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def extract_context_words(text, high_information_words):
words = nltk.word_tokenize(text)
context_words = []
for index, word in enumerate(words):
if word.lower() in high_information_words:
before_word = words[index - 1] if index > 0 else None
after_word = words[index + 1] if index < len(words) - 1 else None
context_words.append((before_word, word, after_word))
return context_words
def create_context_graph(context_words):
graph = Digraph()
for index, (before_word, high_info_word, after_word) in enumerate(context_words):
graph.node(f'before{index}', before_word, shape='box') if before_word else None
graph.node(f'high{index}', high_info_word, shape='ellipse')
graph.node(f'after{index}', after_word, shape='diamond') if after_word else None
if before_word:
graph.edge(f'before{index}', f'high{index}')
if after_word:
graph.edge(f'high{index}', f'after{index}')
return graph
def display_context_graph(context_words):
graph = create_context_graph(context_words)
st.graphviz_chart(graph)
def display_context_table(context_words):
table = "| Before | High Info Word | After |\n|--------|----------------|-------|\n"
for before, high, after in context_words:
table += f"| {before if before else ''} | {high} | {after if after else ''} |\n"
st.markdown(table)
# ...
if uploaded_file:
file_text = uploaded_file.read().decode("utf-8")
text_without_timestamps = remove_timestamps(file_text)
top_words = extract_high_information_words(text_without_timestamps, 10)
st.markdown("**Top 10 High Information Words:**")
st.write(top_words)
context_words = extract_context_words(text_without_timestamps, top_words)
st.markdown("**Relationship Graph:**")
display_relationship_graph(top_words)
st.markdown("**Context Graph:**")
display_context_graph(context_words)
st.markdown("**Context Table:**")
display_context_table(context_words)
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