Update app.py

app.py

@@ -1,37 +1,48 @@
 import streamlit as st
 import pandas as pd
 import joblib
+from sklearn.ensemble import RandomForestRegressor
 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,
+    "PG": 1.0,  # Point Guard
+    "SG": 2.0,  # Shooting Guard
+    "SF": 3.0,  # Small Forward
+    "PF": 4.0,  # Power Forward
+    "C": 5.0,   # Center
 }
 
-# Injury types and average days
+# 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"
+    "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,
@@ -60,123 +71,111 @@ average_days_injured = {
     "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
+
+
+
+# Load player dataset
 @st.cache_resource
 def load_player_data():
-    return pd.read_csv("player_data.csv")
+    return pd.read_csv("/Users/laraschuman/Desktop/CTP-Project/player_data.csv")
 
+# Load Random Forest model
 @st.cache_resource
 def load_rf_model():
-    return joblib.load("rf_injury_change_model.pkl")
+    return joblib.load("/Users/laraschuman/Desktop/CTP-Project/rf_injury_change_model.pkl")
 
-# Main application
+# Main Streamlit app
 def main():
-    st.title("NBA Player Performance Predictor 🏀")
-    st.markdown(
+    st.title("NBA Player Performance Predictor")
+    st.write(
         """
-        Use this tool to predict how a player's performance metrics might change
-        if they experience a hypothetical injury.
+        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 data and model
+    # Load player data
     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']
+    st.sidebar.header("Player and Injury Input")
+
+    # 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]
+
+        if not player_row.empty:
             position = player_row.iloc[0]['position']
-            stats_columns = ['age', 'player_height', 'player_weight']
+            position_numeric = position_mapping.get(position, 0)
+
+            st.sidebar.write(f"**Position**: {position} (Numeric: {position_numeric})")
 
-            st.write(f"**Position**: {position}")
-            st.write(f"**Team**: {team_name}")
+            # 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
+            }
 
-            # Player stats
-            default_stats = {stat: player_row.iloc[0][stat] for stat in stats_columns}
+            # Allow manual adjustment of stats
             for stat in default_stats.keys():
-                default_stats[stat] = st.number_input(f"{stat}", value=default_stats[stat])
+                default_stats[stat] = st.sidebar.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
+            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_mapping.get(position, 0),
-                "injury_type": injury_type,
+                "position": position_numeric,
+                "injury_type": injury_type,  # Include the selected injury type
                 **default_stats
             }])
 
-            # One-hot encode injury type
-            input_data = pd.get_dummies(input_data, columns=["injury_type"], drop_first=True)
+            # Encode injury type
+            input_data["injury_type"] = pd.factorize(input_data["injury_type"])[0]
 
-            # 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
+            # Load Random Forest model
+            try:
+                rf_model = load_rf_model()
 
-            # Ensure columns are in the same order as the model's feature names
-            input_data = input_data[expected_features]
+                # 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
-            st.header("Prediction Results")
-            if st.sidebar.button("Predict"):
-                try:
+                # 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"]
+                    prediction_columns = ["Predicted Change in PTS", "Predicted Change in REB", "Predicted Change inAST"]
                     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}")
+            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.")
 
 if __name__ == "__main__":
-    main()
+    main()