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Update app.py
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app.py
CHANGED
@@ -5,36 +5,48 @@ from folium.plugins import MarkerCluster
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import requests
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from io import BytesIO
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# Load data from Excel URL
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def load_data(url):
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# Perform clustering to find data center location
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def find_data_center(df, n_clusters=1):
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kmeans = KMeans(n_clusters=n_clusters, random_state=0).fit(df[["latitude", "longitude"]])
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return kmeans.cluster_centers_
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# Plot the map with markers
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def plot_map(df, center):
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map = folium.Map(location=[center[0][0], center[0][1]], zoom_start=10)
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marker_cluster = MarkerCluster().add_to(map)
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@@ -63,7 +75,9 @@ def plot_map(df, center):
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# Calculate the impact of data center on latency and bandwidth
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def calculate_impact(df, center):
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avg_latency_before = df['latency'].mean()
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avg_download_before = df['download_speed'].mean()
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avg_upload_before = df['upload_speed'].mean()
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@@ -98,20 +112,27 @@ def main():
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url = "https://huggingface.co/spaces/engralimalik/lace/resolve/main/data%20barbados.xlsx" # URL of your Excel file
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df = load_data(url)
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# Find the data center location using clustering
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center = find_data_center(df)
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# Create the map and save it
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map = plot_map(df, center)
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map
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# Calculate the impact of adding the data center
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latency_reduction, download_increase, upload_increase, avg_latency_before, avg_download_before, avg_upload_before = calculate_impact(df, center)
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print(impact_data)
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print("Map has been saved as index.html.")
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import requests
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from io import BytesIO
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# Load data from Excel URL with error handling
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def load_data(url):
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try:
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print("Loading data from:", url)
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response = requests.get(url)
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if response.status_code == 200:
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lat_long_data = pd.read_excel(BytesIO(response.content), sheet_name="lat long", engine='openpyxl')
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measurement_data = pd.read_excel(BytesIO(response.content), sheet_name="measurement data", engine='openpyxl')
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# Merge data on school_id_giga
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merged_data = pd.merge(
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lat_long_data,
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measurement_data,
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left_on="school_id_giga",
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right_on="school_id_giga",
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how="inner"
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)
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# Strip any extra spaces from column names
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merged_data.columns = merged_data.columns.str.strip()
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print("Data loaded successfully")
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return merged_data
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else:
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print(f"Failed to load data. Status code: {response.status_code}")
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return pd.DataFrame()
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except Exception as e:
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print(f"Error loading data: {e}")
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return pd.DataFrame()
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# Perform clustering to find data center location
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def find_data_center(df, n_clusters=1):
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if df.empty:
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print("Dataframe is empty, skipping clustering")
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return None
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kmeans = KMeans(n_clusters=n_clusters, random_state=0).fit(df[["latitude", "longitude"]])
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return kmeans.cluster_centers_
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# Plot the map with markers
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def plot_map(df, center):
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if df.empty:
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print("Dataframe is empty, skipping map plotting")
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return None
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map = folium.Map(location=[center[0][0], center[0][1]], zoom_start=10)
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marker_cluster = MarkerCluster().add_to(map)
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# Calculate the impact of data center on latency and bandwidth
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def calculate_impact(df, center):
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if df.empty:
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print("Dataframe is empty, skipping impact calculation")
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return None
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avg_latency_before = df['latency'].mean()
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avg_download_before = df['download_speed'].mean()
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avg_upload_before = df['upload_speed'].mean()
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url = "https://huggingface.co/spaces/engralimalik/lace/resolve/main/data%20barbados.xlsx" # URL of your Excel file
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df = load_data(url)
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if df.empty:
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print("No data to process, exiting application.")
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return
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# Find the data center location using clustering
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center = find_data_center(df)
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if center is None:
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print("Could not find data center, exiting application.")
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return
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# Create the map and save it
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map = plot_map(df, center)
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if map:
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map.save("index.html")
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# Calculate the impact of adding the data center
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latency_reduction, download_increase, upload_increase, avg_latency_before, avg_download_before, avg_upload_before = calculate_impact(df, center)
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if latency_reduction is not None:
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impact_data = display_impact(latency_reduction, download_increase, upload_increase, avg_latency_before, avg_download_before, avg_upload_before)
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print("Impact of Data Center on Latency and Bandwidth:")
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print(impact_data)
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print("Map has been saved as index.html.")
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