<｜begin▁of▁sentence｜>### Instruction:
As a data analyst working for a restaurant chain, you are tasked with optimizing the efficiency of menu planning and kitchen operations. Leveraging the networkx library, you have obtained the Adj-Noun adjacency network representing ingredient relationships for various recipes from a GML file called "adjnoun.gml." Now, using the Louvain community detection algorithm available in the cdlib library, you are required to identify clusters of ingredients that frequently appear together in recipes.

Moreover, you are asked to calculate the average embeddedness of ingredients within their respective clusters to better understand how closely connected they are within their culinary communities.
### Response:
import networkx as nx
from cdlib import algorithms, evaluation

# Create a graph from the Adj-Noun adjacency network
G = nx.read_gml('adjnoun.gml')

# Apply the Louvain community detection algorithm to find communities in the network
communities = algorithms.louvain(G)

# Compute the average embeddedness of nodes within their communities
avg_embeddedness = evaluation.avg_embeddedness(G, communities)

print(avg_embeddedness)
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