Update app.py

app.py

@@ -5,6 +5,7 @@ from folium.plugins import MarkerCluster
 import requests
 from io import BytesIO
 import streamlit as st
+import streamlit.components.v1 as components
 
 # Load data from Excel URL with error handling
 def load_data(url):
@@ -43,7 +44,7 @@ def find_data_center(df, n_clusters=1):
     kmeans = KMeans(n_clusters=n_clusters, random_state=0).fit(df[["latitude", "longitude"]])
     return kmeans.cluster_centers_
 
-# Plot the map with markers
+# Plot the map with markers and save as HTML
 def plot_map(df, center):
     if df.empty:
         st.write("Dataframe is empty, skipping map plotting")
@@ -72,7 +73,9 @@ def plot_map(df, center):
         icon=folium.Icon(color="red", icon="cloud")
     ).add_to(map)
     
-    return map
+    # Save the map as an HTML file
+    map.save("map.html")
+    return "map.html"
 
 # Calculate the impact of data center on latency and bandwidth
 def calculate_impact(df, center):
@@ -124,12 +127,12 @@ def main():
         st.write("Could not find data center, exiting application.")
         return
     
-    # Create the map and embed it in the Streamlit app
-    map = plot_map(df, center)
-    if map:
-        from io import BytesIO
-        map_html = map._repr_html_()  # Get the HTML representation of the map
-        st.markdown(map_html, unsafe_allow_html=True)
+    # Create the map and save it as an HTML file
+    map_file = plot_map(df, center)
+    
+    # Embed the map in the Streamlit app using st.components.v1.html
+    if map_file:
+        components.html(open(map_file, 'r').read(), height=600)
     
     # Calculate the impact of adding the data center
     latency_reduction, download_increase, upload_increase, avg_latency_before, avg_download_before, avg_upload_before = calculate_impact(df, center)