Upload app.py

app.py

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+import streamlit as st
+import pandas as pd
+import joblib
+from sklearn.ensemble import RandomForestRegressor
+import plotly.express as px
+
+# 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("/Users/laraschuman/Desktop/CTP-Project/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")
+
+# 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()
+
+    # Sidebar inputs
+    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']
+            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
+                if st.sidebar.button("Predict"):
+                    predictions = rf_model.predict(input_data)
+                    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}")
+
+        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()