Index of documents for similarity matrix and community louvain graph - python-3.x

Question

I am running this script to calculate and plot, the similarity between some documents.
#!/usr/bin/python
# -*- coding: utf-8 -*-
import os
import codecs
import string, re
import nltk
import sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
from pathlib import Path
from matplotlib import cm as cm
import matplotlib.pyplot as plt
from sklearn.metrics.pairwise import cosine_similarity
path = "C:\\Users\\user\\Desktop\\texts\\dataset"
text_files = os.listdir(path)
#print (text_files)
tfidf_vectorizer = TfidfVectorizer()
documents = [open(f, encoding="utf-8").read() for f in text_files if f.endswith('.txt')]
sparse_matrix = tfidf_vectorizer.fit_transform(documents)
#with open('C:\\Users\\user\\Desktop\\texts\\results\\pairwise_similarity2.csv', 'w') as f:
# for item in pairwise_similarity:
# f.write("%s\n" % item)
# f.write('\n')
labels = []
for f in text_files:
if f.endswith('.txt'):
labels.append(f)
#print(labels)
pairwise_similarity = sparse_matrix * sparse_matrix.T
pairwise_similarity_array = pairwise_similarity.toarray()
fig, ax = plt.subplots(figsize=(20,20))
cax = ax.matshow(pairwise_similarity_array, interpolation='spline16')
ax.grid(True)
plt.title('News articles similarity matrix')
plt.xticks(range(23), labels, rotation=90);
plt.yticks(range(23), labels);
fig.colorbar(cax, ticks=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1])
plt.show()
Even though I' have created a labels list, I was wondering how can I access the index of documents to be able to associate a specific document with scores? This would also be helpful to track documents in other tasks as well. For example, I am also using the louvain community library to draw further assumption for the dataset but when trying to apply the labels list as labels it gives an error: AttributeError: 'list' object has no attribute 'items'
Here is the code and the output of community louvain
[![# load the karate club graph
G = nx.from_numpy_matrix(pairwise_similarity_array)
# compute the best partition
partition = community_louvain.best_partition(G)
#print(partition)
modularity = community_louvain.modularity(partition, G)
print(modularity)
# draw the graph
pos = nx.spring_layout(G)
# color the nodes according to their partition
cmap = cm.get_cmap('coolwarm', max(partition.values()) + 1)
nx.draw_networkx_nodes(G, pos, partition.keys(), node_size=100,
cmap=cmap, node_color=list(partition.values()))
nx.draw_networkx_edges(G, pos, alpha=0.5)
nx.draw_networkx_labels(G,pos, labels=doc_labels, font_size=12,font_family='sans-serif')
plt.show()
dendro = community_louvain.generate_dendrogram(G)][1]][1]