<｜begin▁of▁sentence｜>### Instruction:
As a community organizer, you're familiar with the importance of leveraging resources and networks to address community needs. Imagine you're working with a group of local businesses and residents to revitalize a neighborhood. You've gathered data on the relationships between businesses and residents, represented as a bipartite graph stored in a file named "bipartite_graph.gml". Each node in the graph represents either a business or a resident, categorized by a "type" attribute indicating whether it's a 0 or 1. 

To better understand the dynamics within the community, you want to extract the connections among businesses and residents separately. Utilizing the functionality provided by the igraph library, you aim to employ the bipartite_projection function to obtain two distinct one-mode graphs, each focusing on the interactions within a specific group. This will allow you to analyze and strategize more effectively, empowering the community to address social and economic challenges collaboratively.
### Response:
from igraph import Graph

# Load a bipartite graph from a file
g = Graph.Read('bipartite_graph.gml')

# Assuming 'type' is a vertex attribute where 0 indicates one set and 1 indicates the other
types = g.vs['type']

# Generate the bipartite projection
projection = g.bipartite_projection(types=types)

# Retrieve the projections of the bipartite graph
proj1, proj2 = projection

# Print the summary of both projections
print('First projection (type 0 vertices):', proj1.summary())
print('Second projection (type 1 vertices):', proj2.summary())
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