import streamlit as st import pandas as pd import joblib from sklearn.ensemble import RandomForestRegressor import plotly.express as px from sklearn.ensemble import RandomForestRegressor import plotly.graph_objects as go from PIL import Image import plotly.express as px # Set the page configuration st.set_page_config( page_title="NBA Player Performance Predictor", page_icon="🏀", layout="centered" ) # Custom CSS for vibrant NBA sidebar styling st.markdown( """ """, unsafe_allow_html=True ) team_logo_paths = { "Cleveland Cavaliers": "Clevelan-Cavaliers-logo-2022.png", "Atlanta Hawks": "nba-atlanta-hawks-logo.png", "Boston Celtics": "nba-boston-celtics-logo.png", "Brooklyn Nets": "nba-brooklyn-nets-logo.png", "Charlotte Hornets": "nba-charlotte-hornets-logo.png", "Chicago Bulls": "nba-chicago-bulls-logo.png", "Dallas Mavericks": "nba-dallas-mavericks-logo.png", "Denver Nuggets": "nba-denver-nuggets-logo-2018.png", "Detroit Pistons": "nba-detroit-pistons-logo.png", "Golden State Warriors": "nba-golden-state-warriors-logo-2020.png", "Houston Rockets": "nba-houston-rockets-logo-2020.png", "Indiana Pacers": "nba-indiana-pacers-logo.png", "LA Clippers": "nba-la-clippers-logo.png", "Los Angeles Lakers": "nba-los-angeles-lakers-logo.png", "Memphis Grizzlies": "nba-memphis-grizzlies-logo.png", "Miami Heat": "nba-miami-heat-logo.png", "Milwaukee Bucks": "nba-milwaukee-bucks-logo.png", "Minnesota Timberwolves": "nba-minnesota-timberwolves-logo.png", "New Orleans Pelicans": "nba-new-orleans-pelicans-logo.png", "New York Knicks": "nba-new-york-knicks-logo.png", "Oklahoma City Thunder": "nba-oklahoma-city-thunder-logo.png", "Orlando Magic": "nba-orlando-magic-logo.png", "Philadelphia 76ers": "nba-philadelphia-76ers-logo.png", "Phoenix Suns": "nba-phoenix-suns-logo.png", "Portland Trail Blazers": "nba-portland-trail-blazers-logo.png", "Sacramento Kings": "nba-sacramento-kings-logo.png", "San Antonio Spurs": "nba-san-antonio-spurs-logo.png", "Toronto Raptors": "nba-toronto-raptors-logo-2020.png", "Utah Jazz": "nba-utah-jazz-logo.png", "Washington Wizards": "nba-washington-wizards-logo.png", } # Mapping for position to numeric values position_mapping = { "PG": 1.0, # Point Guard "SG": 2.0, # Shooting Guard "SF": 3.0, # Small Forward "PF": 4.0, # Power Forward "C": 5.0, # Center } # Predefined injury types 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" ] # Injury average days dictionary 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, } # Load player dataset @st.cache_resource def load_player_data(): return pd.read_csv("player_data.csv") # Load Random Forest model @st.cache_resource def load_rf_model(): return joblib.load("rf_injury_change_model.pkl") # Main Streamlit app def main(): st.title("NBA Player Performance Predictor 🏀") st.write( """ Predict how a player's performance metrics (e.g., points, rebounds, assists) might change if a hypothetical injury occurs, based on their position and other factors. """ ) # Load player data player_data = load_player_data() rf_model = load_rf_model() st.sidebar.markdown( """

Player Details

""", unsafe_allow_html=True ) # Dropdown for player selection player_list = sorted(player_data['player_name'].dropna().unique()) player_name = st.sidebar.selectbox("Select Player", player_list) if player_name: # Retrieve player details player_row = player_data[player_data['player_name'] == player_name] team_name = player_row.iloc[0]['team_abbreviation'] position = player_row.iloc[0]['position'] if not player_row.empty: position = player_row.iloc[0]['position'] position_numeric = position_mapping.get(position, 0) st.sidebar.write(f"**Position**: {position} (Numeric: {position_numeric})") # Default values for features stats_columns = ['age', 'player_height', 'player_weight'] default_stats = { stat: player_row.iloc[0][stat] if stat in player_row.columns else 0 for stat in stats_columns } # Allow manual adjustment of stats for stat in default_stats.keys(): default_stats[stat] = st.sidebar.number_input(f"{stat}", value=default_stats[stat]) # Injury details injury_type = st.sidebar.selectbox("Select Hypothetical Injury", injury_types) # Replace slider with default average based on injury type default_days_injured = average_days_injured[injury_type] or 30 # Use 30 if None days_injured = st.sidebar.slider( "Estimated Days Injured", 0, 365, int(default_days_injured), help=f"Default days for {injury_type}: {int(default_days_injured) if default_days_injured else 'N/A'}" ) injury_occurrences = st.sidebar.number_input("Injury Occurrences", min_value=0, value=1) # Prepare input data input_data = pd.DataFrame([{ "days_injured": days_injured, "injury_occurrences": injury_occurrences, "position": position_numeric, "injury_type": injury_type, # Include the selected injury type **default_stats }]) # Encode injury type input_data["injury_type"] = pd.factorize(input_data["injury_type"])[0] # Load Random Forest model try: rf_model = load_rf_model() # Align input data with the model's feature names expected_features = rf_model.feature_names_in_ input_data = input_data.reindex(columns=rf_model.feature_names_in_, fill_value=0) # Predict and display results # Predict and display results if st.sidebar.button("Predict 🔮"): 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:") styled_table = pd.DataFrame(predictions, columns=prediction_columns).style.set_table_attributes('class="styled-table"') st.write(styled_table.to_html(), unsafe_allow_html=True) # Plot predictions prediction_df = pd.DataFrame(predictions, columns=prediction_columns) fig = go.Figure() for col in prediction_columns: fig.add_trace(go.Bar( x=[col], y=prediction_df[col], name=col, marker=dict(color=px.colors.qualitative.Plotly[prediction_columns.index(col)]) )) fig.update_layout( title="Predicted Performance Changes", xaxis_title="Metrics", yaxis_title="Change Value", template="plotly_dark", showlegend=True ) st.plotly_chart(fig) except FileNotFoundError: st.error("Model file not found.") except ValueError as e: st.error(f"Error during prediction: {e}") else: st.sidebar.error("Player details not found in the dataset.") else: st.sidebar.error("Please select a player to view details.") st.divider() st.header("Player Overview") col1, col2 = st.columns([1, 2]) with col1: st.subheader("Player Details") st.markdown(f"""
Age
{default_stats['age']}
Height (cm)
{round(default_stats['player_height'], 2)}
Weight (kg)
{round(default_stats['player_weight'], 2)}
""", unsafe_allow_html=True) with col2: # Display team logo if team_name in team_logo_paths: logo_path = team_logo_paths[team_name] try: logo_image = Image.open(logo_path) st.image(logo_image, caption=f"{team_name} Logo", use_container_width=True) except FileNotFoundError: st.error(f"Logo for {team_name} not found.") # Graphs for PPG, AST, and REB st.divider() st.header("Player Performance Graphs") if st.button("Show Performance Graphs"): # Filter data for the selected player player_data_filtered = player_data[player_data["player_name"] == player_name].sort_values(by="season") # Ensure all seasons are included all_seasons = pd.Series(range(player_data["season"].min(), player_data["season"].max() + 1)) player_data_filtered = ( pd.DataFrame({"season": all_seasons}) .merge(player_data_filtered, on="season", how="left") ) if not player_data_filtered.empty: # PPG Graph fig_ppg = px.line( player_data_filtered, x="season", y="pts", title=f"{player_name}: Points Per Game (PPG) Over Seasons", labels={"pts": "Points Per Game (PPG)", "season": "Season"}, markers=True ) fig_ppg.update_layout(template="plotly_white") # AST Graph fig_ast = px.line( player_data_filtered, x="season", y="ast", title=f"{player_name}: Assists Per Game (AST) Over Seasons", labels={"ast": "Assists Per Game (AST)", "season": "Season"}, markers=True ) fig_ast.update_layout(template="plotly_white") # REB Graph fig_reb = px.line( player_data_filtered, x="season", y="reb", title=f"{player_name}: Rebounds Per Game (REB) Over Seasons", labels={"reb": "Rebounds Per Game (REB)", "season": "Season"}, markers=True ) fig_reb.update_layout(template="plotly_white") # Display graphs st.plotly_chart(fig_ppg, use_container_width=True) st.plotly_chart(fig_ast, use_container_width=True) st.plotly_chart(fig_reb, use_container_width=True) else: st.error("No data available for the selected player.") # Footer st.divider() st.markdown(""" ### About This Tool This application predicts how injuries might impact an NBA player's performance using machine learning models. Data is based on historical player stats and injuries. """) if __name__ == "__main__": main()