Add subscription notice in Player ROO section and include a demo video link for enhanced user engagement

app.py

@@ -396,6 +396,7 @@ with tab1:
 with tab2:
     st.header("Player ROO")
     with st.expander("Info and Filters"):
+        st.info("In this demo you'll be able to see information and statistics, but names/teams have been redacted. To see more information, grab a subscription! (https://paydirtdfs.com/subscriptions-choices/)")
         with st.container():
             slate_type_var2 = st.radio("Which slate type are you loading?", ('Regular', 'Showdown'), key='slate_type_var2')
             slate_var2 = st.radio("Which slate data are you loading?", ('Main', 'Secondary', 'Auxiliary'), key='slate_var2')
@@ -628,314 +629,5 @@ with tab2:
 
 with tab3:
     st.header("Optimals")
-    with st.expander("Info and Filters"):
-        col1, col2, col3 = st.columns(3)
-        with col1:
-            slate_type_var3 = st.radio("Which slate type are you loading?", ('Regular', 'Showdown'), key='slate_type_var3')
-            if slate_type_var3 == 'Regular':
-                raw_baselines = roo_data
-            elif slate_type_var3 == 'Showdown':
-                raw_baselines = sd_roo_data
-            slate_var3 = st.radio("Which slate data are you loading?", ('Main', 'Secondary', 'Auxiliary'), key='slate_var3')
-            if slate_type_var3 == 'Regular':
-                if site_var == 'Draftkings':
-                    dk_lineups = init_DK_lineups(slate_type_var3, slate_var3)
-                elif site_var == 'Fanduel':
-                    fd_lineups = init_FD_lineups(slate_type_var3, slate_var3)
-            elif slate_type_var3 == 'Showdown':
-                if site_var == 'Draftkings':
-                    dk_lineups = init_DK_lineups(slate_type_var3, slate_var3)
-                elif site_var == 'Fanduel':
-                    fd_lineups = init_FD_lineups(slate_type_var3, slate_var3)
-        with col2:
-            lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1)
-            player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
-            if player_var1 == 'Specific Players':
-                player_var2 = st.multiselect('Which players do you want?', options = raw_baselines['Player'].unique())
-            elif player_var1 == 'Full Slate':
-                    player_var2 = raw_baselines.Player.values.tolist()
-        with col3:
-            if site_var == 'Draftkings':
-                salary_min_var = st.number_input("Minimum salary used", min_value = 0, max_value = 50000, value = 49000, step = 100, key = 'salary_min_var')
-                salary_max_var = st.number_input("Maximum salary used", min_value = 0, max_value = 50000, value = 50000, step = 100, key = 'salary_max_var')
-            elif site_var == 'Fanduel':
-                salary_min_var = st.number_input("Minimum salary used", min_value = 0, max_value = 35000, value = 34000, step = 100, key = 'salary_min_var')
-                salary_max_var = st.number_input("Maximum salary used", min_value = 0, max_value = 35000, value = 35000, step = 100, key = 'salary_max_var')
-        
-        
-        if site_var == 'Draftkings':
-            if slate_type_var3 == 'Regular':
-                ROO_slice = raw_baselines[raw_baselines['Site'] == 'Draftkings']
-                player_salaries = dict(zip(ROO_slice['Player'], ROO_slice['Salary']))
-                column_names = dk_columns
-            elif slate_type_var3 == 'Showdown':
-                player_salaries = dict(zip(raw_baselines['Player'], raw_baselines['Salary']))
-                column_names = dk_sd_columns
-            # Get the minimum and maximum ownership values from dk_lineups
-            min_own = np.min(dk_lineups[:,12])
-            max_own = np.max(dk_lineups[:,12])
-            
-                    
-        elif site_var == 'Fanduel':
-            raw_baselines = hold_display
-            if slate_type_var3 == 'Regular':
-                ROO_slice = raw_baselines[raw_baselines['Site'] == 'Fanduel']
-                player_salaries = dict(zip(ROO_slice['Player'], ROO_slice['Salary']))
-                column_names = fd_columns
-            elif slate_type_var3 == 'Showdown':
-                player_salaries = dict(zip(raw_baselines['Player'], raw_baselines['Salary']))
-                column_names = fd_sd_columns
-            # Get the minimum and maximum ownership values from dk_lineups
-            min_own = np.min(fd_lineups[:,11])
-            max_own = np.max(fd_lineups[:,11])
-
-        if st.button("Prepare full data export", key='data_export'):
-            name_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
-            data_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
-            if site_var == 'Draftkings':
-                if slate_type_var3 == 'Regular':
-                    map_columns = ['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']
-                elif slate_type_var3 == 'Showdown':
-                    map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
-                for col_idx in map_columns:
-                    data_export[col_idx] = data_export[col_idx].map(dk_id_map)
-            elif site_var == 'Fanduel':
-                if slate_type_var3 == 'Regular':
-                    map_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL']
-                elif slate_type_var3 == 'Showdown':
-                    map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4']
-                for col_idx in map_columns:
-                    data_export[col_idx] = data_export[col_idx].map(fd_id_map)
-            st.download_button(
-                label="Export optimals set (IDs)",
-                data=convert_df(data_export),
-                file_name='MLB_optimals_export.csv',
-                mime='text/csv',
-            )
-            st.download_button(
-                label="Export optimals set (Names)",
-                data=convert_df(name_export),
-                file_name='MLB_optimals_export.csv',
-                mime='text/csv',
-            )
-        
-    if site_var == 'Draftkings':
-        if 'working_seed' in st.session_state:
-            st.session_state.working_seed = st.session_state.working_seed
-            if player_var1 == 'Specific Players':
-                st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
-            elif player_var1 == 'Full Slate':
-                st.session_state.working_seed = dk_lineups.copy()
-            st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
-        elif 'working_seed' not in st.session_state:
-            st.session_state.working_seed = dk_lineups.copy()
-            st.session_state.working_seed = st.session_state.working_seed
-            if player_var1 == 'Specific Players':
-                st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
-            elif player_var1 == 'Full Slate':
-                st.session_state.working_seed = dk_lineups.copy()
-            st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
-        
-    elif site_var == 'Fanduel':
-        if 'working_seed' in st.session_state:
-            st.session_state.working_seed = st.session_state.working_seed
-            if player_var1 == 'Specific Players':
-                st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
-            elif player_var1 == 'Full Slate':
-                st.session_state.working_seed = fd_lineups.copy()
-            st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
-        elif 'working_seed' not in st.session_state:
-            st.session_state.working_seed = fd_lineups.copy()
-            st.session_state.working_seed = st.session_state.working_seed
-            if player_var1 == 'Specific Players':
-                st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
-            elif player_var1 == 'Full Slate':
-                st.session_state.working_seed = fd_lineups.copy()
-            st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
-    st.session_state.data_export_display = st.session_state.data_export_display[st.session_state.data_export_display['salary'] >= salary_min_var]
-    st.session_state.data_export_display = st.session_state.data_export_display[st.session_state.data_export_display['salary'] <= salary_max_var]
-    export_file = st.session_state.data_export_display.copy()
-    name_export = st.session_state.data_export_display.copy()
-    if site_var == 'Draftkings':
-        if slate_type_var3 == 'Regular':
-            map_columns = ['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']
-        elif slate_type_var3 == 'Showdown':
-            map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
-        for col_idx in map_columns:
-            export_file[col_idx] = export_file[col_idx].map(dk_id_map)
-    elif site_var == 'Fanduel':
-        if slate_type_var3 == 'Regular':
-            map_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL']
-        elif slate_type_var3 == 'Showdown':
-            map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4']
-        for col_idx in map_columns:
-            export_file[col_idx] = export_file[col_idx].map(fd_id_map)
-            
-    with st.container():
-        if st.button("Reset Optimals", key='reset3'):
-            for key in st.session_state.keys():
-                del st.session_state[key]
-            if site_var == 'Draftkings':
-                st.session_state.working_seed = dk_lineups.copy()
-            elif site_var == 'Fanduel':
-                st.session_state.working_seed = fd_lineups.copy()
-        if 'data_export_display' in st.session_state:
-            st.dataframe(st.session_state.data_export_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=500, use_container_width = True)
-        st.download_button(
-            label="Export display optimals (IDs)",
-            data=convert_df(export_file),
-            file_name='MLB_display_optimals.csv',
-            mime='text/csv',
-        )
-        st.download_button(
-            label="Export display optimals (Names)",
-            data=convert_df(name_export),
-            file_name='MLB_display_optimals.csv',
-            mime='text/csv',
-        )
-    
-    with st.container():
-        if slate_type_var3 == 'Regular':
-            if 'working_seed' in st.session_state:
-                # Create a new dataframe with summary statistics
-                if site_var == 'Draftkings':
-                    summary_df = pd.DataFrame({
-                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
-                        'Salary': [
-                            np.min(st.session_state.working_seed[:,10]),
-                            np.mean(st.session_state.working_seed[:,10]),
-                            np.max(st.session_state.working_seed[:,10]),
-                            np.std(st.session_state.working_seed[:,10])
-                        ],
-                        'Proj': [
-                            np.min(st.session_state.working_seed[:,11]),
-                            np.mean(st.session_state.working_seed[:,11]),
-                            np.max(st.session_state.working_seed[:,11]),
-                            np.std(st.session_state.working_seed[:,11])
-                        ],
-                        'Own': [
-                            np.min(st.session_state.working_seed[:,16]),
-                            np.mean(st.session_state.working_seed[:,16]),
-                            np.max(st.session_state.working_seed[:,16]),
-                            np.std(st.session_state.working_seed[:,16])
-                        ]
-                    })
-                elif site_var == 'Fanduel':
-                    summary_df = pd.DataFrame({
-                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
-                        'Salary': [
-                            np.min(st.session_state.working_seed[:,9]),
-                            np.mean(st.session_state.working_seed[:,9]),
-                            np.max(st.session_state.working_seed[:,9]),
-                            np.std(st.session_state.working_seed[:,9])
-                        ],
-                        'Proj': [
-                            np.min(st.session_state.working_seed[:,10]),
-                            np.mean(st.session_state.working_seed[:,10]),
-                            np.max(st.session_state.working_seed[:,10]),
-                            np.std(st.session_state.working_seed[:,10])
-                        ],
-                        'Own': [
-                            np.min(st.session_state.working_seed[:,15]),
-                            np.mean(st.session_state.working_seed[:,15]),
-                            np.max(st.session_state.working_seed[:,15]),
-                            np.std(st.session_state.working_seed[:,15])
-                        ]
-                    })
-
-                # Set the index of the summary dataframe as the "Metric" column
-                summary_df = summary_df.set_index('Metric')
-
-                # Display the summary dataframe
-                st.subheader("Optimal Statistics")
-                st.dataframe(summary_df.style.format({
-                    'Salary': '{:.2f}',
-                    'Proj': '{:.2f}',
-                    'Own': '{:.2f}'
-                }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own']), use_container_width=True)
-
-    with st.container():
-        tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"])
-        with tab1:
-            if 'data_export_display' in st.session_state:
-                if site_var == 'Draftkings':
-                    if slate_type_var3 == 'Regular':
-                        player_columns = st.session_state.data_export_display.iloc[:, :10]
-                    elif slate_type_var3 == 'Showdown':
-                        player_columns = st.session_state.data_export_display.iloc[:, :6]
-                elif site_var == 'Fanduel':
-                    if slate_type_var3 == 'Regular':
-                        player_columns = st.session_state.data_export_display.iloc[:, :9]
-                    elif slate_type_var3 == 'Showdown':
-                        player_columns = st.session_state.data_export_display.iloc[:, :5]
-                
-                # Flatten the DataFrame and count unique values
-                value_counts = player_columns.values.flatten().tolist()
-                value_counts = pd.Series(value_counts).value_counts()
-                
-                percentages = (value_counts / lineup_num_var * 100).round(2)
-                
-                # Create a DataFrame with the results
-                summary_df = pd.DataFrame({
-                    'Player': value_counts.index,
-                    'Frequency': value_counts.values,
-                    'Percentage': percentages.values
-                })
-                
-                # Sort by frequency in descending order
-                summary_df['Salary'] = summary_df['Player'].map(player_salaries)
-                summary_df = summary_df[['Player', 'Salary', 'Frequency', 'Percentage']]
-                summary_df = summary_df.sort_values('Frequency', ascending=False)
-                summary_df = summary_df.set_index('Player')
-                
-                # Display the table
-                st.write("Player Frequency Table:")
-                st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True)
-            
-                st.download_button(
-                    label="Export player frequency",
-                    data=convert_df_to_csv(summary_df),
-                    file_name='MLB_player_frequency.csv',
-                    mime='text/csv',
-                )
-        with tab2:
-            if 'working_seed' in st.session_state:
-                if site_var == 'Draftkings':
-                    if slate_type_var3 == 'Regular':
-                        player_columns = st.session_state.working_seed[:, :10]
-                    elif slate_type_var3 == 'Showdown':
-                        player_columns = st.session_state.working_seed[:, :7]
-                elif site_var == 'Fanduel':
-                    if slate_type_var3 == 'Regular':
-                        player_columns = st.session_state.working_seed[:, :9]
-                    elif slate_type_var3 == 'Showdown':
-                        player_columns = st.session_state.working_seed[:, :6]
-                
-                # Flatten the DataFrame and count unique values
-                value_counts = player_columns.flatten().tolist()
-                value_counts = pd.Series(value_counts).value_counts()
-                
-                percentages = (value_counts / len(st.session_state.working_seed) * 100).round(2)
-                # Create a DataFrame with the results
-                summary_df = pd.DataFrame({
-                    'Player': value_counts.index,
-                    'Frequency': value_counts.values,
-                    'Percentage': percentages.values
-                })
-                
-                # Sort by frequency in descending order
-                summary_df['Salary'] = summary_df['Player'].map(player_salaries)
-                summary_df = summary_df[['Player', 'Salary', 'Frequency', 'Percentage']]
-                summary_df = summary_df.sort_values('Frequency', ascending=False)
-                summary_df = summary_df.set_index('Player')
-                
-                # Display the table
-                st.write("Seed Frame Frequency Table:")
-                st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True)
-            
-                st.download_button(
-                    label="Export seed frame frequency",
-                    data=convert_df_to_csv(summary_df),
-                    file_name='MLB_seed_frame_frequency.csv',
-                    mime='text/csv',
-                )
+    VIDEO_URL = "https://www.youtube.com/watch?v=nCr-XKdXm0c"
+    st.video(VIDEO_URL)