Update app.py

app.py

@@ -4,6 +4,9 @@ from sklearn.cluster import KMeans
 from folium.plugins import MarkerCluster
 import requests
 from io import BytesIO
+import streamlit as st
+import folium
+from streamlit.components.v1 import html
 
 # Load data from Excel (directly from the URL)
 def load_data(url):
@@ -42,10 +45,11 @@ def find_data_center(df, n_clusters=1):
 
 # Create a map and plot the points
 def plot_map(df, center):
+    # Create map centered on the data center location
     map = folium.Map(location=[center[0][0], center[0][1]], zoom_start=10)
     marker_cluster = MarkerCluster().add_to(map)
     
-    # Add school locations
+    # Add school locations to the map
     for idx, row in df.iterrows():
         school_name = row.get("school_name", "No Name Provided")  # Ensure correct column access
         
@@ -60,7 +64,7 @@ def plot_map(df, center):
             icon=folium.Icon(color="blue", icon="info-sign")
         ).add_to(marker_cluster)
     
-    # Add data center
+    # Add data center location to the map
     folium.Marker(
         location=[center[0][0], center[0][1]],
         popup="Proposed Data Center",
@@ -75,8 +79,13 @@ def main():
     df = load_data(url)
     center = find_data_center(df)
     map = plot_map(df, center)
-    map.save("index.html")
-    print("Map has been saved as index.html.")
+
+    # Embed the map directly in the Streamlit app
+    map_html = map._repr_html_()  # Render the folium map as HTML
+    html(map_html, width=700, height=500)  # Adjust the size of the embedded map
+
+    st.title("Data Center Location Mapping")
+    st.write("This map shows school locations and proposed data center locations based on clustering.")
 
 if __name__ == "__main__":
     main()