Create app.py

app.py

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+# Import necessary libraries
+import streamlit as st
+import re
+import nltk
+import os
+from nltk.corpus import stopwords
+from nltk import FreqDist
+from graphviz import Digraph
+
+# Set page configuration with a title and favicon
+st.set_page_config(
+    page_title="📺Transcript📜EDA🔍NLTK",
+    page_icon="🌠",
+    layout="wide",
+    initial_sidebar_state="expanded",
+    menu_items={
+        'Get Help': 'https://huggingface.co/awacke1',
+        'Report a bug': "https://huggingface.co/spaces/awacke1/WebDataDownload",
+        'About': "# Midjourney: https://discord.com/channels/@me/997514686608191558"
+    }
+)
+
+st.markdown('''🔍 **Exploratory Data Analysis (EDA)** 📊:     - Dive deep into the sea of data with our EDA feature, unveiling hidden patterns 🕵️‍♂️ and insights 🧠 in your transcripts. Transform raw data into a treasure trove of information 🏆.
+📜 **Natural Language Toolkit (NLTK)** 🛠️:     - Harness the power of NLTK to process and understand human language 🗣️. From tokenization to sentiment analysis, our toolkit is your compass 🧭 in the vast landscape of natural language processing (NLP).
+📺 **Transcript Analysis** 📈:     - Elevate your text analysis with our advanced transcript analysis tools. Whether it's speech recognition 🎙️ or thematic extraction 🌐, turn your audiovisual content into actionable insights 🔑.''')
+
+# Download NLTK resources
+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
+        if before_word:  graph.node(f'before{index}', before_word, shape='box') # 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 after_word: graph.node(f'after{index}', after_word, shape='diamond') # 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():
+    # Exclude specific files
+    excluded_files = {'freeze.txt', 'requirements.txt', 'packages.txt', 'pre-requirements.txt'}
+    
+    # List all .txt files excluding the ones in excluded_files
+    example_files = [f for f in os.listdir() if f.endswith('.txt') and f not in excluded_files]
+    
+    # Check if there are any files to select from
+    if example_files:
+        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()
+    else:
+        st.write("No suitable example files found.")
+    
+    return None
+    
+# Load example files
+def load_example_files_old():
+    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
+
+# Main code for UI
+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)
+
+    with st.expander("📊 Top 10 High Information Words"):
+        st.write(top_words)
+
+    with st.expander("📈 Relationship Graph"):
+        display_relationship_graph(top_words)
+
+    context_words = extract_context_words(text_without_timestamps, top_words)
+
+    with st.expander("🔗 Context Graph"):
+        display_context_graph(context_words)
+
+    with st.expander("📑 Context Table"):
+        display_context_table(context_words)
+
+    with st.expander("Innovation Outlines"):
+        showInnovationOutlines()