app.py

import random
import pandas as pd
import streamlit as st
import pydeck as pdk
from datetime import datetime, timedelta
from salesforce_integration import fetch_salesforce_data
from utils import process_data  # Optional, if you need data processing
# ---- Constants ----
POLES_PER_SITE = 12
SITES = {
    "Hyderabad": [17.385044, 78.486671],
    "Gadwal": [16.2351, 77.8052],
    "Kurnool": [15.8281, 78.0373],
    "Ballari": [12.9716, 77.5946]
}

# ---- Helper Functions ----
def generate_location(base_lat, base_lon):
    return [
        base_lat + random.uniform(-0.02, 0.02),
        base_lon + random.uniform(-0.02, 0.02)
    ]

def simulate_pole(pole_id, site_name):
    lat, lon = generate_location(*SITES[site_name])
    solar_kwh = round(random.uniform(3.0, 7.5), 2)
    wind_kwh = round(random.uniform(0.5, 2.0), 2)
    power_required = round(random.uniform(4.0, 8.0), 2)
    total_power = solar_kwh + wind_kwh
    power_status = 'Sufficient' if total_power >= power_required else 'Insufficient'

    tilt_angle = round(random.uniform(0, 45), 2)
    vibration = round(random.uniform(0, 5), 2)
    camera_status = random.choice(['Online', 'Offline'])

    alert_level = 'Green'
    anomaly_details = []
    if tilt_angle > 30 or vibration > 3:
        alert_level = 'Yellow'
        anomaly_details.append("Tilt or Vibration threshold exceeded.")
    if tilt_angle > 40 or vibration > 4.5:
        alert_level = 'Red'
        anomaly_details.append("Critical tilt or vibration detected.")

    health_score = max(0, 100 - (tilt_angle + vibration * 10))
    timestamp = datetime.now() - timedelta(hours=random.randint(0, 6))

    return {
        'Pole ID': f'{site_name[:3].upper()}-{pole_id:03}',
        'Site': site_name,
        'Latitude': lat,
        'Longitude': lon,
        'Solar (kWh)': solar_kwh,
        'Wind (kWh)': wind_kwh,
        'Power Required (kWh)': power_required,
        'Total Power (kWh)': total_power,
        'Power Status': power_status,
        'Tilt Angle (°)': tilt_angle,
        'Vibration (g)': vibration,
        'Camera Status': camera_status,
        'Health Score': round(health_score, 2),
        'Alert Level': alert_level,
        'Anomalies': "; ".join(anomaly_details),
        'Last Checked': timestamp.strftime('%Y-%m-%d %H:%M:%S')
    }

# ---- Streamlit UI ----
st.set_page_config(page_title="Smart Pole Monitoring", layout="wide")
st.title("🌍 Smart Renewable Pole Monitoring - Multi-Site")

selected_site = st.text_input("Enter site to view (Hyderabad, Gadwal, Kurnool, Ballari):", "Hyderabad")

if selected_site in SITES:
    with st.spinner(f"Simulating poles at {selected_site}..."):
        poles_data = [simulate_pole(i + 1, site) for site in SITES for i in range(POLES_PER_SITE)]
        df = pd.DataFrame(poles_data)
        site_df = df[df['Site'] == selected_site]

    # Summary Metrics
    col1, col2, col3 = st.columns(3)
    col1.metric("Total Poles", site_df.shape[0])
    col2.metric("Red Alerts", site_df[site_df['Alert Level'] == 'Red'].shape[0])
    col3.metric("Power Insufficiencies", site_df[site_df['Power Status'] == 'Insufficient'].shape[0])

    # Table View
    st.subheader(f"📋 Pole Data Table for {selected_site}")
    with st.expander("Filter Options"):
        alert_filter = st.multiselect("Alert Level", options=site_df['Alert Level'].unique(), default=site_df['Alert Level'].unique())
        camera_filter = st.multiselect("Camera Status", options=site_df['Camera Status'].unique(), default=site_df['Camera Status'].unique())

    filtered_df = site_df[(site_df['Alert Level'].isin(alert_filter)) & (site_df['Camera Status'].isin(camera_filter))]
    st.dataframe(filtered_df, use_container_width=True)

    # Charts
    st.subheader("📊 Energy Generation Comparison")
    st.bar_chart(site_df[['Solar (kWh)', 'Wind (kWh)']].mean())

    st.subheader("📈 Tilt vs. Vibration")
    st.scatter_chart(site_df[['Tilt Angle (°)', 'Vibration (g)']])

    # Map with Red Alerts
    st.subheader("📍 Red Alert Pole Locations")
    red_df = site_df[site_df['Alert Level'] == 'Red']
    if not red_df.empty:
        st.pydeck_chart(pdk.Deck(
            initial_view_state=pdk.ViewState(
                latitude=SITES[selected_site][0],
                longitude=SITES[selected_site][1],
                zoom=12,
                pitch=50
            ),
            layers=[
                pdk.Layer(
                    'ScatterplotLayer',
                    data=red_df,
                    get_position='[Longitude, Latitude]',
                    get_color='[255, 0, 0, 160]',
                    get_radius=100,
                )
            ]
        ))
        st.markdown("<h3 style='text-align: center;'>Red Alert Poles are Blinking</h3>", unsafe_allow_html=True)
    else:
        st.info("No red alerts at this time.")

else:
    st.warning("Invalid site. Please enter one of: Hyderabad, Gadwal, Kurnool, Ballari")

# Fetch data from Salesforce
poles_data = fetch_salesforce_data()

# Process the data (optional, based on your model needs)
processed_data = process_data(poles_data)

# Display the processed data in Streamlit
st.title("Poles Data Visualization")
st.write("Processed Poles Data", processed_data)