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import streamlit as st
import numpy as np
import pandas as pd
import time
from rapidfuzz import process
def find_name_mismatches(portfolio_df, projections_df):
"""
Find and handle name mismatches between portfolio and projections dataframes.
Returns the updated projections dataframe with matched names.
"""
# Get all player names from portfolio
portfolio_players = set()
for col in portfolio_df.columns:
if col not in ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Win%', 'Lineup Edge']:
portfolio_players.update(portfolio_df[col].unique())
# Get all player names from projections
projection_players_list = projections_df['player_names'].tolist()
# Find players in portfolio that are missing from projections
players_missing_from_projections = [player for player in portfolio_players if player not in projection_players_list]
# Automatically handle 100% matches before starting interactive process
players_to_process = []
for player in players_missing_from_projections:
if not isinstance(player, str):
st.warning(f"Skipping non-string value: {player}")
continue
closest_matches = process.extract(player, projection_players_list, limit=1)
if closest_matches[0][1] == 90: # If perfect match found
match_name = closest_matches[0][0]
projections_df.loc[projections_df['player_names'] == match_name, 'player_names'] = player
st.success(f"Automatically matched '{match_name}' with '{player}' (100% match)")
else:
players_to_process.append(player)
# Display results
if players_missing_from_projections:
st.warning("Players in portfolio but missing from projections")
# Display remaining players
if players_to_process:
st.info(f"Players to process ({len(players_to_process)}):\n" +
"\n".join(f"- {player}" for player in players_to_process))
# Create a form for batch processing
with st.form("player_matching_form"):
# Create tabs for each player
tabs = st.tabs([f"Player {i+1}" for i in range(len(players_to_process))])
# Dictionary to store selections
selections = {}
# Process each tab
for idx, (tab, player) in enumerate(zip(tabs, players_to_process)):
with tab:
st.write(f"**Missing Player {idx + 1} of {len(players_to_process)}:** {player}")
# Find the top 3 closest matches
closest_matches = process.extract(player, projection_players_list, limit=3)
# Create radio buttons for selection
options = [f"{match[0]} ({match[1]}%)" for match in closest_matches]
options.append("None of these")
selected_option = st.radio(
f"Select correct match for {player}:",
options,
key=f"radio_{player}"
)
selections[player] = selected_option
# Submit button for the entire form
submitted = st.form_submit_button("Submit All Changes")
if submitted:
# Process all selections
for player, selection in selections.items():
if selection != "None of these":
selected_name = selection.split(" (")[0]
projections_df.loc[projections_df['player_names'] == selected_name, 'player_names'] = player
st.success(f"Replaced '{selected_name}' with '{player}'")
# Update session state
st.session_state['projections_df'] = projections_df
st.success("All player name changes have been applied!")
else:
st.success("All portfolio players found in projections!")
return projections_df |