Update backup.app.py

backup.app.py

@@ -1,6 +1,7 @@
 import streamlit as st
 import re
 import nltk
+import os
 from nltk.corpus import stopwords
 from nltk import FreqDist
 from graphviz import Digraph
@@ -9,56 +10,80 @@ nltk.download('punkt')
 nltk.download('stopwords')
 
 def remove_timestamps(text):
-    return re.sub(r'\d{1,2}:\d{2}\n.*\n', '', text)  # Updated regex pattern
-
-def process_text(text):
-    lines = text.split("\n")
-    processed_lines = []
-
-    for line in lines:
-        if line:
-            processed_lines.append(line)
-
-    outline = ""
-    for i, line in enumerate(processed_lines):
-        if i % 2 == 0:
-            outline += f"**{line}**\n"
-        else:
-            outline += f"- {line} 😄\n"
-
-    return outline
+    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)
-    high_information_words = [word for word, _ in freq_dist.most_common(top_n)]
-
-    return high_information_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'])
 
-if uploaded_file:
+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)
@@ -66,4 +91,11 @@ if uploaded_file:
     st.write(top_words)
 
     st.markdown("**Relationship Graph:**")
-    display_relationship_graph(top_words)
+    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)