Create app.py

app.py

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+import pandas as pd
+import folium
+from sklearn.cluster import KMeans
+from folium.plugins import MarkerCluster
+
+# Load data from Excel
+def load_data(filename):
+    lat_long_data = pd.read_excel(filename, sheet_name='LatLong')
+    download_data = pd.read_excel(filename, sheet_name='DownloadUpload')
+    return pd.merge(lat_long_data, download_data, on='school_id', how='inner')
+
+# Perform clustering to find data center location
+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_
+
+# Create a map and plot the points
+def plot_map(df, center):
+    map = folium.Map(location=[center[0][0], center[0][1]], zoom_start=10)
+    marker_cluster = MarkerCluster().add_to(map)
+    # Add school locations
+    for idx, row in df.iterrows():
+        folium.Marker(
+            location=[row['latitude'], row['longitude']],
+            popup=f"Download Speed: {row['download_speed']} Mbps, Upload Speed: {row['upload_speed']} Mbps",
+            icon=folium.Icon(color='blue', icon='info-sign')
+        ).add_to(marker_cluster)
+    # Add data center
+    folium.Marker(
+        location=[center[0][0], center[0][1]],
+        popup="Proposed Data Center",
+        icon=folium.Icon(color='red', icon='cloud')
+    ).add_to(map)
+    return map
+
+# Main function to run the application
+def main():
+    filename = '/path/to/your/Copy of data barbados(1).xlsx'
+    df = load_data(filename)
+    center = find_data_center(df)
+    map = plot_map(df, center)
+    map.save('index.html')
+
+if __name__ == '__main__':
+    main()