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import streamlit as st
import numpy as np
import pandas as pd
import time
from fuzzywuzzy import process
def find_name_mismatches(portfolio_df, projections_df):
# Create a copy of the projections dataframe to avoid modifying the original
projections_df = projections_df.copy()
col_count = len(portfolio_df.columns)
portfolio_df.columns = range(col_count)
if 'player_names' not in projections_df.columns:
st.error("No 'player_names' column found in projections file")
return projections_df
# Get unique player names from portfolio and projections
portfolio_players = set()
for col in portfolio_df.columns:
portfolio_players.update(portfolio_df[col].unique())
projection_players = set(projections_df['player_names'].unique())
projection_players_list = list(projection_players)
# Find players in portfolio that are missing from projections
players_missing_from_projections = list(portfolio_players - projection_players)
# 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] == 100: # 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)
# Initialize session state for tracking current player if not exists
if 'current_player_index' not in st.session_state:
st.session_state.current_player_index = 0
st.session_state.players_to_process = players_to_process
# Display results
if players_missing_from_projections:
st.warning("Players in portfolio but missing from projections")
# Display remaining players
remaining_players = st.session_state.players_to_process[st.session_state.current_player_index:]
st.info(f"Remaining players to process ({len(remaining_players)}):\n" +
"\n".join(f"- {player}" for player in remaining_players))
if st.session_state.current_player_index < len(st.session_state.players_to_process):
current_player = st.session_state.players_to_process[st.session_state.current_player_index]
# Find the top 3 closest matches
closest_matches = process.extract(current_player, projection_players_list, limit=3)
st.write(f"**Missing Player {st.session_state.current_player_index + 1} of {len(st.session_state.players_to_process)}:** {current_player}")
# 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:",
options,
key=f"radio_{current_player}"
)
if st.button("Confirm Selection"):
if selected_option != "None of these":
selected_name = selected_option.split(" (")[0]
projections_df.loc[projections_df['player_names'] == selected_name, 'player_names'] = current_player
st.success(f"Replaced '{selected_name}' with '{current_player}'")
st.session_state['projections_df'] = projections_df
# Move to next player
st.session_state.current_player_index += 1
st.rerun()
else:
st.success("All players have been processed!")
# Reset the index for future runs
st.session_state.current_player_index = 0
st.session_state.players_to_process = []
else:
st.success("All portfolio players found in projections!")
return projections_df |