visual update to segmented control

src/streamlit_app.py

@@ -26,33 +26,38 @@ sim_all_hold = pd.DataFrame(columns=['Player', 'Team', 'Book', 'Prop Type', 'Pro
 st.markdown("""
 <style>
     /* Tab styling */
-    .stTabs [data-baseweb="tab-list"] {
-        gap: 8px;
+    .stElementContainer [data-baseweb="button-group"] {
+        gap: 2.000rem;
         padding: 4px;
     }
-
-    .stTabs [data-baseweb="tab"] {
-        height: 50px;
+    .stElementContainer [kind="segmented_control"] {
+        height: 2.000rem;
         white-space: pre-wrap;
         background-color: #DAA520;
         color: white;
-        border-radius: 10px;
+        border-radius: 20px;
         gap: 1px;
         padding: 10px 20px;
         font-weight: bold;
         transition: all 0.3s ease;
     }
-
-    .stTabs [aria-selected="true"] {
+    .stElementContainer [kind="segmented_controlActive"] {
+        height: 3.000rem;
         background-color: #DAA520;
         border: 3px solid #FFD700;
-        color: white;
+        border-radius: 10px;
+        color: black;
     }
-
-    .stTabs [data-baseweb="tab"]:hover {
+    .stElementContainer [kind="segmented_control"]:hover {
         background-color: #FFD700;
         cursor: pointer;
     }
+
+    div[data-baseweb="select"] > div {
+        background-color: #DAA520;
+        color: white;
+    }
+
 </style>""", unsafe_allow_html=True)
 
 def calculate_poisson(row):
@@ -179,9 +184,17 @@ def convert_df_to_csv(df):
 game_model, raw_baselines, player_stats, prop_frame, pick_frame, market_props, timestamp = init_baselines()
 t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 
-tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(["Game Betting Model", 'Prop Market', "Player Projections", "Prop Trend Table", "Player Prop Simulations", "Stat Specific Simulations"])
-
-with tab1:
+selected_tab = st.segmented_control(
+    "Select Tab",
+    options=["Game Betting Model", 'Prop Market', "Player Projections", "Prop Trend Table", "Player Prop Simulations", "Stat Specific Simulations"],
+    selection_mode='single',
+    default='Game Betting Model',
+    width='stretch',
+    label_visibility='collapsed',
+    key='tab_selector'
+)
+
+if selected_tab == 'Game Betting Model':
     with st.expander("Info and Filters"):
         st.info(t_stamp)
         if st.button("Reset Data", key='reset1'):
@@ -207,7 +220,7 @@ with tab1:
         key='team_export',
     )
 
-with tab2:
+if selected_tab == 'Prop Market':
     with st.expander("Info and Filters"):
         st.info(t_stamp)
         if st.button("Reset Data", key='reset2'):
@@ -243,7 +256,7 @@ with tab2:
         mime='text/csv',
     )
 
-with tab3:
+if selected_tab == 'Player Projections':
     with st.expander("Info and Filters"):
         st.info(t_stamp)
         if st.button("Reset Data", key='reset3'):
@@ -266,7 +279,7 @@ with tab3:
         mime='text/csv',
     )
     
-with tab4:
+if selected_tab == 'Prop Trend Table':
     with st.expander("Info and Filters"):
         st.info(t_stamp)
         if st.button("Reset Data", key='reset4'):
@@ -296,7 +309,7 @@ with tab4:
         mime='text/csv',
     )
     
-with tab5:
+if selected_tab == 'Player Prop Simulations':
     st.info(t_stamp)
     if st.button("Reset Data", key='reset5'):
               st.cache_data.clear()
@@ -443,7 +456,7 @@ with tab5:
                             plot_hold_container = st.empty()
                             st.plotly_chart(fig, use_container_width=True)
 
-with tab6:
+if selected_tab == 'Stat Specific Simulations':
     st.info(t_stamp)
     st.info('The Over and Under percentages are a compositve percentage based on simulations, historical performance, and implied probabilities, and may be different than you would expect based purely on the median projection. Likewise, the Edge of a bet is not the only indicator of if you should make the bet or not as the suggestion is using a base acceptable threshold to determine how much edge you should have for each stat category.')
     if st.button("Reset Data/Load Data", key='reset6'):