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Running
James McCool
Enhance team analysis functionality with sidebar options for team selection and prediction settings. Updated init_team_data function to incorporate win/loss predictions and display projected kills and deaths based on user input.
a655909
| import streamlit as st | |
| st.set_page_config(layout="wide") | |
| import numpy as np | |
| import pandas as pd | |
| import pymongo | |
| import time | |
| def init_conn(): | |
| uri = st.secrets['mongo_uri'] | |
| client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) | |
| db = client["League_of_Legends_Database"] | |
| collection = db["gamelogs"] | |
| team_names = collection.distinct("teamname") | |
| player_names = collection.distinct("playername") | |
| return db, team_names, player_names | |
| db, team_names, player_names = init_conn() | |
| # Create sidebar container for options | |
| with st.sidebar: | |
| st.header("Team Analysis Options") | |
| selected_team = st.selectbox( | |
| "Select Team", | |
| options=team_names, | |
| index=team_names.index("T1") if "T1" in team_names else 0 | |
| ) | |
| st.subheader("Prediction Settings") | |
| win_loss = st.selectbox( | |
| "Select Win/Loss", | |
| options=["Win", "Loss"], | |
| index=0 | |
| ) | |
| kill_prediction = st.number_input( | |
| "Predicted Team Kills", | |
| min_value=0, | |
| max_value=100, | |
| value=15 | |
| ) | |
| death_prediction = st.number_input( | |
| "Predicted Team Deaths", | |
| min_value=0, | |
| max_value=100, | |
| value=10 | |
| ) | |
| def init_team_data(team, win_loss, kill_prediction, death_prediction): | |
| collection = db["gamelogs"] | |
| cursor = collection.find({"teamname": team}) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['playername', 'teamname', 'playername_avg_kill_share_win', 'playername_avg_death_share_win', 'playername_avg_assist_share_win', 'playername_avg_cs_share_win', 'playername_avg_kill_share_loss', 'playername_avg_death_share_loss', 'playername_avg_assist_share_loss', 'playername_avg_cs_share_loss']] | |
| raw_display = raw_display.rename(columns = {'playername_avg_kill_share_win': 'wKill%', 'playername_avg_death_share_win': 'wDeath%', 'playername_avg_assist_share_win': 'wAssist%', 'playername_avg_cs_share_win': 'wCS%', 'playername_avg_kill_share_loss': 'lKill%', 'playername_avg_death_share_loss': 'lDeath%', 'playername_avg_assist_share_loss': 'lAssist%', 'playername_avg_cs_share_loss': 'lCS%'}) | |
| team_data = raw_display.drop_duplicates(subset = ['playername']) | |
| if win_loss == "Win": | |
| team_data['Kill_Proj'] = team_data['wKill%'] * kill_prediction | |
| team_data['Death_Proj'] = team_data['wDeath%'] * death_prediction | |
| else: | |
| team_data['Kill_Proj'] = team_data['lKill%'] * kill_prediction | |
| team_data['Death_Proj'] = team_data['lDeath%'] * death_prediction | |
| return team_data | |
| if st.button("Run"): | |
| st.dataframe(init_team_data(selected_team, win_loss, kill_prediction, death_prediction)) |