')['Net Sales Value'].sum().reset_index()
-    fig_customer = px.bar(sales_by_customer, x='Customer Name', y='Net Sales Value', title='Sales by Customer')
-    st.plotly_chart(fig_customer)
-
-    # Visualization 2: Sales by Product (Bar chart)
-    st.subheader("Sales by Product")
-    sales_by_product = filtered_df.groupby('Product Name')['Net Sales Value'].sum().reset_index()
-    fig_product = px.bar(sales_by_product, x='Product Name', y='Net Sales Value', title='Sales by Product')
-    st.plotly_chart(fig_product)
-
-    # Visualization 3: Year-over-Year Sales Trend (Line chart)
-    st.subheader("Year-over-Year Sales Trend")
-    year_trend = filtered_df.groupby(['Year'])['Net Sales Value'].sum().reset_index()
-    fig_year_trend = px.line(year_trend, x='Year', y='Net Sales Value', title='Year-over-Year Sales Trend')
-    st.plotly_chart(fig_year_trend)
-
-     Add 'Net Sales Value - Cost' as a new column
-     filtered_df['Net Sales Value - Cost'] = filtered_df['Net Sales Value'] - filtered_df['Cost']
-
-   # Visualization 4: Profit and Loss Overview (Simple Table)
-   st.subheader("Profit and Loss Overview")
-   pnl_data = filtered_df[['Customer Name', 'Product Name', 'Net Sales Value', 'Cost', 'Net Sales Value - Cost']]
-   pnl_data.columns = ['Customer', 'Product', 'Sales', 'Cost', 'Profit']
-   st.dataframe(pnl_data)
-    
-    # Visualization 5: Gross Margin Distribution (Pie chart)
-    st.subheader("Gross Margin Distribution")
-    margin_data = filtered_df.groupby('Product Name')['Net Sales Value'].sum().reset_index()
-    fig_margin = px.pie(margin_data, names='Product Name', values='Net Sales Value', title='Gross Margin by Product')
-    st.plotly_chart(fig_margin)
+    # Visualization 4: Profit and Loss Overview (Simple Table)
+    st.subheader("Profit and Loss Overview")
+    pnl_data = filtered_df[['Customer Name', 'Product Name', 'Net Sales Value', 'Cost', 'Net Sales Value - Cost']]
+    pnl_data.columns = ['Customer', 'Product', 'Sales', 'Cost', 'Profit']
+    st.dataframe(pnl_data)
 
 else:
     st.write("No data available for the selected filters.")