Enhance UI/UX with custom Streamlit tab styling and reorganize Data Export tab layout

app.py

@@ -21,6 +21,37 @@ freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'}
 dk_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 fd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 
+st.markdown("""
+<style>
+    /* Tab styling */
+    .stTabs [data-baseweb="tab-list"] {
+        gap: 8px;
+        padding: 4px;
+    }
+
+    .stTabs [data-baseweb="tab"] {
+        height: 50px;
+        white-space: pre-wrap;
+        background-color: #FFD700;
+        color: white;
+        border-radius: 10px;
+        gap: 1px;
+        padding: 10px 20px;
+        font-weight: bold;
+        transition: all 0.3s ease;
+    }
+
+    .stTabs [aria-selected="true"] {
+        background-color: #DAA520;
+        color: white;
+    }
+
+    .stTabs [data-baseweb="tab"]:hover {
+        background-color: #DAA520;
+        cursor: pointer;
+    }
+</style>""", unsafe_allow_html=True)
+
 @st.cache_data(ttl = 599)
 def init_DK_seed_frames(sport, split): 
         if sport == 'NFL':
@@ -251,14 +282,13 @@ except:
 
 tab1, tab2 = st.tabs(['Contest Sims', 'Data Export'])
 with tab2:
-    col1, col2 = st.columns([1, 7])
-    with col1:
+    with st.expander("Info and Filters"):
         if st.button("Load/Reset Data", key='reset1'):
-              st.cache_data.clear()
-              for key in st.session_state.keys():
-                  del st.session_state[key]
-              dk_raw, fd_raw = init_baselines('NFL')
-        
+            st.cache_data.clear()
+            for key in st.session_state.keys():
+                del st.session_state[key]
+            dk_raw, fd_raw = init_baselines('NFL')
+    
         sport_var1 = st.radio("What sport are you working with?", ('NBA', 'NFL'), key='sport_var1')
         slate_var1 = st.radio("Which data are you loading?", ('Showdown', 'Secondary Showdown', 'Auxiliary Showdown'), key='slate_var1')
         sharp_split_var = st.number_input("How many lineups do you want?", value=10000, max_value=500000, min_value=10000, step=10000)
@@ -348,70 +378,69 @@ with tab2:
                 file_name='NFL_SD_optimals_export.csv',
                     mime='text/csv',
                 )
+        
+    if st.button("Load Data", key='load_data'):
+        if site_var1 == 'Draftkings':
+            if 'working_seed' in st.session_state:
+                st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], team_var2)]
+                st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 10], stack_var2)]
+                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
+            elif 'working_seed' not in st.session_state:
+                if slate_var1 == 'Showdown':
+                    st.session_state.working_seed = init_DK_seed_frames(sport_var1, sharp_split_var)
+                    if sport_var1 == 'NFL':
+                        export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    elif sport_var1 == 'NBA':
+                        export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                elif slate_var1 == 'Secondary Showdown':
+                    st.session_state.working_seed = init_DK_secondary_seed_frames(sport_var1, sharp_split_var)
+                    if sport_var1 == 'NFL':
+                        export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    elif sport_var1 == 'NBA':
+                        export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                elif slate_var1 == 'Auxiliary Showdown':
+                    st.session_state.working_seed = init_DK_auxiliary_seed_frames(sport_var1, sharp_split_var)
+                    if sport_var1 == 'NFL':
+                        export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    elif sport_var1 == 'NBA':
+                        export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], team_var2)]
+                st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 10], stack_var2)]
+                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
             
-    with col2:
-        if st.button("Load Data", key='load_data'):
-            if site_var1 == 'Draftkings':
-                if 'working_seed' in st.session_state:
-                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], team_var2)]
-                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 10], stack_var2)]
-                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
-                elif 'working_seed' not in st.session_state:
-                    if slate_var1 == 'Showdown':
-                        st.session_state.working_seed = init_DK_seed_frames(sport_var1, sharp_split_var)
-                        if sport_var1 == 'NFL':
-                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-                        elif sport_var1 == 'NBA':
-                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-                    elif slate_var1 == 'Secondary Showdown':
-                        st.session_state.working_seed = init_DK_secondary_seed_frames(sport_var1, sharp_split_var)
-                        if sport_var1 == 'NFL':
-                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-                        elif sport_var1 == 'NBA':
-                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-                    elif slate_var1 == 'Auxiliary Showdown':
-                        st.session_state.working_seed = init_DK_auxiliary_seed_frames(sport_var1, sharp_split_var)
-                        if sport_var1 == 'NFL':
-                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-                        elif sport_var1 == 'NBA':
-                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], team_var2)]
-                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 10], stack_var2)]
-                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
-                
-            elif site_var1 == 'Fanduel':
-                if 'working_seed' in st.session_state:
-                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 8], team_var2)]
-                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)]
-                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
-                elif 'working_seed' not in st.session_state:
-                    if slate_var1 == 'Showdown':
-                        st.session_state.working_seed = init_FD_seed_frames(sport_var1, sharp_split_var)
-                        if sport_var1 == 'NFL':
-                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-                        elif sport_var1 == 'NBA':
-                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-                    elif slate_var1 == 'Secondary Showdown':
-                        st.session_state.working_seed = init_FD_secondary_seed_frames(sport_var1, sharp_split_var)
-                        if sport_var1 == 'NFL':
-                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-                        elif sport_var1 == 'NBA':
-                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-                    elif slate_var1 == 'Auxiliary Showdown':
-                        st.session_state.working_seed = init_FD_auxiliary_seed_frames(sport_var1, sharp_split_var)
-                        if sport_var1 == 'NFL':
-                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-                        elif sport_var1 == 'NBA':
-                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-                    raw_baselines = fd_raw
-                    column_names = fd_columns
-                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 8], team_var2)]
-                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)]
-                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
-                
-        with st.container():
-            if 'data_export_display' in st.session_state:
-                st.dataframe(st.session_state.data_export_display.style.format(freq_format, precision=2), use_container_width = True)
+        elif site_var1 == 'Fanduel':
+            if 'working_seed' in st.session_state:
+                st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 8], team_var2)]
+                st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)]
+                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
+            elif 'working_seed' not in st.session_state:
+                if slate_var1 == 'Showdown':
+                    st.session_state.working_seed = init_FD_seed_frames(sport_var1, sharp_split_var)
+                    if sport_var1 == 'NFL':
+                        export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    elif sport_var1 == 'NBA':
+                        export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                elif slate_var1 == 'Secondary Showdown':
+                    st.session_state.working_seed = init_FD_secondary_seed_frames(sport_var1, sharp_split_var)
+                    if sport_var1 == 'NFL':
+                        export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    elif sport_var1 == 'NBA':
+                        export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                elif slate_var1 == 'Auxiliary Showdown':
+                    st.session_state.working_seed = init_FD_auxiliary_seed_frames(sport_var1, sharp_split_var)
+                    if sport_var1 == 'NFL':
+                        export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    elif sport_var1 == 'NBA':
+                        export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                raw_baselines = fd_raw
+                column_names = fd_columns
+                st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 8], team_var2)]
+                st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)]
+                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
+            
+    with st.container():
+        if 'data_export_display' in st.session_state:
+            st.dataframe(st.session_state.data_export_display.style.format(freq_format, precision=2), use_container_width = True)
             
 with tab1:
     col1, col2 = st.columns([1, 7])