Implement player portfolio filtering and display in `app.py`

- Added a new form for users to filter player data by game type and specific players, enhancing user interaction.
- Introduced logic to calculate salary, median projections, and ownership based on selected game type (Classic or Showdown).
- Updated the display of the portfolio dataframe with improved styling and pagination for better data visualization.

app.py

@@ -78,9 +78,70 @@ with tab1:
             
             # Update projections_df with any new matches
             st.session_state['contest_df'], st.session_state['projections_df'] = find_name_mismatches(st.session_state['Contest'], st.session_state['projections_df'])
-    st.dataframe(st.session_state['contest_df'].head(100))
-    st.dataframe(st.session_state['projections_df'].head(100))
 
 with tab2:
     if st.button('Clear data', key='reset3'):
         st.session_state.clear()
+    if 'portfolio' in st.session_state and 'projections_df' in st.session_state:
+        col1, col2 = st.columns([1, 8])
+        excluded_cols = ['BaseName', 'EntryCount']
+        with col1:
+            with st.form(key='filter_form'):
+                type_var = st.selectbox("Select Game Type", ['Classic', 'Showdown'])
+                player_parse_var = st.selectbox("Do you want to view a specific player(s) or a group of players?", ['All', 'Specific'])
+                if player_parse_var == 'Specific':
+                    player_names = st.multiselect("Select players", options=st.session_state['entry_list'])
+                else:
+                    player_names = st.session_state['entry_list']
+                submitted = st.form_submit_button("Submit")
+
+
+        map_dict = {
+            'pos_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])),
+            'team_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team'])),
+            'salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
+            'proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'])),
+            'own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'])),
+            'own_percent_rank':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['ownership'].rank(pct=True))),
+            'cpt_salary_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['salary'])),
+            'cpt_proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)),
+            'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
+        }
+        
+        if type_var == 'Classic':
+            st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
+            st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
+            st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)
+        elif type_var == 'Showdown':
+            # Calculate salary (CPT uses cpt_salary_map, others use salary_map)
+            st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(
+                lambda row: map_dict['cpt_salary_map'].get(row.iloc[0], 0) + 
+                          sum(map_dict['salary_map'].get(player, 0) for player in row.iloc[1:]),
+                axis=1
+            )
+            
+            # Calculate median (CPT uses cpt_proj_map, others use proj_map)
+            st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(
+                lambda row: map_dict['cpt_proj_map'].get(row.iloc[0], 0) + 
+                          sum(map_dict['proj_map'].get(player, 0) for player in row.iloc[1:]),
+                axis=1
+            )
+            
+            # Calculate ownership (CPT uses cpt_own_map, others use own_map)
+            st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(
+                lambda row: map_dict['cpt_own_map'].get(row.iloc[0], 0) + 
+                          sum(map_dict['own_map'].get(player, 0) for player in row.iloc[1:]),
+                axis=1
+            )
+
+        with col2:
+            # Display the paginated dataframe first
+            st.dataframe(
+                st.session_state['portfolio'].style
+                .background_gradient(axis=0)
+                .background_gradient(cmap='RdYlGn')
+                .background_gradient(cmap='RdYlGn_r', subset=['Finish_percentile', 'Own', 'Dupes'])
+                .format(precision=2), 
+                height=1000,
+                use_container_width=True
+            )