app.py

import streamlit as st
import pandas as pd
import joblib
import plotly.express as px
from PIL import Image

# Set the page configuration
st.set_page_config(
    page_title="NBA Player Performance Predictor 🏀",
    page_icon="🏀",
    layout="centered"
)

# Mapping for position to numeric values
position_mapping = {
    "PG": 1.0,
    "SG": 2.0,
    "SF": 3.0,
    "PF": 4.0,
    "C": 5.0,
}

# Injury types and average days
injury_types = [
    "foot fracture injury", "hip flexor surgery injury", "calf strain injury",
    "quad injury injury", "shoulder sprain injury", "foot sprain injury",
    "torn rotator cuff injury injury", "torn mcl injury", "hip flexor strain injury",
    "fractured leg injury", "sprained mcl injury", "ankle sprain injury",
    "hamstring injury injury", "meniscus tear injury", "torn hamstring injury",
    "dislocated shoulder injury", "ankle fracture injury", "fractured hand injury",
    "bone spurs injury", "acl tear injury", "hip labrum injury", "back surgery injury",
    "arm injury injury", "torn shoulder labrum injury", "lower back spasm injury"
]

average_days_injured = {
    "foot fracture injury": 207.666667,
    "hip flexor surgery injury": 256.000000,
    "calf strain injury": 236.000000,
    "quad injury injury": 283.000000,
    "shoulder sprain injury": 259.500000,
    "foot sprain injury": 294.000000,
    "torn rotator cuff injury injury": 251.500000,
    "torn mcl injury": 271.000000,
    "hip flexor strain injury": 253.000000,
    "fractured leg injury": 250.250000,
    "sprained mcl injury": 228.666667,
    "ankle sprain injury": 231.333333,
    "hamstring injury injury": 220.000000,
    "meniscus tear injury": 201.250000,
    "torn hamstring injury": 187.666667,
    "dislocated shoulder injury": 269.000000,
    "ankle fracture injury": 114.500000,
    "fractured hand injury": 169.142857,
    "bone spurs injury": 151.500000,
    "acl tear injury": 268.000000,
    "hip labrum injury": 247.500000,
    "back surgery injury": 215.800000,
    "arm injury injury": 303.666667,
    "torn shoulder labrum injury": 195.666667,
    "lower back spasm injury": 234.000000,
}

team_logo_paths = {
    "Cleveland Cavaliers": "NBA_LOGOs/Clevelan-Cavaliers-logo-2022.png",
    "Atlanta Hawks": "NBA_LOGOs/nba-atlanta-hawks-logo.png",
    "Boston Celtics": "NBA_LOGOs/nba-boston-celtics-logo.png",
    "Brooklyn Nets": "NBA_LOGOs/nba-brooklyn-nets-logo.png",
    "Charlotte Hornets": "NBA_LOGOs/nba-charlotte-hornets-logo.png",
    "Chicago Bulls": "NBA_LOGOs/nba-chicago-bulls-logo.png",
    "Dallas Mavericks": "NBA_LOGOs/nba-dallas-mavericks-logo.png",
    "Denver Nuggets": "NBA_LOGOs/nba-denver-nuggets-logo-2018.png",
    "Detroit Pistons": "NBA_LOGOs/nba-detroit-pistons-logo.png",
    "Golden State Warriors": "NBA_LOGOs/nba-golden-state-warriors-logo-2020.png",
    "Houston Rockets": "NBA_LOGOs/nba-houston-rockets-logo-2020.png",
    "Indiana Pacers": "NBA_LOGOs/nba-indiana-pacers-logo.png",
    "LA Clippers": "NBA_LOGOs/nba-la-clippers-logo.png",
    "Los Angeles Lakers": "NBA_LOGOs/nba-los-angeles-lakers-logo.png",
    "Memphis Grizzlies": "NBA_LOGOs/nba-memphis-grizzlies-logo.png",
    "Miami Heat": "NBA_LOGOs/nba-miami-heat-logo.png",
    "Milwaukee Bucks": "NBA_LOGOs/nba-milwaukee-bucks-logo.png",
    "Minnesota Timberwolves": "NBA_LOGOs/nba-minnesota-timberwolves-logo.png",
    "New Orleans Pelicans": "NBA_LOGOs/nba-new-orleans-pelicans-logo.png",
    "New York Knicks": "NBA_LOGOs/nba-new-york-knicks-logo.png",
    "Oklahoma City Thunder": "NBA_LOGOs/nba-oklahoma-city-thunder-logo.png",
    "Orlando Magic": "NBA_LOGOs/nba-orlando-magic-logo.png",
    "Philadelphia 76ers": "NBA_LOGOs/nba-philadelphia-76ers-logo.png",
    "Phoenix Suns": "NBA_LOGOs/nba-phoenix-suns-logo.png",
    "Portland Trail Blazers": "NBA_LOGOs/nba-portland-trail-blazers-logo.png",
    "Sacramento Kings": "NBA_LOGOs/nba-sacramento-kings-logo.png",
    "San Antonio Spurs": "NBA_LOGOs/nba-san-antonio-spurs-logo.png",
    "Toronto Raptors": "NBA_LOGOs/nba-toronto-raptors-logo-2020.png",
    "Utah Jazz": "NBA_LOGOs/nba-utah-jazz-logo.png",
    "Washington Wizards": "NBA_LOGOs/nba-washington-wizards-logo.png",
}

# Caching player data and model
@st.cache_resource
def load_player_data():
    return pd.read_csv("player_data.csv")

@st.cache_resource
def load_rf_model():
    return joblib.load("rf_injury_change_model.pkl")

# Main application
def main():
    st.title("NBA Player Performance Predictor 🏀")
    st.markdown(
        """
        Use this tool to predict how a player's performance metrics might change
        if they experience a hypothetical injury.
        """
    )

    # Load data and model
    player_data = load_player_data()
    rf_model = load_rf_model()

    # Sidebar inputs
    with st.sidebar:
        st.header("Player & Injury Inputs")
        player_list = sorted(player_data['player_name'].dropna().unique())
        player_name = st.selectbox("Select Player", player_list)

        if player_name:
            # Filter data for the selected player
            player_row = player_data[player_data['player_name'] == player_name]
            team_name = player_row.iloc[0]['team_abbreviation']
            position = player_row.iloc[0]['position']
            stats_columns = ['age', 'player_height', 'player_weight']

            st.write(f"**Position**: {position}")
            st.write(f"**Team**: {team_name}")

            # Player stats
            default_stats = {stat: player_row.iloc[0][stat] for stat in stats_columns}
            for stat in default_stats.keys():
                default_stats[stat] = st.number_input(f"{stat}", value=default_stats[stat])

            # Injury details
            injury_type = st.selectbox("Select Hypothetical Injury", injury_types)
            default_days_injured = average_days_injured.get(injury_type, 30)
            days_injured = st.slider("Estimated Days Injured", 0, 365, int(default_days_injured))
            injury_occurrences = st.number_input("Injury Occurrences", min_value=0, value=1)

            # Prepare data for prediction
            input_data = pd.DataFrame([{
                "days_injured": days_injured,
                "injury_occurrences": injury_occurrences,
                "position": position_mapping.get(position, 0),
                "injury_type": injury_type,
                **default_stats
            }])

            # One-hot encode injury type
            input_data = pd.get_dummies(input_data, columns=["injury_type"], drop_first=True)

            # Align with model's feature names
            expected_features = rf_model.feature_names_in_
            for feature in expected_features:
                if feature not in input_data.columns:
                    input_data[feature] = 0

            # Ensure columns are in the same order as the model's feature names
            input_data = input_data[expected_features]

            # Predict and display results
            st.header("Prediction Results")
            if st.sidebar.button("Predict"):
                try:
                    predictions = rf_model.predict(input_data)
                    prediction_columns = ["Predicted Change in PTS", "Predicted Change in REB", "Predicted Change in AST"]
                    st.subheader("Predicted Post-Injury Performance")
                    st.write("Based on the inputs, here are the predicted metrics:")
                    st.table(pd.DataFrame(predictions, columns=prediction_columns))
                except FileNotFoundError:
                    st.error("Model file not found.")
                except ValueError as e:
                    st.error(f"Error during prediction: {e}")

if __name__ == "__main__":
    main()