import streamlit as st
import seaborn as sns
import matplotlib.pyplot as plt

def display_dashboard(df):
    st.subheader("📊 System Summary")
    col1, col2, col3 = st.columns(3)
    col1.metric("Total Poles", df.shape[0])
    col2.metric("🚨 Red Alerts", df[df['AlertLevel']=="Red"].shape[0])
    col3.metric("⚡ Power Issues", df[df['PowerSufficient']=="No"].shape[0])

def display_charts(df):
    st.subheader("⚙️ Energy Generation Trends")
    st.bar_chart(df.set_index("PoleID")[["SolarGen(kWh)", "WindGen(kWh)"]])
    st.subheader("📉 Tilt vs Vibration")
    st.scatter_chart(df.rename(columns={"Tilt(°)": "Tilt", "Vibration(g)": "Vibration"}).set_index("PoleID")[["Tilt", "Vibration"]])
    
def display_heatmap(df):
    st.subheader("🔥 Correlation Heatmap")
    # Select numerical columns for correlation
    numerical_cols = ["SolarGen(kWh)", "WindGen(kWh)", "Tilt(°)", "Vibration(g)"]
    corr_matrix = df[numerical_cols].corr()
    
    # Create heatmap using seaborn
    fig, ax = plt.subplots(figsize=(8, 6))
    sns.heatmap(corr_matrix, annot=True, cmap="coolwarm", vmin=-1, vmax=1, center=0, ax=ax)
    plt.title("Correlation Heatmap of Pole Metrics")
    
    # Display in Streamlit
    st.pyplot(fig)