Adding projections management

app.py

@@ -1266,7 +1266,7 @@ with st.container():
 try:
     selected_tab = st.segmented_control(
     "Select Tab",
-    options=["Data Load", "Manage Portfolio"],
+    options=["Data Load", "Projections Management", "Manage Portfolio"],
     selection_mode='single',
     default='Data Load',
     label_visibility='collapsed',
@@ -1276,7 +1276,7 @@ try:
 except:
     selected_tab = st.segmented_control(
     "Select Tab",
-    options=["Data Load", "Manage Portfolio"],
+    options=["Data Load", "Projections Management", "Manage Portfolio"],
     selection_mode='single',
     default='Data Load',
     label_visibility='collapsed',
@@ -1738,6 +1738,158 @@ if selected_tab == 'Data Load':
             
             del st.session_state['portfolio'], st.session_state['export_portfolio']
 
+
+if selected_tab == 'Projections Management':
+    if 'projections_df' in st.session_state and st.session_state['projections_df'] is not None:
+        st.subheader("Edit Player Projections")
+        st.caption("Modify median, ownership, or captain ownership values directly in the table below. Changes will update both the projections and all related mappings.")
+        
+        # Get the current projections dataframe
+        projections_editor_df = st.session_state['projections_df'].copy()
+        
+        # Define column configuration for the data editor
+        column_config = {
+            'player_names': st.column_config.TextColumn(
+                'Player',
+                disabled=True,
+                width='medium'
+            ),
+            'position': st.column_config.TextColumn(
+                'Position',
+                disabled=True,
+                width='small'
+            ),
+            'team': st.column_config.TextColumn(
+                'Team',
+                disabled=True,
+                width='small'
+            ),
+            'salary': st.column_config.NumberColumn(
+                'Salary',
+                disabled=True,
+                width='small',
+                format='$%d'
+            ),
+            'median': st.column_config.NumberColumn(
+                'Median',
+                min_value=0.0,
+                max_value=100.0,
+                step=0.1,
+                format='%.2f',
+                width='small'
+            ),
+            'ownership': st.column_config.NumberColumn(
+                'Ownership %',
+                min_value=0.0,
+                max_value=100.0,
+                step=0.1,
+                format='%.2f',
+                width='small'
+            ),
+            'captain ownership': st.column_config.NumberColumn(
+                'Captain Own %',
+                min_value=0.0,
+                max_value=100.0,
+                step=0.1,
+                format='%.2f',
+                width='small'
+            )
+        }
+        
+        # Search/filter functionality
+        search_col, team_filter_col, position_filter_col = st.columns([2, 1, 1])
+        with search_col:
+            player_search = st.text_input("🔍 Search players", placeholder="Type player name...", key='proj_player_search')
+        with team_filter_col:
+            team_options = ['All Teams'] + sorted(projections_editor_df['team'].unique().tolist())
+            team_filter = st.selectbox("Filter by Team", options=team_options, key='proj_team_filter')
+        with position_filter_col:
+            position_options = ['All Positions'] + sorted(projections_editor_df['position'].unique().tolist())
+            position_filter = st.selectbox("Filter by Position", options=position_options, key='proj_position_filter')
+        
+        # Apply filters
+        filtered_df = projections_editor_df.copy()
+        if player_search:
+            filtered_df = filtered_df[filtered_df['player_names'].str.contains(player_search, case=False, na=False)]
+        if team_filter != 'All Teams':
+            filtered_df = filtered_df[filtered_df['team'] == team_filter]
+        if position_filter != 'All Positions':
+            filtered_df = filtered_df[filtered_df['position'] == position_filter]
+        
+        # Display the editable dataframe
+        edited_df = st.data_editor(
+            filtered_df,
+            column_config=column_config,
+            use_container_width=True,
+            hide_index=True,
+            num_rows='fixed',
+            key='projections_editor'
+        )
+        
+        # Check for changes and update
+        if not edited_df.equals(filtered_df):
+            # Find which rows changed
+            changed_mask = ~(edited_df[['median', 'ownership', 'captain ownership']] == filtered_df[['median', 'ownership', 'captain ownership']]).all(axis=1)
+            changed_rows = edited_df[changed_mask]
+            
+            if len(changed_rows) > 0:
+                # Update the projections_df in session state
+                for idx, row in changed_rows.iterrows():
+                    player_name = row['player_names']
+                    
+                    # Find and update the original projections_df
+                    orig_idx = st.session_state['projections_df'][st.session_state['projections_df']['player_names'] == player_name].index
+                    if len(orig_idx) > 0:
+                        st.session_state['projections_df'].loc[orig_idx[0], 'median'] = row['median']
+                        st.session_state['projections_df'].loc[orig_idx[0], 'ownership'] = row['ownership']
+                        st.session_state['projections_df'].loc[orig_idx[0], 'captain ownership'] = row['captain ownership']
+                    
+                    # Update map_dict entries
+                    if 'map_dict' in st.session_state:
+                        st.session_state['map_dict']['proj_map'][player_name] = float(row['median'])
+                        st.session_state['map_dict']['own_map'][player_name] = float(row['ownership'])
+                        
+                        # Update ownership percent rank
+                        ownership_series = pd.Series(st.session_state['map_dict']['own_map'])
+                        st.session_state['map_dict']['own_percent_rank'] = dict(ownership_series.rank(pct=True).astype('float32'))
+                        
+                        # Update captain mappings based on site/type/sport configuration
+                        if 'cpt_proj_map' in st.session_state['map_dict']:
+                            # Determine the multiplier based on site/type/sport
+                            if site_var == 'Draftkings':
+                                if type_var == 'Showdown' and sport_var == 'GOLF':
+                                    st.session_state['map_dict']['cpt_proj_map'][player_name] = float(row['median'])
+                                else:
+                                    st.session_state['map_dict']['cpt_proj_map'][player_name] = float(row['median']) * 1.5
+                            elif site_var == 'Fanduel':
+                                st.session_state['map_dict']['cpt_proj_map'][player_name] = float(row['median']) * 1.5
+                        
+                        if 'cpt_own_map' in st.session_state['map_dict']:
+                            # Captain ownership uses the captain ownership column directly
+                            if type_var == 'Showdown' and sport_var == 'GOLF':
+                                st.session_state['map_dict']['cpt_own_map'][player_name] = float(row['ownership'])
+                            else:
+                                st.session_state['map_dict']['cpt_own_map'][player_name] = float(row['captain ownership'])
+                
+                # Clear working_frame to force recalculation with new projections
+                if 'working_frame' in st.session_state:
+                    del st.session_state['working_frame']
+                
+                st.success(f"✅ Updated {len(changed_rows)} player(s). Portfolio metrics will recalculate on next view.")
+                st.rerun()
+        
+        # Display summary statistics
+        st.divider()
+        summary_col1, summary_col2, summary_col3 = st.columns(3)
+        with summary_col1:
+            st.metric("Total Players", len(projections_editor_df))
+        with summary_col2:
+            st.metric("Avg Median", f"{projections_editor_df['median'].mean():.2f}")
+        with summary_col3:
+            st.metric("Avg Ownership", f"{projections_editor_df['ownership'].mean():.2f}%")
+    else:
+        st.info("📋 No projections file loaded yet. Please upload projections in the Data Load tab first.")
+
 if selected_tab == 'Manage Portfolio':
     if 'base_frame_names' not in st.session_state:
             st.session_state['base_frame_names'] = {}