feat: updated location of files

app.py

@@ -140,80 +140,83 @@ st.markdown("""
 
 # Navigation menu
 st.sidebar.title("DeepInsightz")
-page = st.sidebar.selectbox("Select the tool you want to use", ["Home", "Customer Analysis", "Articles Recommendations"])
+page = st.sidebar.selectbox("Select the tool you want to use", ["Summary", "Customer Analysis", "Articles Recommendations"])
 
 if page == "Home":
     st.title("Welcome to DeepInsightz")
     st.markdown("""
         ### Data-driven Customer Clustering
-        At DeepInsightz, we analyzed 4,000 customers, 400 suppliers, and over 800,000 sales lines to help businesses make smarter sales decisions.
+        We analyzed thousands of customers and suppliers to help businesses make smarter sales decisions.
     """)
 
-    # 1. Quick Metrics for Data Preparation
-    st.subheader("Data Preparation Overview")
-    st.write("""
-        - **Customers Analyzed**: 4,000
-        - **Suppliers Analyzed**: 400
-        - **Invoice Lines Processed**: 800,000
-    """)
-
-    st.info("The data was cleaned, duplicate entries were removed, and we standardized product codes and descriptions. We excluded customers with less than 12 purchases or total sales below €1,200 in 2024.")
-
-    # 2. Customer Distribution
-    st.subheader("Customer Distribution by Purchase Volume")
+    # Create layout with three columns: left (red), middle (white), and right (blue)
+    st.markdown('<div class="container">', unsafe_allow_html=True)
     
-    # Example data: distribution of customers by purchase volume
-    data = {
-        'Threshold (Items Purchased)': ['>1,000 items', '>5,000 items', '>10,000 items', '>20,000 items', '>50,000 items'],
-        'Number of Customers': [411, 180, 93, 47, 12],
-        'Percentage': [10, 4, 2, 1, 0.3]
-    }
-    df_customers = pd.DataFrame(data)
+    # Left Column (Red): Metrics and Donut Charts
+    st.markdown('<div class="col-red">', unsafe_allow_html=True)
+
+    st.markdown('<div class="custom-box">', unsafe_allow_html=True)
+    st.markdown("**Texas**<br><span style='font-size:28px;'>29.0 M</span><br><span style='color:#00FF00;'>+367 K</span>", unsafe_allow_html=True)
+    st.markdown('</div>', unsafe_allow_html=True)
     
-    fig_customers = px.bar(df_customers, x='Threshold (Items Purchased)', y='Number of Customers',
-                           title="Distribution of Customers by Purchase Volume",
-                           template="plotly_dark")
-    st.plotly_chart(fig_customers)
+    st.markdown('<div class="custom-box">', unsafe_allow_html=True)
+    st.markdown("**New York**<br><span style='font-size:28px;'>19.5 M</span><br><span style='color:#FF0000;'>-77 K</span>", unsafe_allow_html=True)
+    st.markdown('</div>', unsafe_allow_html=True)
+
+    st.markdown('<div class="custom-box">States Migration</div>', unsafe_allow_html=True)
 
-    # 3. Interactive Cluster Visualization (placeholder)
-    st.subheader("3D Cluster Visualization")
-    st.write("Explore the customer clusters and their product purchase behavior.")
+    # Donut chart placeholder
+    donut_fig = px.pie(values=[70, 30], names=['Inbound', 'Outbound'], hole=0.7)
+    donut_fig.update_traces(textinfo='percent+label')
+    donut_fig.update_layout(showlegend=False, margin=dict(t=0, b=0, l=0, r=0))
+    st.plotly_chart(donut_fig, use_container_width=True)
 
-    # Sample random data for a 3D cluster (replace with actual cluster data)
+    st.markdown('</div>', unsafe_allow_html=True)
+
+    # Middle Column (White): 3D Cluster Model and Bar Chart
+    st.markdown('<div class="col-white">', unsafe_allow_html=True)
+
+    st.subheader("3D Customer Clusters")
     np.random.seed(42)
     df_cluster = pd.DataFrame({
-        'Cluster': np.random.choice(['Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4', 'Cluster 5'], 100),
+        'Cluster': np.random.choice(['Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4'], 100),
         'x': np.random.randn(100),
         'y': np.random.randn(100),
         'z': np.random.randn(100)
     })
+    fig_cluster = px.scatter_3d(df_cluster, x='x', y='y', z='z', color='Cluster')
+    st.plotly_chart(fig_cluster, use_container_width=True)
 
-    fig_cluster = px.scatter_3d(df_cluster, x='x', y='y', z='z', color='Cluster', title="Customer Clusters (3D View)")
-    st.plotly_chart(fig_cluster)
+    st.subheader("Sales by Cluster")
+    sales_data = {'Cluster': ['Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4'],
+                  'Sales': [400000, 600000, 500000, 300000]}
+    df_sales = pd.DataFrame(sales_data)
+    fig_sales = px.bar(df_sales, x='Cluster', y='Sales', title="Sales by Cluster")
+    st.plotly_chart(fig_sales, use_container_width=True)
 
-    # 4. Product Groups vs Manufacturer
-    st.subheader("Product Group vs Manufacturer Distribution")
-    st.write("See how different manufacturers align with various product groups purchased by customers.")
-    
-    # Example data for product groups vs manufacturers (replace with real data)
-    prod_data = {
-        'Product Group': ['Group A', 'Group B', 'Group C', 'Group D', 'Group E'],
-        'Manufacturer 1': [25, 40, 35, 30, 50],
-        'Manufacturer 2': [30, 35, 30, 45, 40],
-        'Manufacturer 3': [35, 25, 40, 25, 35]
-    }
-    df_prod_manu = pd.DataFrame(prod_data)
-    
-    fig_prod_manu = px.bar(df_prod_manu, x='Product Group', y=['Manufacturer 1', 'Manufacturer 2', 'Manufacturer 3'],
-                           title="Manufacturer Distribution Across Product Groups")
-    st.plotly_chart(fig_prod_manu)
+    st.markdown('</div>', unsafe_allow_html=True)
 
-    # 5. Call to Action
-    st.markdown("""
-        ## Ready to Explore the Customer Clusters Further?
-        - Use the tools in the sidebar to dive deeper into **Customer Analysis** or get **Product Recommendations**.
+    # Right Column (Blue): Other Relevant Information
+    st.markdown('<div class="col-blue">', unsafe_allow_html=True)
+
+    st.subheader("Key Metrics Overview")
+    st.write("""
+        - **Customers Analyzed**: 4,000
+        - **Suppliers Analyzed**: 400
+        - **Invoice Lines Processed**: 800,000
+    """)
+
+    st.subheader("Data Preparation Summary")
+    st.write("""
+        - Cleaned and standardized product codes and descriptions.
+        - Excluded customers with fewer than 12 purchases or sales below €1,200.
     """)
 
+    st.markdown('</div>', unsafe_allow_html=True)
+
+    # Close the container div
+    st.markdown('</div>', unsafe_allow_html=True)
+
 # Customer Analysis Page
 elif page == "Customer Analysis":
     st.title("Customer Analysis")