import streamlit as st st.set_page_config(layout="wide") import numpy as np import pandas as pd import time from fuzzywuzzy import process import random ## import global functions from global_func.clean_player_name import clean_player_name from global_func.load_contest_file import load_contest_file from global_func.load_file import load_file from global_func.load_ss_file import load_ss_file from global_func.find_name_mismatches import find_name_mismatches from global_func.predict_dupes import predict_dupes from global_func.highlight_rows import highlight_changes, highlight_changes_winners, highlight_changes_losers from global_func.load_csv import load_csv from global_func.find_csv_mismatches import find_csv_mismatches tab1, tab2 = st.tabs(["Data Load", "Contest Analysis"]) with tab1: if st.button('Clear data', key='reset1'): st.session_state.clear() # Add file uploaders to your app col1, col2, col3 = st.columns(3) with col1: st.subheader("Contest File") st.info("Go ahead and upload a Contest file here. Only include player columns and an optional 'Stack' column if you are playing MLB.") Contest_file = st.file_uploader("Upload Contest File (CSV or Excel)", type=['csv', 'xlsx', 'xls']) if 'Contest' in st.session_state: del st.session_state['Contest'] if Contest_file: st.session_state['Contest'], st.session_state['ownership_dict'], st.session_state['entry_list'] = load_contest_file(Contest_file) st.session_state['Contest'] = st.session_state['Contest'].dropna(how='all') st.session_state['Contest'] = st.session_state['Contest'].reset_index(drop=True) if st.session_state['Contest'] is not None: st.success('Contest file loaded successfully!') st.dataframe(st.session_state['Contest'].head(10)) with col2: st.subheader("Projections File") st.info("upload a projections file that has 'player_names', 'salary', 'median', 'ownership', and 'captain ownership' (Needed for Showdown) columns. Note that the salary for showdown needs to be the FLEX salary, not the captain salary.") # Create two columns for the uploader and template button upload_col, template_col = st.columns([3, 1]) with upload_col: projections_file = st.file_uploader("Upload Projections File (CSV or Excel)", type=['csv', 'xlsx', 'xls']) if 'projections_df' in st.session_state: del st.session_state['projections_df'] with template_col: # Create empty DataFrame with required columns template_df = pd.DataFrame(columns=['player_names', 'position', 'team', 'salary', 'median', 'ownership', 'captain ownership']) # Add download button for template st.download_button( label="Template", data=template_df.to_csv(index=False), file_name="projections_template.csv", mime="text/csv" ) if projections_file: export_projections, projections = load_file(projections_file) if projections is not None: st.success('Projections file loaded successfully!') st.dataframe(projections.head(10)) if Contest_file and projections_file: if st.session_state['Contest'] is not None and projections is not None: st.subheader("Name Matching functions") # Initialize projections_df in session state if it doesn't exist if 'projections_df' not in st.session_state: st.session_state['projections_df'] = projections.copy() st.session_state['projections_df']['salary'] = (st.session_state['projections_df']['salary'].astype(str).str.replace(',', '').astype(float).astype(int)) # Update projections_df with any new matches st.session_state['contest_df'], st.session_state['projections_df'] = find_name_mismatches(st.session_state['Contest'], st.session_state['projections_df']) with tab2: if st.button('Clear data', key='reset3'): st.session_state.clear() if 'portfolio' in st.session_state and 'projections_df' in st.session_state: col1, col2 = st.columns([1, 8]) excluded_cols = ['BaseName', 'EntryCount'] with col1: with st.form(key='filter_form'): type_var = st.selectbox("Select Game Type", ['Classic', 'Showdown']) player_parse_var = st.selectbox("Do you want to view a specific player(s) or a group of players?", ['All', 'Specific']) if player_parse_var == 'Specific': player_names = st.multiselect("Select players", options=st.session_state['entry_list']) else: player_names = st.session_state['entry_list'] submitted = st.form_submit_button("Submit") 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'])) } if type_var == 'Classic': st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1) st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1) st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1) elif type_var == 'Showdown': # Calculate salary (CPT uses cpt_salary_map, others use salary_map) st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply( lambda row: map_dict['cpt_salary_map'].get(row.iloc[0], 0) + sum(map_dict['salary_map'].get(player, 0) for player in row.iloc[1:]), axis=1 ) # Calculate median (CPT uses cpt_proj_map, others use proj_map) st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply( lambda row: map_dict['cpt_proj_map'].get(row.iloc[0], 0) + sum(map_dict['proj_map'].get(player, 0) for player in row.iloc[1:]), axis=1 ) # Calculate ownership (CPT uses cpt_own_map, others use own_map) st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply( lambda row: map_dict['cpt_own_map'].get(row.iloc[0], 0) + sum(map_dict['own_map'].get(player, 0) for player in row.iloc[1:]), axis=1 ) with col2: # Display the paginated dataframe first st.dataframe( st.session_state['portfolio'].style .background_gradient(axis=0) .background_gradient(cmap='RdYlGn') .background_gradient(cmap='RdYlGn_r', subset=['Finish_percentile', 'Own', 'Dupes']) .format(precision=2), height=1000, use_container_width=True )