Update app.py

app.py

@@ -470,6 +470,42 @@ if st.session_state['active_card'] == 'card3':
     agg_df_filtered = agg_df_filtered.dropna(subset=['PE_Coeff'])
     st.dataframe(agg_df_filtered)
     st.write(agg_df_filtered.shape)
+    # Extract values for the current and previous years from row 1 and row 2 of the dataframe
+    current_year_row = agg_df_filtered.iloc[1]  # Row 1 - Current Year
+    previous_year_row = agg_df_filtered.iloc[0]  # Row 2 - Previous Year
+
+    # Extract values for Unit Price and Sales Volume
+    unit_price_current_year = current_year_row['UnitPrice']
+    unit_price_previous_year = previous_year_row['UnitPrice']
+    sales_volume_current_year = current_year_row['SalesVolume']
+    sales_volume_previous_year = previous_year_row['SalesVolume']
+
+    # Calculate percentage changes for Unit Price and Sales Volume
+    unit_price_pct = ((unit_price_current_year - unit_price_previous_year) / unit_price_previous_year) * 100
+    sales_volume_pct = ((sales_volume_current_year - sales_volume_previous_year) / sales_volume_previous_year) * 100
+
+    # Calculate PE Coefficient
+    pe_coeff = sales_volume_pct / unit_price_pct
+
+    # Render LaTeX formulas with dynamic values using st.latex
+    st.latex(rf"""
+    \text{{Unit Price \% Change}} = \frac{{\text{{Unit Price in Current Year}} - \text{{Unit Price in Previous Year}}}}{{\text{{Unit Price in Previous Year}}}} \times 100 = \frac{{{unit_price_current_year:.2f} - {unit_price_previous_year:.2f}}}{{{unit_price_previous_year:.2f}}} \times 100 = {unit_price_pct:.2f}\%
+    """)
+
+    st.latex(rf"""
+    \text{{Sales Volume \% Change}} = \frac{{\text{{Sales Volume in Current Year}} - \text{{Sales Volume in Previous Year}}}}{{\text{{Sales Volume in Previous Year}}}} \times 100 = \frac{{{sales_volume_current_year:.2f} - {sales_volume_previous_year:.2f}}}{{{sales_volume_previous_year:.2f}}} \times 100 = {sales_volume_pct:.2f}\%
+    """)
+
+    st.latex(rf"""
+    \text{{PE Coefficient}} = \frac{{\text{{Sales Volume \% Change}}}}{{\text{{Unit Price \% Change}}}} = \frac{{{sales_volume_pct:.2f}}}{{{unit_price_pct:.2f}}} = {pe_coeff:.2f}
+    """)
+    # Explanation Text
+    st.markdown(f"""
+    ### Explanation of Calculations (for {current_year_row['Region']}, {current_year_row['Itemtype']}, FY {current_year_row['Fy']}):
+    - **Unit Price Percentage Change**: The percentage change in unit price YOY. For FY {current_year_row['Fy']}, the unit price changed by **{unit_price_pct:.2f}%**.
+    - **Sales Volume Percentage Change**: The percentage change in sales volume YOY. For FY {current_year_row['Fy']}, the sales volume changed by **{sales_volume_pct:.2f}%**.
+    - **Price Elasticity (PE) Coefficient**: The PE coefficient for FY {current_year_row['Fy']} is **{pe_coeff:.2f}**.
+    """)
     # Plot the PE Coefficient with Plotly
     fig = px.line(
         agg_df_filtered,