Revert

app.py

@@ -29,37 +29,6 @@ player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_fi
 dk_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', 'Own']
 fd_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', '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_resource(ttl = 60)
 def init_baselines():
     collection = db["PGA_Range_of_Outcomes"] 
@@ -151,31 +120,30 @@ salaryCut = 0
 tab1, tab2 = st.tabs(["Player Overall Projections", "Optimals and Exposures"])
 
 with tab1:
-    with st.expander("Info and Filters"):
-        if st.button("Reset Data", key='reset1'):
-                # Clear values from *all* all in-memory and on-disk data caches:
-                # i.e. clear values from both square and cube
-                st.cache_data.clear()
-                roo_data, sd_roo_data, timestamp = init_baselines()
-                dk_lineups = init_DK_lineups('Regular')
-                fd_lineups = init_FD_lineups('Regular')
-                hold_display = roo_data
-                for key in st.session_state.keys():
-                    del st.session_state[key]
+    if st.button("Reset Data", key='reset1'):
+            # Clear values from *all* all in-memory and on-disk data caches:
+            # i.e. clear values from both square and cube
+            st.cache_data.clear()
+            roo_data, sd_roo_data, timestamp = init_baselines()
+            dk_lineups = init_DK_lineups('Regular')
+            fd_lineups = init_FD_lineups('Regular')
+            hold_display = roo_data
+            for key in st.session_state.keys():
+                del st.session_state[key]
 
-        st.write(timestamp)
-        info_container = st.empty()
-        options_container = st.empty()
-        hold_container = st.empty()
+    st.write(timestamp)
+    info_container = st.empty()
+    options_container = st.empty()
+    hold_container = st.empty()
 
-        with options_container:
-            col1, col2, col3 = st.columns(3)
-            with col1:
-                view_var = st.radio("Select a View", ["Simple", "Advanced"])
-            with col2:
-                site_var = st.radio("Select a Site", ["Draftkings", "FanDuel"])
-            with col3:
-                type_var = st.radio("Select a Type", ["Full Slate", "Showdown"])
+    with options_container:
+        col1, col2, col3 = st.columns(3)
+        with col1:
+            view_var = st.radio("Select a View", ["Simple", "Advanced"])
+        with col2:
+            site_var = st.radio("Select a Site", ["Draftkings", "FanDuel"])
+        with col3:
+            type_var = st.radio("Select a Type", ["Full Slate", "Showdown"])
     with hold_container:
         if type_var == "Full Slate":
             display = hold_display[hold_display['Site'] == site_var]
@@ -205,7 +173,8 @@ with tab1:
     )
 
 with tab2:
-    with st.expander("Info and Filters"):
+    col1, col2 = st.columns([1, 7])
+    with col1:
         if st.button("Load/Reset Data", key='reset2'):
             st.cache_data.clear()
             roo_data, sd_roo_data, timestamp = init_baselines()
@@ -284,195 +253,196 @@ with tab2:
                 file_name='NBA_optimals_export.csv',
                 mime='text/csv',
             )
+    with col2:
         
-    if site_var1 == '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':
+        if site_var1 == '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.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
-        
-    elif site_var1 == '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)
-
-    export_file = st.session_state.data_export_display.copy()
-    # if site_var1 == 'Draftkings':
-    #     for col_idx in range(6):
-    #         export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
-    # elif site_var1 == 'Fanduel':
-    #     for col_idx in range(6):
-    #         export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
+                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)
             
-    with st.container():
-        if st.button("Reset Optimals", key='reset3'):
-            for key in st.session_state.keys():
-                del st.session_state[key]
-            if site_var1 == 'Draftkings':
-                st.session_state.working_seed = dk_lineups.copy()
-            elif site_var1 == 'Fanduel':
+        elif site_var1 == '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()
-        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",
-            data=convert_df(export_file),
-            file_name='NBA_display_optimals.csv',
-            mime='text/csv',
-        )
-    
-    with st.container():
-        if 'working_seed' in st.session_state:
-            # Create a new dataframe with summary statistics
-            if site_var1 == 'Draftkings':
-                summary_df = pd.DataFrame({
-                    'Metric': ['Min', 'Average', 'Max', 'STDdev'],
-                    'Salary': [
-                        np.min(st.session_state.working_seed[:,6]),
-                        np.mean(st.session_state.working_seed[:,6]),
-                        np.max(st.session_state.working_seed[:,6]),
-                        np.std(st.session_state.working_seed[:,6])
-                    ],
-                    'Proj': [
-                        np.min(st.session_state.working_seed[:,7]),
-                        np.mean(st.session_state.working_seed[:,7]),
-                        np.max(st.session_state.working_seed[:,7]),
-                        np.std(st.session_state.working_seed[:,7])
-                    ],
-                    'Own': [
-                        np.min(st.session_state.working_seed[:,8]),
-                        np.mean(st.session_state.working_seed[:,8]),
-                        np.max(st.session_state.working_seed[:,8]),
-                        np.std(st.session_state.working_seed[:,8])
-                    ]
-                })
-            elif site_var1 == 'Fanduel':
-                summary_df = pd.DataFrame({
-                    'Metric': ['Min', 'Average', 'Max', 'STDdev'],
-                    'Salary': [
-                        np.min(st.session_state.working_seed[:,6]),
-                        np.mean(st.session_state.working_seed[:,6]),
-                        np.max(st.session_state.working_seed[:,6]),
-                        np.std(st.session_state.working_seed[:,6])
-                    ],
-                    'Proj': [
-                        np.min(st.session_state.working_seed[:,7]),
-                        np.mean(st.session_state.working_seed[:,7]),
-                        np.max(st.session_state.working_seed[:,7]),
-                        np.std(st.session_state.working_seed[:,7])
-                    ],
-                    'Own': [
-                        np.min(st.session_state.working_seed[:,8]),
-                        np.mean(st.session_state.working_seed[:,8]),
-                        np.max(st.session_state.working_seed[:,8]),
-                        np.std(st.session_state.working_seed[:,8])
-                    ]
-                })
-
-            # Set the index of the summary dataframe as the "Metric" column
-            summary_df = summary_df.set_index('Metric')
+                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)
 
-            # 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:
+        export_file = st.session_state.data_export_display.copy()
+        # if site_var1 == 'Draftkings':
+        #     for col_idx in range(6):
+        #         export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
+        # elif site_var1 == 'Fanduel':
+        #     for col_idx in range(6):
+        #         export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict)
+                
+        with st.container():
+            if st.button("Reset Optimals", key='reset3'):
+                for key in st.session_state.keys():
+                    del st.session_state[key]
                 if site_var1 == 'Draftkings':
-                    player_columns = st.session_state.data_export_display.iloc[:, :6]
+                    st.session_state.working_seed = dk_lineups.copy()
                 elif site_var1 == 'Fanduel':
-                    player_columns = st.session_state.data_export_display.iloc[:, :6]
-                
-                # 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='PGA_player_frequency.csv',
-                    mime='text/csv',
-                )
-        with tab2:
+                    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",
+                data=convert_df(export_file),
+                file_name='NBA_display_optimals.csv',
+                mime='text/csv',
+            )
+        
+        with st.container():
             if 'working_seed' in st.session_state:
+                # Create a new dataframe with summary statistics
                 if site_var1 == 'Draftkings':
-                    player_columns = st.session_state.working_seed[:, :6]
+                    summary_df = pd.DataFrame({
+                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                        'Salary': [
+                            np.min(st.session_state.working_seed[:,6]),
+                            np.mean(st.session_state.working_seed[:,6]),
+                            np.max(st.session_state.working_seed[:,6]),
+                            np.std(st.session_state.working_seed[:,6])
+                        ],
+                        'Proj': [
+                            np.min(st.session_state.working_seed[:,7]),
+                            np.mean(st.session_state.working_seed[:,7]),
+                            np.max(st.session_state.working_seed[:,7]),
+                            np.std(st.session_state.working_seed[:,7])
+                        ],
+                        'Own': [
+                            np.min(st.session_state.working_seed[:,8]),
+                            np.mean(st.session_state.working_seed[:,8]),
+                            np.max(st.session_state.working_seed[:,8]),
+                            np.std(st.session_state.working_seed[:,8])
+                        ]
+                    })
                 elif site_var1 == 'Fanduel':
-                    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
-                })
+                    summary_df = pd.DataFrame({
+                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                        'Salary': [
+                            np.min(st.session_state.working_seed[:,6]),
+                            np.mean(st.session_state.working_seed[:,6]),
+                            np.max(st.session_state.working_seed[:,6]),
+                            np.std(st.session_state.working_seed[:,6])
+                        ],
+                        'Proj': [
+                            np.min(st.session_state.working_seed[:,7]),
+                            np.mean(st.session_state.working_seed[:,7]),
+                            np.max(st.session_state.working_seed[:,7]),
+                            np.std(st.session_state.working_seed[:,7])
+                        ],
+                        'Own': [
+                            np.min(st.session_state.working_seed[:,8]),
+                            np.mean(st.session_state.working_seed[:,8]),
+                            np.max(st.session_state.working_seed[:,8]),
+                            np.std(st.session_state.working_seed[:,8])
+                        ]
+                    })
+
+                # 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_var1 == 'Draftkings':
+                        player_columns = st.session_state.data_export_display.iloc[:, :6]
+                    elif site_var1 == 'Fanduel':
+                        player_columns = st.session_state.data_export_display.iloc[:, :6]
+                    
+                    # 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)
                 
-                # 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')
+                    st.download_button(
+                        label="Export player frequency",
+                        data=convert_df_to_csv(summary_df),
+                        file_name='PGA_player_frequency.csv',
+                        mime='text/csv',
+                    )
+            with tab2:
+                if 'working_seed' in st.session_state:
+                    if site_var1 == 'Draftkings':
+                        player_columns = st.session_state.working_seed[:, :6]
+                    elif site_var1 == 'Fanduel':
+                        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)
                 
-                # 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='PGA_seed_frame_frequency.csv',
-                    mime='text/csv',
-                )
+                    st.download_button(
+                        label="Export seed frame frequency",
+                        data=convert_df_to_csv(summary_df),
+                        file_name='PGA_seed_frame_frequency.csv',
+                        mime='text/csv',
+                    )