Update app.py

app.py

@@ -3,6 +3,52 @@ 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"
+)
+
+
+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 = {
@@ -72,18 +118,15 @@ average_days_injured = {
 }
 
 
-
-
-
 # Load player dataset
 @st.cache_resource
 def load_player_data():
-    return pd.read_csv("/Users/laraschuman/Desktop/CTP-Project/player_data.csv")
+    return pd.read_csv("player_data.csv")
 
 # Load Random Forest model
 @st.cache_resource
 def load_rf_model():
-    return joblib.load("/Users/laraschuman/Desktop/CTP-Project/rf_injury_change_model.pkl")
+    return joblib.load("rf_injury_change_model.pkl")
 
 # Main Streamlit app
 def main():
@@ -109,6 +152,8 @@ def main():
     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']
@@ -177,5 +222,90 @@ def main():
     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.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 __name__ == "__main__":
     main()