Update app.py

app.py

@@ -56,6 +56,9 @@ if uploaded_file:
     # Clean whitespace and ensure consistent case in 'Customer Name' and 'Product Name'
     df['Customer Name'] = df['Customer Name'].str.strip().str.lower()
     df['Product Name'] = df['Product Name'].str.strip().str.lower()
+    df['City'] = df['City'].str.strip().str.lower()
+    df['State'] = df['State'].str.strip().str.lower()
+    df['Country'] = df['Country'].str.strip().str.lower()
 
     # Sidebar Filters
     st.sidebar.header("Filter Options")
@@ -63,6 +66,9 @@ if uploaded_file:
     # Text Input for Customer and Product
     customer_query = st.sidebar.text_input('Enter Customer Name (partial or full):').strip().lower()
     product_query = st.sidebar.text_input('Enter Product Name (partial or full):').strip().lower()
+    city_query = st.sidebar.text_input('Enter City (partial or full):').strip().lower()
+    state_query = st.sidebar.text_input('Enter State (partial or full):').strip().lower()
+    country_query = st.sidebar.text_input('Enter Country (partial or full):').strip().lower()
 
     # Date Range Selection
     start_date = st.sidebar.date_input('Start Date:', df['Date'].min())
@@ -82,19 +88,24 @@ if uploaded_file:
     if product_query:
         filtered_df = filtered_df[filtered_df['Product Name'].str.contains(product_query, case=False, na=False)]
 
+    # Filter Data by City
+    if city_query:
+        filtered_df = filtered_df[filtered_df['City'].str.contains(city_query, case=False, na=False)]
+
+    # Filter Data by State
+    if state_query:
+        filtered_df = filtered_df[filtered_df['State'].str.contains(state_query, case=False, na=False)]
+
+    # Filter Data by Country
+    if country_query:
+        filtered_df = filtered_df[filtered_df['Country'].str.contains(country_query, case=False, na=False)]
+
     # Display Filtered Data
     st.write(f"Filtered Data: {len(filtered_df)} records found.")
     st.dataframe(filtered_df)
 
-    # Key Metrics: High Margin Product and Highest Sales Month
+    # Visualizations
     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')