Refactor app.py to improve session state handling and user interaction

- Streamlined the management of session state variables in app.py, specifically focusing on the handling of 'Contest' and 'projections_df'.
- Enhanced the layout for user filters and data clearing options, ensuring a more intuitive user experience.
- Maintained the functionality of 'calc_toggle' to track calculation states effectively during user interactions.

app.py

@@ -86,46 +86,45 @@ with tab2:
     st.write(st.session_state['calc_toggle'])
     st.write(st.session_state['projections_df'])
     st.write(st.session_state['Contest'])
-    if 'Contest' in st.session_state and 'projections_df' in st.session_state and st.session_state['calc_toggle']:
-        col1, col2 = st.columns([1, 8])
-        excluded_cols = ['BaseName', 'EntryCount']
-        player_columns = [col for col in st.session_state['Contest'].columns if col not in excluded_cols]
-        for col in player_columns:
-            st.session_state['Contest'][col] = st.session_state['Contest'][col].astype(str)
-        
-        # Create mapping dictionaries
-        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']))
-        }
-        # Create a copy of the dataframe for calculations
-        working_df = st.session_state['Contest'].copy()
+    excluded_cols = ['BaseName', 'EntryCount']
+    player_columns = [col for col in st.session_state['Contest'].columns if col not in excluded_cols]
+    for col in player_columns:
+        st.session_state['Contest'][col] = st.session_state['Contest'][col].astype(str)
+    
+    # Create mapping dictionaries
+    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']))
+    }
+    # Create a copy of the dataframe for calculations
+    working_df = st.session_state['Contest'].copy()
 
-        with col1:
-            with st.expander("Info and filters"):
-                if st.button('Clear data', key='reset3'):
-                    st.session_state.clear()
-                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:
-                        if 'player_frame' in st.session_state:
-                            del st.session_state['player_frame']
-                        if 'stack_frame' in st.session_state:
-                            del st.session_state['stack_frame']
-                        # 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"):
+        if st.button('Clear data', key='reset3'):
+            st.session_state.clear()
+        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:
+                if 'player_frame' in st.session_state:
+                    del st.session_state['player_frame']
+                if 'stack_frame' in st.session_state:
+                    del st.session_state['stack_frame']
+                # 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)]
+                    st.session_state['calc_toggle'] = True
 
+    if 'Contest' in st.session_state and 'projections_df' in st.session_state and st.session_state['calc_toggle']:    
         if type_var == 'Classic':
             working_df['stack'] = working_df.apply(
                 lambda row: Counter(