import streamlit as st
import pandas as pd
import numpy as np
import datetime
import time

# --- Simulate Data ---
def simulate_data(n=10, faults=True):
    today = datetime.date.today()
    poles = [f"Pole_{i+1:03}" for i in range(n)]
    data = []
    for pole in poles:
        solar = round(np.random.uniform(3.0, 7.5), 2)
        wind = round(np.random.uniform(0.5, 2.0), 2)
        required = round(np.random.uniform(1.0, 1.5), 2)
        total = solar + wind
        cam = np.random.choice(['Online', 'Offline'], p=[0.85, 0.15]) if faults else "Online"
        tilt = round(np.random.uniform(0, 12), 1)
        vib = round(np.random.uniform(0.1, 2.5), 2)
        sufficient = "Yes" if total >= required else "No"
        anomaly = []
        if faults:
            if solar < 4.0:
                anomaly.append("Low Solar Output")
            if wind < 0.7:
                anomaly.append("Low Wind Output")
            if tilt > 10:
                anomaly.append("Pole Tilt Risk")
            if vib > 2.0:
                anomaly.append("Vibration Alert")
            if cam == "Offline":
                anomaly.append("Camera Offline")
            if sufficient == "No":
                anomaly.append("Power Insufficient")
        alert = "Green"
        if len(anomaly) == 1:
            alert = "Yellow"
        elif len(anomaly) > 1:
            alert = "Red"
        data.append({
            "Pole ID": pole,
            "Date": today,
            "Solar Gen (kWh)": solar,
            "Wind Gen (kWh)": wind,
            "Power Required (kWh)": required,
            "Power Sufficient": sufficient,
            "Camera Status": cam,
            "Tilt (°)": tilt,
            "Vibration (g)": vib,
            "Anomalies": "; ".join(anomaly) if anomaly else "None",
            "Alert Level": alert
        })
    return pd.DataFrame(data)

# --- Filter Data ---
def apply_filters(df, alert_filter, cam_filter):
    filtered_df = df[df["Alert Level"].isin(alert_filter)]
    if cam_filter != "All":
        filtered_df = filtered_df[filtered_df["Camera Status"] == cam_filter]
    return filtered_df

# --- Dashboard ---
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['Alert Level'] == "Red"].shape[0])
    col3.metric("⚡ Power Issues", df[df['Power Sufficient'] == "No"].shape[0])

def display_charts(df):
    st.subheader("⚙️ Energy Generation Trends")
    st.bar_chart(df.set_index("Pole ID")[["Solar Gen (kWh)", "Wind Gen (kWh)"]])
    st.subheader("📉 Tilt vs Vibration")
    st.scatter_chart(df.rename(columns={"Tilt (°)": "Tilt", "Vibration (g)": "Vibration"}).set_index("Pole ID")[["Tilt", "Vibration"]])

# --- Streamlit App ---
st.set_page_config(page_title="Vedavathi Smart Pole Monitoring", layout="wide")
st.title("📡 Vedavathi Smart Pole Monitoring - Dynamic Stream")

# Sidebar Controls
st.sidebar.header("🛠️ Simulation Settings")
num_poles = st.sidebar.slider("Number of Poles", 5, 50, 10)
simulate_faults = st.sidebar.checkbox("Simulate Random Faults", value=True)
refresh_rate = st.sidebar.slider("Refresh Every (seconds)", 5, 60, 10)

st.sidebar.header("📂 Filter Data")
alert_filter = st.sidebar.multiselect("Alert Level", ["Green", "Yellow", "Red"], default=["Green", "Yellow", "Red"])
cam_filter = st.sidebar.selectbox("Camera Status", ["All", "Online", "Offline"], index=0)

# Placeholder for Live Data
placeholder = st.empty()

while True:
    df = simulate_data(num_poles, simulate_faults)
    filtered_df = apply_filters(df, alert_filter, cam_filter)

    with placeholder.container():
        display_dashboard(df)
        st.subheader("📋 Pole Monitoring Table")
        st.dataframe(filtered_df, use_container_width=True)
        display_charts(df)

    time.sleep(refresh_rate)