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import random
import pandas as pd
import streamlit as st
# ---- Constants ----
TOTAL_POLES = 1000 # Simulating a subset (e.g., 1000) for demo purposes
# ---- Helper Functions ----
def simulate_pole(pole_id):
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) # degrees
vibration = round(random.uniform(0, 5), 2) # g-force
camera_status = random.choice(['Online', 'Offline'])
# Alert classification logic
alert_level = 'Green'
if tilt_angle > 30 or vibration > 3:
alert_level = 'Yellow'
if tilt_angle > 40 or vibration > 4.5:
alert_level = 'Red'
health_score = max(0, 100 - (tilt_angle + vibration * 10))
return {
'Pole ID': f'P{pole_id:05}',
'RFID': f'RFID{pole_id:05}',
'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
}
# ---- App Logic ----
st.set_page_config(page_title="Smart Pole Monitoring", layout="wide")
st.title("🚀 AI-Enabled Smart Pole Monitoring System")
# Sidebar controls
num_poles = st.sidebar.slider("Number of Poles to Simulate", 100, 2000, 1000, step=100)
# Simulate Data
with st.spinner("Simulating poles..."):
poles_data = [simulate_pole(i) for i in range(1, num_poles + 1)]
df = pd.DataFrame(poles_data)
# ---- Dashboard ----
col1, col2, col3 = st.columns(3)
col1.metric("Total Poles", num_poles)
col2.metric("Red Alerts", df[df['Alert Level'] == 'Red'].shape[0])
col3.metric("Power Insufficiencies", df[df['Power Status'] == 'Insufficient'].shape[0])
# ---- Data Table ----
st.subheader("📋 Pole-wise Table View")
with st.expander("Filter Options"):
alert_filter = st.multiselect("Alert Level", options=df['Alert Level'].unique(), default=df['Alert Level'].unique())
camera_filter = st.multiselect("Camera Status", options=df['Camera Status'].unique(), default=df['Camera Status'].unique())
filtered_df = df[(df['Alert Level'].isin(alert_filter)) & (df['Camera Status'].isin(camera_filter))]
st.dataframe(filtered_df, use_container_width=True)
# ---- Charts ----
st.subheader("📊 Energy Generation Comparison")
st.bar_chart(df[['Solar (kWh)', 'Wind (kWh)']].mean())
st.subheader("📈 Tilt vs. Vibration")
st.scatter_chart(df[['Tilt Angle (°)', 'Vibration (g)']])
st.success("Simulation Complete. Ready for AI + Salesforce integration 🚀")
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