import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

# Custom Streamlit styling with sticky navbar
st.markdown(
    """
    <style>
    body {
        background-color: #1E1E1E;
        color: #FFFFFF;
        font-family: 'Arial', sans-serif;
    }
    .stButton>button {
        background-color: #4A90E2;
        color: #FFFFFF;
        border-radius: 15px;
        padding: 12px 24px;
        font-size: 16px;
        font-weight: bold;
    }
    .title {
        color: #64FFDA;
        text-shadow: 1px 1px #FF4C4C;
    }
    .stTabs [data-testid="stHorizontalBlock"] {
        position: sticky;
        top: 0;
        background-color: #1E1E1E;
        z-index: 10;
    }
    </style>
    """,
    unsafe_allow_html=True
)

# Load the League of Legends dataset
st.title("League of Legends Game Win Predictor")
st.markdown("#### Predict whether the Blue Team will dominate the game using Random Forest Classifier", unsafe_allow_html=True)

# Load dataset directly
file_path = 'high_diamond_ranked_10min.csv'
df = pd.read_csv(file_path)

# Preprocess data and train model (runs once)
df = df[['blueFirstBlood', 'blueKills', 'blueDeaths', 'blueAssists', 'blueTotalGold', 'blueTotalExperience', 'blueDragons', 'blueHeralds', 'blueTowersDestroyed', 'blueWins']]
df = df.dropna()

# Define features and target
X = df.drop('blueWins', axis=1)
y = df['blueWins']

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Scale data
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Train Logistic Regression Model
model = LogisticRegression(random_state=42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

# Top Tabs Navigation
tab1, tab2, tab3 = st.tabs(["📊 Dataset", "📈 Visualization", "🎮 Prediction"])

# Dataset Section
with tab1:
    st.write("### 📊 Dataset Preview")
    st.dataframe(df.head())

# Visualization Section
with tab2:
    # Display model performance
    accuracy = accuracy_score(y_test, y_pred)
    st.write("### 🔥 Model Performance")
    st.write(f"**✅ Model Accuracy:** {accuracy:.2f}")

    # Visualizing performance
    st.write("### 📊 Performance Breakdown")
    conf_matrix = confusion_matrix(y_test, y_pred)
    st.write("Confusion Matrix:")
    fig, ax = plt.subplots()
    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='coolwarm', ax=ax)
    st.pyplot(fig)

    # Feature Importance Visualization
    st.write("### 🔍 Feature Importance")
    feature_importance = abs(model.coef_[0])
    features = X.columns
    fig, ax = plt.subplots()
    ax.barh(features, feature_importance, color='#4A90E2')
    ax.set_title("Feature Importance for Blue Team Win Prediction")
    ax.set_xlabel("Importance")
    st.pyplot(fig)

# Prediction Section
with tab3:
    st.write("### 🎮 Predict Game Outcome")
    st.markdown("Adjust the stats below to simulate a match scenario!")

    first_blood = st.selectbox("Did Blue Team Get First Blood?", [0, 1])
    kills = st.slider("Blue Team Kills", min_value=0, max_value=50, value=5)
    deaths = st.slider("Blue Team Deaths", min_value=0, max_value=50, value=5)
    assists = st.slider("Blue Team Assists", min_value=0, max_value=50, value=10)
    total_gold = st.slider("Blue Team Total Gold", min_value=10000, max_value=100000, value=50000)
    total_exp = st.slider("Blue Team Total Experience", min_value=10000, max_value=100000, value=50000)
    dragons = st.slider("Blue Team Dragons Taken", min_value=0, max_value=5, value=1)
    heralds = st.slider("Blue Team Heralds Taken", min_value=0, max_value=2, value=0)
    towers = st.slider("Blue Team Towers Destroyed", min_value=0, max_value=11, value=2)

    if st.button("✨ Predict Win"):
        input_data = scaler.transform([[first_blood, kills, deaths, assists, total_gold, total_exp, dragons, heralds, towers]])
        prediction = model.predict(input_data)[0]
        prediction_proba = model.predict_proba(input_data)[0]

        st.subheader("🔮 Prediction Result")
        result_text = "🏅 Blue Team is likely to WIN! 🎉" if prediction == 1 else "⚔️ Blue Team is likely to LOSE. 😞"
        st.success(result_text) if prediction == 1 else st.error(result_text)
        st.write(f"Confidence: {prediction_proba[prediction]:.2f}")

        # Win/Loss Bar Chart
        st.write("### 📊 Win Probability Breakdown")
        fig, ax = plt.subplots()
        ax.bar(["Win", "Lose"], [prediction_proba[1], prediction_proba[0]], color=["#64FFDA", "#FF4C4C"])
        ax.set_ylim(0, 1)
        ax.set_ylabel("Probability")
        ax.set_title("Blue Team Win/Loss Probability")
        st.pyplot(fig)