Refactor name matching analysis in app.py to ensure execution only occurs when 'origin_portfolio' is absent. This update improves the accuracy of player name matching and enhances session state management for portfolio and projections, ensuring proper data handling during user interactions.

app.py

@@ -180,115 +180,116 @@ with tab1:
                 projections = projections.apply(lambda x: x.replace(player_wrong_names_mlb, player_right_names_mlb))
                 st.dataframe(projections.head(10))
 
-    if portfolio_file and projections_file:
-        if st.session_state['portfolio'] is not None and projections is not None:
-            st.subheader("Name Matching Analysis")
-            # Initialize projections_df in session state if it doesn't exist
-            # Get unique names from portfolio
-            portfolio_names = get_portfolio_names(st.session_state['portfolio'])
-            try:
-                csv_names = st.session_state['csv_file']['Name'].tolist()
-            except:
-                csv_names = st.session_state['csv_file']['Nickname'].tolist()
-            projection_names = projections['player_names'].tolist()
-            
-            # Create match dictionary for portfolio names to projection names
-            portfolio_match_dict = {}
-            unmatched_names = []
-            for portfolio_name in portfolio_names:
-                match = process.extractOne(
-                    portfolio_name,
-                    csv_names,
-                    score_cutoff=87
-                )
-                if match:
-                    portfolio_match_dict[portfolio_name] = match[0]
-                    if match[1] < 100:
-                        st.write(f"{portfolio_name} matched from portfolio to site csv {match[0]} with a score of {match[1]}%")
-                else:
-                    portfolio_match_dict[portfolio_name] = portfolio_name
-                    unmatched_names.append(portfolio_name)
-            
-            # Update portfolio with matched names
-            portfolio = st.session_state['portfolio'].copy()
-            player_columns = [col for col in portfolio.columns 
-                            if col not in ['salary', 'median', 'Own']]
-            
-            # For each player column, update names using the match dictionary
-            for col in player_columns:
-                portfolio[col] = portfolio[col].map(lambda x: portfolio_match_dict.get(x, x))
-            st.session_state['portfolio'] = portfolio
-            
-            # Create match dictionary for portfolio names to projection names
-            projections_match_dict = {}
-            unmatched_proj_names = []
-            for projections_name in projection_names:
-                match = process.extractOne(
-                    projections_name,
-                    csv_names,
-                    score_cutoff=87
-                )
-                if match:
-                    projections_match_dict[projections_name] = match[0]
-                    if match[1] < 100:
-                        st.write(f"{projections_name} matched from projections to site csv {match[0]} with a score of {match[1]}%")
-                else:
-                    projections_match_dict[projections_name] = projections_name
-                    unmatched_proj_names.append(projections_name)
-            
-            # Update projections with matched names
-            projections['player_names'] = projections['player_names'].map(lambda x: projections_match_dict.get(x, x))
-            st.session_state['projections_df'] = projections
+    if 'origin_portfolio' not in st.session_state:
+        if portfolio_file and projections_file:
+            if st.session_state['portfolio'] is not None and projections is not None:
+                st.subheader("Name Matching Analysis")
+                # Initialize projections_df in session state if it doesn't exist
+                # Get unique names from portfolio
+                portfolio_names = get_portfolio_names(st.session_state['portfolio'])
+                try:
+                    csv_names = st.session_state['csv_file']['Name'].tolist()
+                except:
+                    csv_names = st.session_state['csv_file']['Nickname'].tolist()
+                projection_names = projections['player_names'].tolist()
+                
+                # Create match dictionary for portfolio names to projection names
+                portfolio_match_dict = {}
+                unmatched_names = []
+                for portfolio_name in portfolio_names:
+                    match = process.extractOne(
+                        portfolio_name,
+                        csv_names,
+                        score_cutoff=87
+                    )
+                    if match:
+                        portfolio_match_dict[portfolio_name] = match[0]
+                        if match[1] < 100:
+                            st.write(f"{portfolio_name} matched from portfolio to site csv {match[0]} with a score of {match[1]}%")
+                    else:
+                        portfolio_match_dict[portfolio_name] = portfolio_name
+                        unmatched_names.append(portfolio_name)
+                
+                # Update portfolio with matched names
+                portfolio = st.session_state['portfolio'].copy()
+                player_columns = [col for col in portfolio.columns 
+                                if col not in ['salary', 'median', 'Own']]
+                
+                # For each player column, update names using the match dictionary
+                for col in player_columns:
+                    portfolio[col] = portfolio[col].map(lambda x: portfolio_match_dict.get(x, x))
+                st.session_state['portfolio'] = portfolio
+                
+                # Create match dictionary for portfolio names to projection names
+                projections_match_dict = {}
+                unmatched_proj_names = []
+                for projections_name in projection_names:
+                    match = process.extractOne(
+                        projections_name,
+                        csv_names,
+                        score_cutoff=87
+                    )
+                    if match:
+                        projections_match_dict[projections_name] = match[0]
+                        if match[1] < 100:
+                            st.write(f"{projections_name} matched from projections to site csv {match[0]} with a score of {match[1]}%")
+                    else:
+                        projections_match_dict[projections_name] = projections_name
+                        unmatched_proj_names.append(projections_name)
+                
+                # Update projections with matched names
+                projections['player_names'] = projections['player_names'].map(lambda x: projections_match_dict.get(x, x))
+                st.session_state['projections_df'] = projections
 
-            projections_names = st.session_state['projections_df']['player_names'].tolist()
-            portfolio_names = get_portfolio_names(st.session_state['portfolio'])
-            
-            # Create match dictionary for portfolio names to projection names
-            projections_match_dict = {}
-            unmatched_proj_names = []
-            for projections_name in projection_names:
-                match = process.extractOne(
-                    projections_name,
-                    portfolio_names,
-                    score_cutoff=87
-                )
-                if match:
-                    projections_match_dict[projections_name] = match[0]
-                    if match[1] < 100:
-                        st.write(f"{projections_name} matched from portfolio to projections {match[0]} with a score of {match[1]}%")
-                else:
-                    projections_match_dict[projections_name] = projections_name
-                    unmatched_proj_names.append(projections_name)
-            
-            # Update projections with matched names
-            projections['player_names'] = projections['player_names'].map(lambda x: projections_match_dict.get(x, x))
-            st.session_state['projections_df'] = projections
-
-            if sport_var in stacking_sports:
-                team_dict = dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team']))
-                st.session_state['portfolio']['Stack'] = st.session_state['portfolio'].apply(
-                    lambda row: Counter(
-                        team_dict.get(player, '') for player in row[2:]
-                        if team_dict.get(player, '') != ''
-                    ).most_common(1)[0][0] if any(team_dict.get(player, '') for player in row[2:]) else '',
-                    axis=1
-                )
-                st.session_state['portfolio']['Size'] = st.session_state['portfolio'].apply(
-                    lambda row: Counter(
-                        team_dict.get(player, '') for player in row[2:]
-                        if team_dict.get(player, '') != ''
-                    ).most_common(1)[0][1] if any(team_dict.get(player, '') for player in row[2:]) else 0,
-                    axis=1
-                )
-                stack_dict = dict(zip(st.session_state['portfolio'].index, st.session_state['portfolio']['Stack']))
-                size_dict = dict(zip(st.session_state['portfolio'].index, st.session_state['portfolio']['Size']))
-
-            working_frame = st.session_state['portfolio'].copy()
-            try:
-                st.session_state['export_dict'] = dict(zip(st.session_state['csv_file']['Name'], st.session_state['csv_file']['Name + ID']))
-            except:
-                st.session_state['export_dict'] = dict(zip(st.session_state['csv_file']['Nickname'], st.session_state['csv_file']['Id']))
-            st.session_state['origin_portfolio'] = st.session_state['portfolio'].copy()
+                projections_names = st.session_state['projections_df']['player_names'].tolist()
+                portfolio_names = get_portfolio_names(st.session_state['portfolio'])
+                
+                # Create match dictionary for portfolio names to projection names
+                projections_match_dict = {}
+                unmatched_proj_names = []
+                for projections_name in projection_names:
+                    match = process.extractOne(
+                        projections_name,
+                        portfolio_names,
+                        score_cutoff=87
+                    )
+                    if match:
+                        projections_match_dict[projections_name] = match[0]
+                        if match[1] < 100:
+                            st.write(f"{projections_name} matched from portfolio to projections {match[0]} with a score of {match[1]}%")
+                    else:
+                        projections_match_dict[projections_name] = projections_name
+                        unmatched_proj_names.append(projections_name)
+                
+                # Update projections with matched names
+                projections['player_names'] = projections['player_names'].map(lambda x: projections_match_dict.get(x, x))
+                st.session_state['projections_df'] = projections
+
+                if sport_var in stacking_sports:
+                    team_dict = dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team']))
+                    st.session_state['portfolio']['Stack'] = st.session_state['portfolio'].apply(
+                        lambda row: Counter(
+                            team_dict.get(player, '') for player in row[2:]
+                            if team_dict.get(player, '') != ''
+                        ).most_common(1)[0][0] if any(team_dict.get(player, '') for player in row[2:]) else '',
+                        axis=1
+                    )
+                    st.session_state['portfolio']['Size'] = st.session_state['portfolio'].apply(
+                        lambda row: Counter(
+                            team_dict.get(player, '') for player in row[2:]
+                            if team_dict.get(player, '') != ''
+                        ).most_common(1)[0][1] if any(team_dict.get(player, '') for player in row[2:]) else 0,
+                        axis=1
+                    )
+                    stack_dict = dict(zip(st.session_state['portfolio'].index, st.session_state['portfolio']['Stack']))
+                    size_dict = dict(zip(st.session_state['portfolio'].index, st.session_state['portfolio']['Size']))
+
+                working_frame = st.session_state['portfolio'].copy()
+                try:
+                    st.session_state['export_dict'] = dict(zip(st.session_state['csv_file']['Name'], st.session_state['csv_file']['Name + ID']))
+                except:
+                    st.session_state['export_dict'] = dict(zip(st.session_state['csv_file']['Nickname'], st.session_state['csv_file']['Id']))
+                st.session_state['origin_portfolio'] = st.session_state['portfolio'].copy()
 
 # with tab2:
 #     if st.button('Clear data', key='reset2'):