Update app.py

app.py

@@ -1,30 +1,48 @@
 import streamlit as st
-from modules.simulator import simulate_data
-from modules.filters import apply_filters
-from modules.visuals import display_dashboard, display_heatmap
-from modules.ai_engine import AIEngine
+import folium
+from folium.plugins import HeatMap
+import pandas as pd
+import plotly.express as px
 
-st.set_page_config(page_title="Pole Monitoring Dashboard", layout="wide")
-st.title("📡 Pole Monitoring - Real-Time Simulation")
+# 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)
 
-st.sidebar.header("🛠️ Simulation Controls")
-num_poles = st.sidebar.slider("Number of Poles", min_value=5, max_value=50, value=10)
-simulate_faults = st.sidebar.checkbox("Simulate Random Faults", value=True)
+# 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])
 
-# Simulate pole data
-df = simulate_data(num_poles, simulate_faults)
+    display_fault_heatmap(df)
 
-# AI predictions (optional)
-ai_engine = AIEngine()
-df = ai_engine.predict_health(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.sidebar.header("📂 Filter Data")
-alert_filter = st.sidebar.multiselect("Alert Level", ["Green", "Yellow", "Red"], default=["Green", "Yellow", "Red"])
-filtered_df = apply_filters(df, alert_filter)
-
-# Dashboard and Heatmap Display
-display_dashboard(filtered_df)
-display_heatmap(filtered_df)
-
-st.subheader("📋 Pole Monitoring Table")
-st.dataframe(filtered_df, use_container_width=True)
+    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)