import streamlit as st
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"
)

# 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",
    "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 = {
    "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("player_data.csv")

# Load Random Forest model
@st.cache_resource
def load_rf_model():
    return joblib.load("rf_injury_change_model.pkl")

# Main Streamlit app
def main():
    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!
        """
    )

    # Load player data and model
    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
    )
    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]
        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']
            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)

        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)

if __name__ == "__main__":
    main()