Enhance player filtering and metric calculations in app.py

- Reorganized the player selection form within an expander for better user experience.
- Added logic to calculate 'stack' and 'stack_size' metrics based on player data for the 'Classic' game type, improving data analysis capabilities.
- Streamlined the application of filters to ensure accurate data processing and user interaction.

app.py

@@ -4,7 +4,7 @@ import numpy as np
 import pandas as pd
 import time
 from fuzzywuzzy import process
-import random
+from collections import Counter
 
 ## import global functions
 from global_func.clean_player_name import clean_player_name
@@ -101,18 +101,33 @@ with tab2:
         working_df = st.session_state['Contest'].copy()
 
         with col1:
-            with st.form(key='filter_form'):
-                type_var = st.selectbox("Select Game Type", ['Classic', 'Showdown'])
-                entry_parse_var = st.selectbox("Do you want to view a specific player(s) or a group of players?", ['All', 'Specific'])
-                entry_names = st.multiselect("Select players", options=st.session_state['entry_list'], default=[])
-                submitted = st.form_submit_button("Submit")
-                if submitted:
-                    # Apply entry name filter if specific entries are selected
-                    if entry_parse_var == 'Specific' and entry_names:
-                        working_df = working_df[working_df['BaseName'].isin(entry_names)]
+            with st.expander("Info and filters"):
+                with st.form(key='filter_form'):
+                    type_var = st.selectbox("Select Game Type", ['Classic', 'Showdown'])
+                    entry_parse_var = st.selectbox("Do you want to view a specific player(s) or a group of players?", ['All', 'Specific'])
+                    entry_names = st.multiselect("Select players", options=st.session_state['entry_list'], default=[])
+                    submitted = st.form_submit_button("Submit")
+                    if submitted:
+                        # Apply entry name filter if specific entries are selected
+                        if entry_parse_var == 'Specific' and entry_names:
+                            working_df = working_df[working_df['BaseName'].isin(entry_names)]
 
         # Calculate metrics based on game type
         if type_var == 'Classic':
+            working_df['stack'] = working_df.apply(
+                lambda row: Counter(
+                    map_dict['team_map'].get(player, '') for player in row
+                    if map_dict['team_map'].get(player, '') != ''
+                ).most_common(1)[0][0] if any(map_dict['team_map'].get(player, '') for player in row) else '',
+                axis=1
+            )
+            working_df['stack_size'] = working_df.apply(
+                lambda row: Counter(
+                    map_dict['team_map'].get(player, '') for player in row
+                    if map_dict['team_map'].get(player, '') != ''
+                ).most_common(1)[0][1] if any(map_dict['team_map'].get(player, '') for player in row) else '',
+                axis=1
+            )
             working_df['salary'] = working_df.apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
             working_df['median'] = working_df.apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
             working_df['Own'] = working_df.apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)