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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_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['position_dict'], st.session_state['ownership_dict'], st.session_state['entry_list'] = load_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 Analysis")
# # 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['projections_df'] = find_name_mismatches(st.session_state['Contest'], st.session_state['projections_df'])
# if csv_file is not None and 'export_dict' not in st.session_state:
# # Create a dictionary of Name to Name+ID from csv_file
# try:
# name_id_map = dict(zip(
# st.session_state['csv_file']['Name'],
# st.session_state['csv_file']['Name + ID']
# ))
# except:
# name_id_map = dict(zip(
# st.session_state['csv_file']['Nickname'],
# st.session_state['csv_file']['Id']
# ))
# # Function to find best match
# def find_best_match(name):
# best_match = process.extractOne(name, name_id_map.keys())
# if best_match and best_match[1] >= 85: # 85% match threshold
# return name_id_map[best_match[0]]
# return name # Return original name if no good match found
# # Apply the matching
# projections['upload_match'] = projections['player_names'].apply(find_best_match)
# st.session_state['export_dict'] = dict(zip(projections['player_names'], projections['upload_match']))
with tab2:
if st.button('Clear data', key='reset3'):
st.session_state.clear()
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