Update app.py

app.py

@@ -4,11 +4,12 @@ from sklearn.cluster import KMeans
 from folium.plugins import MarkerCluster
 import requests
 from io import BytesIO
+import streamlit as st
 
 # Load data from Excel URL with error handling
 def load_data(url):
     try:
-        print("Loading data from:", url)
+        st.write(f"Loading data from {url}...")
         response = requests.get(url)
         if response.status_code == 200:
             lat_long_data = pd.read_excel(BytesIO(response.content), sheet_name="lat long", engine='openpyxl')
@@ -25,19 +26,19 @@ def load_data(url):
             
             # Strip any extra spaces from column names
             merged_data.columns = merged_data.columns.str.strip()
-            print("Data loaded successfully")
+            st.write("Data loaded successfully")
             return merged_data
         else:
-            print(f"Failed to load data. Status code: {response.status_code}")
+            st.write(f"Failed to load data. Status code: {response.status_code}")
             return pd.DataFrame()
     except Exception as e:
-        print(f"Error loading data: {e}")
+        st.write(f"Error loading data: {e}")
         return pd.DataFrame()
 
 # Perform clustering to find data center location
 def find_data_center(df, n_clusters=1):
     if df.empty:
-        print("Dataframe is empty, skipping clustering")
+        st.write("Dataframe is empty, skipping clustering")
         return None
     kmeans = KMeans(n_clusters=n_clusters, random_state=0).fit(df[["latitude", "longitude"]])
     return kmeans.cluster_centers_
@@ -45,7 +46,7 @@ def find_data_center(df, n_clusters=1):
 # Plot the map with markers
 def plot_map(df, center):
     if df.empty:
-        print("Dataframe is empty, skipping map plotting")
+        st.write("Dataframe is empty, skipping map plotting")
         return None
     
     map = folium.Map(location=[center[0][0], center[0][1]], zoom_start=10)
@@ -76,7 +77,7 @@ def plot_map(df, center):
 # Calculate the impact of data center on latency and bandwidth
 def calculate_impact(df, center):
     if df.empty:
-        print("Dataframe is empty, skipping impact calculation")
+        st.write("Dataframe is empty, skipping impact calculation")
         return None
     avg_latency_before = df['latency'].mean()
     avg_download_before = df['download_speed'].mean()
@@ -105,7 +106,8 @@ def display_impact(latency_reduction, download_increase, upload_increase, avg_la
         }
     }
     
-    return impact_data
+    st.write("Impact of Data Center on Latency and Bandwidth:")
+    st.write(impact_data)
 
 # Main function to run the application
 def main():
@@ -113,28 +115,26 @@ def main():
     df = load_data(url)
     
     if df.empty:
-        print("No data to process, exiting application.")
+        st.write("No data to process, exiting application.")
         return
     
     # Find the data center location using clustering
     center = find_data_center(df)
     if center is None:
-        print("Could not find data center, exiting application.")
+        st.write("Could not find data center, exiting application.")
         return
     
-    # Create the map and save it
+    # Create the map and embed it in the Streamlit app
     map = plot_map(df, center)
     if map:
-        map.save("index.html")
+        from io import BytesIO
+        map_html = map._repr_html_()  # Get the HTML representation of the map
+        st.markdown(map_html, unsafe_allow_html=True)
     
     # 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)
     if latency_reduction is not None:
-        impact_data = display_impact(latency_reduction, download_increase, upload_increase, avg_latency_before, avg_download_before, avg_upload_before)
-        print("Impact of Data Center on Latency and Bandwidth:")
-        print(impact_data)
-
-    print("Map has been saved as index.html.")
+        display_impact(latency_reduction, download_increase, upload_increase, avg_latency_before, avg_download_before, avg_upload_before)
 
 if __name__ == "__main__":
     main()