Refactor player filtering and calculation logic in app.py for improved functionality

- Reorganized the player selection form to ensure filters are applied only after submission, enhancing user experience.
- Updated the logic for applying filters and calculating metrics based on game type, ensuring accurate data processing.
- Reset pagination when new filters are applied to maintain consistency in data display.

app.py

@@ -84,13 +84,7 @@ with tab2:
     if 'contest_df' 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'])
-                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")
-
+        
         # Create mapping dictionaries
         map_dict = {
             'pos_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['position'])),
@@ -104,34 +98,46 @@ with tab2:
             'cpt_own_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
         }
 
+        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")
+
         # Create a copy of the dataframe for calculations
         working_df = st.session_state['contest_df'].copy()
 
-        # Apply filters if submitted
-        if submitted and entry_parse_var == 'Specific':
-            working_df = working_df[working_df['BaseName'].isin(entry_names)]
-
-        # Calculate metrics based on game type
-        if type_var == 'Classic':
-            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)
-        elif type_var == 'Showdown':
-            working_df['salary'] = working_df.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
-            )
-            working_df['median'] = working_df.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
-            )
-            working_df['Own'] = working_df.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
-            )
+        # Apply filters and calculations only after form submission
+        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['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)
+            elif type_var == 'Showdown':
+                working_df['salary'] = working_df.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
+                )
+                working_df['median'] = working_df.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
+                )
+                working_df['Own'] = working_df.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
+                )
+
+            # Reset pagination when new filters are applied
+            st.session_state.current_page = 0
 
         # Initialize pagination in session state if not exists
         if 'current_page' not in st.session_state: