Update app.py

app.py

@@ -15,86 +15,6 @@ st.set_page_config(
     layout="centered"
 )
 
-# Custom CSS for vibrant NBA sidebar header
-st.markdown(
-    """
-    <style>
-    body {
-        background: linear-gradient(to bottom, #0033a0, #ed174c); /* NBA team colors gradient */
-        font-family: 'Trebuchet MS', sans-serif;
-        margin: 0;
-        padding: 0;
-        color: white; /* Set text color to white */
-    }
-    .sidebar .sidebar-content {
-        background: linear-gradient(to bottom, #4B0082, #1E90FF); /* Purple to blue gradient */
-        border-radius: 10px;
-        padding: 10px;
-        color: #ffffff; /* Set sidebar text color to white */
-    }
-    .sidebar h2 {
-        background: linear-gradient(to right, #FF1493, #FF4500); /* Pink to red gradient */
-        color: white; /* Text color */
-        padding: 10px;
-        text-align: center;
-        font-size: 20px;
-        font-weight: bold;
-        border-radius: 5px;
-        text-shadow: 2px 2px #000000; /* Add shadow for better visibility */
-        margin-bottom: 15px;
-    }
-    .stButton > button {
-        background-color: #ffcc00; /* Bold yellow */
-        color: #0033a0; /* Button text color */
-        border: none;
-        border-radius: 5px;
-        padding: 10px 15px;
-        font-size: 16px;
-        transition: background-color 0.3s ease;
-    }
-    .stButton > button:hover {
-        background-color: #ffc107; /* Brighter yellow */
-        box-shadow: 0px 4px 6px rgba(0, 0, 0, 0.2);
-    }
-    .stMarkdown h1, .stMarkdown h2, .stMarkdown h3 {
-        color: #ffffff; /* Set headings color to white */
-        text-shadow: 2px 2px #000000; /* Add shadow for better visibility */
-    }
-    .block-container {
-        border-radius: 10px;
-        padding: 20px;
-        background-color: rgba(0, 0, 0, 0.8); /* Dark semi-transparent background */
-        color: #ffffff; /* Ensure text inside the container is white */
-    }
-    .dataframe {
-        background-color: rgba(255, 255, 255, 0.1); /* Transparent table background */
-        color: #ffffff; /* Table text color */
-        border-radius: 10px;
-    }
-    .stPlotlyChart {
-        background-color: rgba(0, 0, 0, 0.8); /* Match dark theme */
-        padding: 10px;
-        border-radius: 10px;
-        box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.5);
-    }
-    .styled-table {
-        width: 100%;
-        border-collapse: collapse;
-        margin: 25px 0;
-        font-size: 18px;
-        text-align: left;
-        border-radius: 5px 5px 0 0;
-        overflow: hidden;
-        color: #ffffff; /* Table text color */
-    }
-    .styled-table th, .styled-table td {
-        padding: 12px 15px;
-    }
-    </style>
-    """,
-    unsafe_allow_html=True
-)
-
 
 team_logo_paths = {
     "Cleveland Cavaliers": "Clevelan-Cavaliers-logo-2022.png",
@@ -210,27 +130,22 @@ def load_rf_model():
 
 # Main Streamlit app
 def main():
-    st.title("NBA Player Performance Predictor 🏀")
+    st.title("NBA Player Performance Predictor")
     st.write(
         """
-        Welcome to the **NBA Player Performance Predictor**! This app helps predict changes in a player's performance metrics
-        after experiencing a hypothetical injury. Simply input the details and see the magic happen!
+        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 and model
+    # Load player data
     player_data = load_player_data()
     rf_model = load_rf_model()
 
     # Sidebar inputs
-    st.sidebar.markdown(
-        """
-        <div style="padding: 10px; background: linear-gradient(to right, #6a11cb, #2575fc); color: white; border-radius: 10px;">
-        <h3>Player Details</h3>
-        </div>
-        """,
-        unsafe_allow_html=True
-    )
+    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)
 
@@ -290,49 +205,106 @@ def main():
                 expected_features = rf_model.feature_names_in_
                 input_data = input_data.reindex(columns=rf_model.feature_names_in_, fill_value=0)
 
-        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:
-            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_column_width=True)
-                except FileNotFoundError:
-                    st.error(f"Logo for {team_name} not found.")
-
-            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_df = pd.DataFrame(predictions, columns=prediction_columns)
-
-                st.subheader("Predicted Post-Injury Performance")
-                st.write(prediction_df)
-
-                fig = go.Figure()
-                for col in prediction_columns:
-                    fig.add_trace(go.Bar(
-                        x=[col],
-                        y=prediction_df[col],
-                        name=col,
-                        marker=dict(color=px.colors.qualitative.Plotly[prediction_columns.index(col)])
-                    ))
-
-                fig.update_layout(
-                    title="Predicted Performance Changes",
-                    xaxis_title="Metrics",
-                    yaxis_title="Change Value",
-                    template="plotly_dark"
-                )
-                st.plotly_chart(fig, use_container_width=True)
+                # 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.")
+
+    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")
+        )
+
+        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()