Update modules/visuals.py

modules/visuals.py

@@ -1,38 +1,15 @@
-import folium
-from folium.plugins import HeatMap
 import streamlit as st
+import plotly.express as px
 
-def display_heatmap(df):
-    # Initialize the base map at a central location (around Hyderabad)
-    center_lat = 17.385044  # Example latitude for Hyderabad
-    center_lon = 78.486671  # Example longitude for Hyderabad
-    map = folium.Map(location=[center_lat, center_lon], zoom_start=6)
-
-    # Add heatmap layer
-    heat_data = []
-    for index, row in df.iterrows():
-        lat = row["Latitude"]
-        lon = row["Longitude"]
-        alert = row["Alert Level"]
-        
-        # Color mapping for heatmap intensity based on alert level
-        if alert == "Green":
-            color = [0, 255, 0]  # Green
-        elif alert == "Yellow":
-            color = [255, 255, 0]  # Yellow
-        elif alert == "Red":
-            color = [255, 0, 0]  # Red
-        
-        # Add data to the heatmap layer (latitude, longitude, intensity)
-        heat_data.append([lat, lon, 1 if alert == "Red" else 0.5 if alert == "Yellow" else 0.1])
-
-    # Create HeatMap with data
-    HeatMap(heat_data).add_to(map)
+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 the map in Streamlit
-    folium_static(map)
-
-def folium_static(map):
-    # This is a helper function to render Folium maps in Streamlit
-    from streamlit.components.v1 import html
-    html(map._repr_html_(), width=725, height=500)
+def display_heatmap(df):
+    st.subheader("🌡️ Fault Heatmap")
+    # Grouping the data by area (simplified) and plotting the heatmap
+    heatmap_data = df.groupby(['Pole ID', 'Alert Level']).size().unstack(fill_value=0)
+    fig = px.imshow(heatmap_data, title="Fault Distribution")
+    st.plotly_chart(fig)