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app.py

@@ -9,6 +9,15 @@ def load_data():
     # Load pre-processed data from csv file
     df = pd.read_csv("processed_data.csv")  # replace with your dataset
 
+   # Explicitly cast metric columns to numeric types after reading from CSV
+    try:
+        df['total_price'] = df['total_price'].astype(float)
+        df['quantity'] = df['quantity'].astype(int)
+    except Exception as e:
+        # Fallback for logging if a type conversion fails unexpectedly
+        print(f"Error during type casting in app.py: {e}")
+
+
     # Ensure order_time is treated as datetime for plots (important since it loses dtype during CSV save/load)
     if 'order_time' in df.columns:
         df['order_time'] = pd.to_datetime(df['order_time'])
@@ -34,6 +43,7 @@ def app():
     total_revenue = df['total_price'].sum()
     most_popular_pizza_size = df['pizza_size'].value_counts().idxmax()
     most_frequent_pizza_category = df['pizza_category'].value_counts().idxmax()
+    most_popular_pizza_name = df['pizza_name'].value_counts().idxmax()
     total_pizzas_sold = df['quantity'].sum()
              
 
@@ -49,6 +59,7 @@ def app():
     st.sidebar.metric("Total Revenue", f"${total_revenue:,.2f}")
     st.sidebar.metric("Most Popular Pizza Size", most_popular_pizza_size )
     st.sidebar.metric("Most Popular Pizza Category", most_frequent_pizza_category)
+    st.sidebar.metric("Most Popular Pizza Name", most_popular_pizza_name)
     st.sidebar.metric("Total Pizzas Sold", total_pizzas_sold)
 
   
@@ -98,10 +109,16 @@ def app():
         # Axis and Final Display Cleanup
         ax.set_xlabel(" ".join(plot["x"].split("_")).title())
         
-        if "y" in plot.keys() and plot["y"] != "quantity":
-            ax.set_ylabel(" ".join(plot["y"].split("_")).title())
-        elif plot["type"] != "count":
-             ax.set_ylabel("Quantity")
+        if "y" in plot.keys():
+            # Customize y-label for revenue plots
+            if plot["title"] == "Monthly Revenue Trends by Pizza Category":
+                 ax.set_ylabel("Total Revenue (USD)")
+            elif plot["y"] != "quantity":
+                ax.set_ylabel(" ".join(plot["y"].split("_")).title())
+            else:
+                 ax.set_ylabel("Quantity Sold")
+        elif plot["type"] == "count":
+             ax.set_ylabel("Order Count")
         
         st.pyplot(fig)
         plt.close(fig) # Close the figure to free up memory