<｜begin▁of▁sentence｜>### Instruction:
Given the Astrophysics collaborations Graph which you can read from a .gml file named astro-ph.gml, can you use the I-Louvain algorithm in cdlib for attributed community detection, including randomly assigned attributes of 'interest' (with options 'Astrophysics', 'Cosmology', 'Galaxy') and 'seniority' (with options 'Junior', 'Mid-Career', 'Senior') for each node? Can you then generate and print a JSON representation of the detected communities?
### Response:
import networkx as nx
from cdlib import algorithms
import random

# Load the astrophysics collaboration network
G = nx.read_gml('astro-ph.gml')

# Define scalar attributes for the nodes
labels = {}
for node in G.nodes():
    labels[node] = {"interest": random.choice(['Astrophysics', 'Cosmology', 'Galaxy']), 
                    "seniority": random.choice(['Junior', 'Mid-Career', 'Senior'])}

# Apply the I-Louvain algorithm for attributed community detection
communities = algorithms.ilouvain(G, labels)

# Convert the result to JSON format
communities_json = communities.to_json()

print(communities_json)
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