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()