feat: updated location of files

app.py

@@ -12,7 +12,7 @@ from utils import recomienda_tfid
 
 
 # Page configuration
-st.set_page_config(page_title="Customer Insights App", page_icon=":bar_chart:")
+st.set_page_config(page_title="DeepInsightz", page_icon=":bar_chart:")
 
 # Load CSV files at the top
 df = pd.read_csv("df_clean.csv")
@@ -106,9 +106,52 @@ st.sidebar.title("DeepInsightz")
 page = st.sidebar.selectbox("Select the tool you want to use", ["Home", "Customer Analysis", "Articles Recommendations"])
 
 # Home Page
+# Home Page Design
 if page == "Home":
-    st.markdown("## Welcome to the Customer Insights App")
-    st.write("Use the dropdown menu to navigate between the different sections.")
+    st.title("Welcome to DeepInsightz")
+    st.markdown("""
+        ### Personalized Sales Insights & Product Recommendations
+        Discover customer trends and boost your sales by understanding purchase patterns and getting tailored product suggestions.
+    """)
+
+    # 1. Quick Metrics (Placeholder for now)
+    st.subheader("Quick Overview")
+    col1, col2, col3 = st.columns(3)
+    col1.metric("Total Customers", "1,532", "+3.8%")
+    col2.metric("Total Sales (2023)", "€2.9M", "+4.2%")
+    col3.metric("Top Cluster", "Cluster 3", "+1.5%")
+
+    # 2. 3D Cluster Model (Placeholder with random data)
+    st.subheader("Customer Segmentation (3D Clusters)")
+    st.write("Explore the different customer types based on purchase patterns.")
+    
+    # Create random data for 3D scatter plot (replace this with your real data)
+    np.random.seed(42)
+    df_3d = pd.DataFrame({
+        'Cluster': np.random.choice(['Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4', 'Cluster 5', 'Cluster 6', 'Cluster 7'], 100),
+        'x': np.random.randn(100),
+        'y': np.random.randn(100),
+        'z': np.random.randn(100)
+    })
+
+    fig_3d = px.scatter_3d(df_3d, x='x', y='y', z='z', color='Cluster', title="Customer Clusters (3D View)")
+    st.plotly_chart(fig_3d)
+
+    # 3. Sales by Cluster (Example of a simple bar chart)
+    st.subheader("Sales by Cluster")
+    sales_data = {
+        'Cluster': ['Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4', 'Cluster 5', 'Cluster 6', 'Cluster 7'],
+        'Sales': [500000, 400000, 600000, 350000, 450000, 550000, 300000]
+    }
+    df_sales = pd.DataFrame(sales_data)
+    fig_sales = px.bar(df_sales, x='Cluster', y='Sales', title="Total Sales by Cluster (2023)")
+    st.plotly_chart(fig_sales)
+
+    # 4. Call to Action
+    st.markdown("""
+        ## Ready to Dive Deeper?
+        - Use the tools in the sidebar to explore **Customer Analysis** or get **Articles Recommendations**.
+    """)
 
 # Customer Analysis Page
 elif page == "Customer Analysis":