app.py

import streamlit as st
st.set_page_config(layout="wide")
import numpy as np
import pandas as pd
import pymongo
import time
from datetime import datetime, timedelta

@st.cache_resource
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"] 
        min_date = datetime.strptime(collection.find_one({}, sort=[("date", 1)])["date"], "%Y-%m-%d %H:%M:%S")
        max_date = datetime.strptime(collection.find_one({}, sort=[("date", -1)])["date"], "%Y-%m-%d %H:%M:%S")
        team_names = collection.distinct("teamname")
        player_names = collection.distinct("playername")

        return db, team_names, player_names, min_date, max_date
    
db, team_names, player_names, min_date, max_date = init_conn()

display_formats = {'wKill%': '{:.2%}', 'wDeath%': '{:.2%}', 'wAssist%': '{:.2%}', 'lKill%': '{:.2%}', 'lDeath%': '{:.2%}', 'lAssist%': '{:.2%}'}

# Create sidebar container for options
with st.sidebar:
    st.header("Team Analysis Options")

    # Date filtering options
    st.subheader("Date Range")
    date_filter = st.radio(
        "Select Date Range",
        ["Last Year", "Custom Range"]
    )
    
    if date_filter == "Last Year":
        end_date = max_date
        start_date = (end_date - timedelta(days=365))
    else:
        col1, col2 = st.columns(2)
        with col1:
            start_date = st.date_input(
                "Start Date",
                value=max_date.date() - timedelta(days=30),
                min_value=min_date.date(),
                max_value=max_date.date()
            )
        with col2:
            end_date = st.date_input(
                "End Date",
                value=max_date.date(),
                min_value=min_date.date(),
                max_value=max_date.date()
            )

    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=20
    )
    
    death_prediction = st.number_input(
        "Predicted Team Deaths",
        min_value=0,
        max_value=100,
        value=5
    )

@st.cache_data(ttl = 60)
def init_team_data(team, win_loss, kill_prediction, death_prediction, start_date, end_date):  
        
        # Convert date objects to datetime strings in the correct format
        start_datetime = datetime.combine(start_date, datetime.min.time()).strftime("%Y-%m-%d %H:%M:%S")
        end_datetime = datetime.combine(end_date, datetime.max.time()).strftime("%Y-%m-%d %H:%M:%S")

        collection = db["gamelogs"] 
        cursor = collection.find({"teamname": team, "date": {"$gte": start_datetime, "$lte": end_datetime}})
    
        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_total_cs_win', 'playername_avg_kill_share_loss', 'playername_avg_death_share_loss', 'playername_avg_assist_share_loss', 'playername_avg_total_cs_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_total_cs_win': 'wCS', 'playername_avg_kill_share_loss': 'lKill%', 'playername_avg_death_share_loss': 'lDeath%', 'playername_avg_assist_share_loss': 'lAssist%', 'playername_avg_total_cs_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
            team_data['Assist_Proj'] = team_data['wAssist%'] * kill_prediction
            team_data = team_data[['playername', 'teamname', 'wKill%', 'wDeath%', 'wAssist%', 'wCS', 'Kill_Proj', 'Death_Proj', 'Assist_Proj']]
        else:
            team_data['Kill_Proj'] = team_data['lKill%'] * kill_prediction
            team_data['Death_Proj'] = team_data['lDeath%'] * death_prediction
            team_data['Assist_Proj'] = team_data['lAssist%'] * kill_prediction
            team_data = team_data[['playername', 'teamname', 'lKill%', 'lDeath%', 'lAssist%', 'lCS', 'Kill_Proj', 'Death_Proj', 'Assist_Proj']]

        return team_data.dropna().reset_index(drop=True)

if st.button("Run"):
    st.dataframe(init_team_data(selected_team, win_loss, kill_prediction, death_prediction, start_date, end_date).style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(display_formats, precision=2), use_container_width = True)