Refactored the page into smaller functions to make it more modular and easier to follow and maintain.

pages/020_Data_Browser.py

@@ -5,100 +5,109 @@ import plotly.express as px
 from src.st_helpers import st_setup
 from src.datatypes import *
 
-if st_setup('LLM Arch'):
-    if not DataLoader.loaded:
-        DataLoader.load_data()
 
-    summary = st.container()
-    with summary:
-        st.write("# Data Browser")
-
-        sumcol1, sumcol2 = st.columns([1, 3])
-
-        with sumcol1:
-            dbs = DataLoader.available_dbs()
-            if len(dbs) > 1:
-                st.write(f'**:1234: Database Selector**')
-                idx = dbs.index(DataLoader.active_db)
-                DataLoader.set_db_name(st.selectbox("Connected to:", dbs, index=idx, label_visibility="collapsed"))
-
-            st.write(f'**:1234: Summary Statistics**')
-            summary = f'- **{len(Category.all):,}** categories'
-            summary += f'\n- **{len(Product.all):,}** products'
-            summary += f'\n- **{len(Feature.all):,}** features'
-            summary += f'\n- **{len(Review.all):,}** reviews'
-            st.markdown(summary)
-            if st.button('Force data reload'):
-                DataLoader.load_data(True)
-                st.rerun()
-
-        with sumcol2:
-            cats = Category.all_sorted()
-
-            with st.expander("**Review Counts**"):
-                category_names = [c.name for c in cats]
-                category_review_counts = [sum([p.review_count for p in c.products]) for c in cats]
-                data = zip(category_names, category_review_counts)
-                df = pd.DataFrame(data, columns=["Category", "Review Count"])
-                st.bar_chart(df, x="Category", y="Review Count")
-
-            with st.expander("**Product Ratings**"):
-                data = []
-                for c in cats:
-                    for p in c.products:
-                        data.append([c.name, p.average_rating])
-                df = pd.DataFrame(data, columns=['Category', 'Mean Product Rating'])
-                fig = px.box(df, x="Category", y="Mean Product Rating")
-                fig.update_xaxes(tickangle=-90)
-                st.plotly_chart(fig, use_container_width=True)
-
-            with st.expander("**Product Prices**"):
-                data = []
-                for c in cats:
-                    for p in c.products:
-                        data.append([c.name, p.price])
-                df = pd.DataFrame(data, columns=['Category', 'Price'])
-                fig = px.box(df, x="Category", y="Price")
-                fig.update_xaxes(tickangle=-90)
-                st.plotly_chart(fig, use_container_width=True)
-
-    subhead = st.container()
-
-    col1, col2 = st.columns([1, 3])
-
-    with col1:
+def show_db_selector_and_summary_in_container(container) -> None:
+    with container:
+        dbs = DataLoader.available_dbs()
+        if len(dbs) > 1:
+            st.write(f'**:1234: Database Selector**')
+            idx = dbs.index(DataLoader.active_db)
+            DataLoader.set_db_name(st.selectbox("Connected to:", dbs, index=idx, label_visibility="collapsed"))
+
+        st.write(f'**:1234: Summary Statistics**')
+        summary = f'- **{len(Category.all):,}** categories'
+        summary += f'\n- **{len(Product.all):,}** products'
+        summary += f'\n- **{len(Feature.all):,}** features'
+        summary += f'\n- **{len(Review.all):,}** reviews'
+        st.markdown(summary)
+        if st.button('Force data reload'):
+            DataLoader.load_data(True)
+            st.rerun()
+
+
+def show_data_summary_charts_in_container(container) -> None:
+    with container:
+        cats = Category.all_sorted()
 
+        with st.expander("**Review Counts**"):
+            category_names = [c.name for c in cats]
+            category_review_counts = [sum([p.review_count for p in c.products]) for c in cats]
+            data = zip(category_names, category_review_counts)
+            df = pd.DataFrame(data, columns=["Category", "Review Count"])
+            st.bar_chart(df, x="Category", y="Review Count")
+
+        with st.expander("**Product Ratings**"):
+            data = []
+            for c in cats:
+                for p in c.products:
+                    data.append([c.name, p.average_rating])
+            df = pd.DataFrame(data, columns=['Category', 'Mean Product Rating'])
+            fig = px.box(df, x="Category", y="Mean Product Rating")
+            fig.update_xaxes(tickangle=-90)
+            st.plotly_chart(fig, use_container_width=True)
+
+        with st.expander("**Product Prices**"):
+            data = []
+            for c in cats:
+                for p in c.products:
+                    data.append([c.name, p.price])
+            df = pd.DataFrame(data, columns=['Category', 'Price'])
+            fig = px.box(df, x="Category", y="Price")
+            fig.update_xaxes(tickangle=-90)
+            st.plotly_chart(fig, use_container_width=True)
+
+
+def show_top_section() -> None:
+    """
+    Writes the top section to the streamlit page, showing the currently selected database
+    and some associated summary numbers and charts
+    """
+    top_section = st.container()
+    with top_section:
+        summary_left, summary_right = st.columns([1, 3])
+        show_db_selector_and_summary_in_container(summary_left)
+        show_data_summary_charts_in_container(summary_right)
+
+
+def get_user_selected_category(container) -> Category:
+    """
+    Show a selector to pick a category and return the selected category
+    """
+    with container:
         st.write('**Category**')
         cats = Category.all_sorted()
         options = [f"{c.name}" for c in cats]
         selection = st.radio("**Category**", options, label_visibility="collapsed")
+        return Category.by_name(selection)
 
-    selected_category = Category.by_name(selection)
 
-    with subhead:
-        st.write(f'### {selection}')
-
-    with col2:
-        features = [f.name for f in selected_category.features]
+def show_category_datatable_in_container(category, container) -> None:
+    """
+    Displays a category of products into a given container.  Shows the products themselvses
+    as a table and then if any products are ticked to show the reviews it displays the associated reviews
+    also.
+    """
+    with container:
+        features = [f.name for f in category.features]
         features.sort()
-        st.write(f"**{selection[:-1]} Features ({len(features)}):**")
+        st.write(f"**{category.singular_name} Features ({len(features)}):**")
         st.write('; '.join(features))
 
-        prod_index = [p.id for p in selected_category.products]
+        prod_index = [p.id for p in category.products]
         prod_data = [[p.name, p.price, p.feature_count, ', '.join([str(f) for f in p.features]), p.review_count,
-                      p.average_rating, False, p.description] for p in selected_category.products]
+                      p.average_rating, False, p.description] for p in category.products]
         prod_columns = ['Name', 'Price', 'Feature Count', 'Features', 'Review Count', 'Average Rating', 'Show Reviews?',
                         'Description']
         prod_df = pd.DataFrame(prod_data, index=prod_index, columns=prod_columns)
-        total_reviews = sum([p.review_count for p in selected_category.products])
-        st.write(f"**{selection} ({len(prod_index)}). Having {total_reviews} reviews in total:**")
+        total_reviews = sum([p.review_count for p in category.products])
+        st.write(f"**{category.name} ({len(prod_index)}). Having {total_reviews} reviews in total:**")
         edited_df = st.data_editor(prod_df, disabled=(
         'Name', 'Price', 'Feature Count', 'Features', 'Review Count', 'Average Rating', 'Description'))
 
         selected_product_count = edited_df['Show Reviews?'].sum()
         selected_review_count = edited_df[edited_df['Show Reviews?']]['Review Count'].sum()
 
-        st.write(f"**{selection[:-1]} Reviews ({selected_review_count} from {selected_product_count} products):**")
+        st.write(f"**{category.singular_name} Reviews ({selected_review_count} from {selected_product_count} products):**")
         if selected_review_count > 0:
             selected_products = list(edited_df[edited_df['Show Reviews?']].index)
             products = Product.for_ids(selected_products)
@@ -112,4 +121,24 @@ if st_setup('LLM Arch'):
             rev_df = pd.DataFrame(rev_data, index=rev_index, columns=rev_columns)
             st.dataframe(rev_df, width=10000)
         else:
-            st.write("Check boxes in the table above to see reviews for products.")
+            st.write("Check boxes in the table above to see reviews for products.")
+
+
+def show_bottom_section() -> None:
+    # Set up space
+    selected_category_sub_heading = st.container()
+    category_col, datatable_col = st.columns([1, 3])
+
+    # Display into containers
+    selected_category = get_user_selected_category(category_col)
+    with selected_category_sub_heading:
+        st.write(f'### {selected_category.name}')
+    show_category_datatable_in_container(selected_category, datatable_col)
+
+
+if st_setup('LLM Arch'):
+    if not DataLoader.loaded:
+        DataLoader.load_data()
+    st.write("# Data Browser")
+    show_top_section()
+    show_bottom_section()