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import streamlit as st
import re
import nltk
import os
from nltk.corpus import stopwords
from nltk import FreqDist
from graphviz import Digraph
nltk.download('punkt')
nltk.download('stopwords')
def remove_timestamps(text):
return re.sub(r'\d{1,2}:\d{2}\n.*\n', '', text)
def extract_high_information_words(text, top_n=10):
words = nltk.word_tokenize(text)
words = [word.lower() for word in words if word.isalpha()]
stop_words = set(stopwords.words('english'))
filtered_words = [word for word in words if word not in stop_words]
freq_dist = FreqDist(filtered_words)
return [word for word, _ in freq_dist.most_common(top_n)]
def create_relationship_graph(words):
graph = Digraph()
for index, word in enumerate(words):
graph.node(str(index), word)
if index > 0:
graph.edge(str(index - 1), str(index), label=str(index))
return graph
def display_relationship_graph(words):
graph = create_relationship_graph(words)
st.graphviz_chart(graph)
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)
def load_example_files():
example_files = [f for f in os.listdir() if f.endswith('.txt')]
selected_file = st.selectbox("Select an example file:", example_files)
if st.button(f"Load {selected_file}"):
with open(selected_file, 'r', encoding="utf-8") as file:
return file.read()
return None
uploaded_file = st.file_uploader("Choose a .txt file", type=['txt'])
example_text = load_example_files()
if example_text:
file_text = example_text
elif uploaded_file:
file_text = uploaded_file.read().decode("utf-8")
else:
file_text = ""
if file_text:
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)
st.markdown("**Relationship Graph:**")
display_relationship_graph(top_words)
context_words = extract_context_words(text_without_timestamps, top_words)
st.markdown("**Context Graph:**")
display_context_graph(context_words)
st.markdown("**Context Table:**")
display_context_table(context_words)
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