feat: updated location of files

app.py

@@ -149,73 +149,61 @@ if page == "Summary":
         We analyzed thousands of customers and suppliers to help businesses make smarter sales decisions.
     """)
 
-    # Create layout with three columns: left (red), middle (white), and right (blue)
-    st.markdown('<div class="container">', unsafe_allow_html=True)
-    
-    # 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)
-    
-    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)
-
-    # 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)
+    # Create layout with three columns: left (col1), middle (col2), and right (col3)
+    col1, col2, col3 = st.columns([1, 2, 1])
 
-    st.markdown('</div>', unsafe_allow_html=True)
+    # Left Column (Red): Metrics and Donut Charts
+    with col1:
+        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)
+        
+        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)
+
+        # 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)
 
     # 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'], 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)
-
-    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)
-
-    st.markdown('</div>', unsafe_allow_html=True)
-
-    # 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)
+    with col2:
+        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'], 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)
+
+        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)
+
+    # Right Column (Blue): Key Metrics Overview and Data Preparation Summary
+    with col3:
+        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.
+        """)
 
 # Customer Analysis Page
 elif page == "Customer Analysis":