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()