Update app.py

app.py

@@ -1,14 +1,12 @@
 import streamlit as st
 import pandas as pd
 import joblib
-from sklearn.ensemble import RandomForestRegressor
-import plotly.graph_objects as go
-from PIL import Image
 import plotly.express as px
+from PIL import Image
 
 # Set the page configuration
 st.set_page_config(
-    page_title="NBA Player Performance Predictor",
+    page_title="NBA Player Performance Predictor 🏀",
     page_icon="🏀",
     layout="centered"
 )
@@ -63,36 +61,36 @@ average_days_injured = {
 }
 
 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",
+    "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
@@ -104,14 +102,15 @@ def load_player_data():
 def load_rf_model():
     return joblib.load("rf_injury_change_model.pkl")
 
-# Main application
 # 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.
-    """)
+    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()
@@ -152,103 +151,32 @@ def main():
                 "injury_type": injury_type,
                 **default_stats
             }])
-            input_data["injury_type"] = pd.factorize(input_data["injury_type"])[0]
 
+            # 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.button("Predict"):
-                predictions = rf_model.predict(input_data)
-                predictions = [round(float(pred), 2) for pred in predictions]
-
-                # Display prediction results
-                prediction_columns = ["Predicted Change in PTS", "Predicted Change in REB", "Predicted Change in AST"]
-                result_df = pd.DataFrame([predictions], columns=prediction_columns)
-                st.table(result_df)
-            
-                # Main content layout
-    st.divider()
-    st.header("Player Overview")
-    col1, col2 = st.columns([1, 2])
-
-    with col1:
-        st.subheader("Player Details")
-        st.metric("Age", default_stats['age'])
-        st.metric("Height (cm)", default_stats['player_height'])
-        st.metric("Weight (kg)", default_stats['player_weight'])
-
-    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")
-            .fillna({"pts": 0, "ast": 0, "reb": 0})  # Fill missing values
-        )
-
-        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 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()