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import streamlit as st
import folium
from folium.plugins import HeatMap
import pandas as pd
import plotly.express as px
# Function to display the fault heatmap
def display_fault_heatmap(df):
st.subheader("🌡️ Fault Heatmap")
# Filter out red alert poles
red_alert_poles = df[df["Alert Level"] == "Red"]
# Create a folium map centered around a location (e.g., Hyderabad)
map_center = [17.385044, 78.486671] # Hyderabad latitude, longitude (you can adjust for other cities)
folium_map = folium.Map(location=map_center, zoom_start=7)
# Add markers for poles with red alerts
for _, row in red_alert_poles.iterrows():
folium.CircleMarker(
location=[row['Latitude'], row['Longitude']], # Adjust your data accordingly
radius=8,
color='red',
fill=True,
fill_color='red',
fill_opacity=0.7,
popup=f"Pole: {row['Pole ID']}, Anomalies: {row['Anomalies']}"
).add_to(folium_map)
# Display the map
st.write(folium_map)
# Function to display the dashboard
def display_dashboard(df):
st.subheader("📊 System Summary")
col1, col2 = st.columns(2)
col1.metric("Total Poles", df.shape[0])
col2.metric("🚨 Red Alerts", df[df['Alert Level'] == "Red"].shape[0])
display_fault_heatmap(df)
def display_charts(df):
st.subheader("⚙️ Energy Generation")
st.plotly_chart(px.bar(df, x="Pole ID", y=["Solar Gen (kWh)", "Wind Gen (kWh)"], barmode="group"))
st.subheader("📉 Tilt vs Vibration")
fig = px.scatter(df, x="Tilt (°)", y="Vibration (g)", color="Alert Level", hover_data=["Pole ID"])
st.plotly_chart(fig)
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