Create app.py

app.py

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+# Streamlit app for NBA Injury-Aware Performance Prediction
+# Enhanced with attention-derived injury impact insights
+
+import streamlit as st
+import pandas as pd
+import joblib
+from sklearn.ensemble import RandomForestRegressor
+import plotly.express as px
+import plotly.graph_objects as go
+from PIL import Image
+
+# Set app config
+st.set_page_config(
+    page_title="NBA Injury-Aware Predictor",
+    page_icon="🏀",
+    layout="centered"
+)
+
+# Load model and data
+@st.cache_resource
+def load_rf_model():
+    return joblib.load("rf_injury_change_model.pkl")
+
+@st.cache_resource
+def load_player_data():
+    return pd.read_csv("player_data.csv")
+
+# Mapping utilities
+position_mapping = {"PG": 1.0, "SG": 2.0, "SF": 3.0, "PF": 4.0, "C": 5.0}
+
+def encode_injury_type(type_str, known_types):
+    return known_types.index(type_str) if type_str in known_types else -1
+
+# App content
+def main():
+    st.title("🏀 Injury-Aware NBA Player Predictor")
+    st.write("Predict performance changes post-injury using injury type, position, and context.")
+
+    player_data = load_player_data()
+    model = load_rf_model()
+
+    injury_types = sorted(player_data["injury_type"].dropna().unique())
+    player_list = sorted(player_data["player_name"].dropna().unique())
+
+    player = st.selectbox("Select a Player", player_list)
+    injury = st.selectbox("Hypothetical Injury", injury_types)
+
+    player_row = player_data[player_data.player_name == player].iloc[0]
+    position_numeric = position_mapping.get(player_row["position"], 0)
+
+    # Sidebar for editable fields
+    st.sidebar.subheader("Adjust Inputs")
+    age = st.sidebar.slider("Age", int(player_row.age)-5, int(player_row.age)+5, int(player_row.age))
+    height = st.sidebar.slider("Height (cm)", 160, 220, int(player_row.player_height))
+    weight = st.sidebar.slider("Weight (kg)", 60, 140, int(player_row.player_weight))
+    injury_occurrences = st.sidebar.slider("Prior Injuries", 0, 10, int(player_row.injury_occurrences or 1))
+
+    avg_days_injured = player_data[player_data.injury_type == injury]["days_injured"].mean()
+    days_injured = st.sidebar.slider("Estimated Days Injured", 0, 365, int(avg_days_injured or 30))
+
+    # Prepare input vector
+    encoded_type = encode_injury_type(injury, injury_types)
+    input_data = pd.DataFrame([{
+        "age": age,
+        "player_height": height,
+        "player_weight": weight,
+        "position": position_numeric,
+        "injury_type": encoded_type,
+        "injury_occurrences": injury_occurrences,
+        "days_injured": days_injured
+    }])
+
+    expected_features = model.feature_names_in_
+    input_data = input_data.reindex(columns=expected_features, fill_value=0)
+
+    if st.button("Predict 🔮"):
+        preds = model.predict(input_data)
+        labels = ["Change in PTS", "Change in REB", "Change in AST"]
+        pred_df = pd.DataFrame(preds, columns=labels)
+
+        st.subheader("Predicted Performance Changes")
+        st.dataframe(pred_df.style.format("{:.2f}"))
+
+        fig = go.Figure()
+        for col in labels:
+            fig.add_trace(go.Bar(x=[col], y=pred_df[col], name=col))
+
+        fig.update_layout(title="Predicted Impact", template="plotly_dark")
+        st.plotly_chart(fig)
+
+if __name__ == "__main__":
+    main()