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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 | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from sklearn.cluster import KMeans | |
| from sklearn.metrics.pairwise import linear_kernel | |
| from io import BytesIO | |
| import base64 | |
| # 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): | |
| if before_word: | |
| graph.node(f'before{index}', before_word, shape='box') | |
| graph.node(f'high{index}', high_info_word, shape='ellipse') | |
| if after_word: | |
| graph.node(f'after{index}', after_word, shape='diamond') | |
| 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 | |
| def cluster_sentences(sentences, num_clusters): | |
| # Filter sentences with length over 10 characters | |
| sentences = [sentence for sentence in sentences if len(sentence) > 10] | |
| # Check if the number of sentences is less than the desired number of clusters | |
| if len(sentences) < num_clusters: | |
| # If so, adjust the number of clusters to match the number of sentences | |
| num_clusters = len(sentences) | |
| # Vectorize the sentences | |
| vectorizer = TfidfVectorizer() | |
| X = vectorizer.fit_transform(sentences) | |
| # Perform k-means clustering | |
| kmeans = KMeans(n_clusters=num_clusters, random_state=42) | |
| kmeans.fit(X) | |
| # Calculate the centroid of each cluster | |
| cluster_centers = kmeans.cluster_centers_ | |
| # Group sentences by cluster and calculate similarity to centroid | |
| clustered_sentences = [[] for _ in range(num_clusters)] | |
| for i, label in enumerate(kmeans.labels_): | |
| similarity = linear_kernel(cluster_centers[label:label+1], X[i:i+1]).flatten()[0] | |
| clustered_sentences[label].append((similarity, sentences[i])) | |
| # Order sentences within each cluster based on their similarity to the centroid | |
| for cluster in clustered_sentences: | |
| cluster.sort(reverse=True) # Sort based on similarity (descending order) | |
| # Return the ordered clustered sentences without similarity scores for display | |
| return [[sentence for _, sentence in cluster] for cluster in clustered_sentences] | |
| # Function to convert text to a downloadable file | |
| def get_text_file_download_link(text_to_download, filename='Output.txt', button_label="💾 Save"): | |
| buffer = BytesIO() | |
| buffer.write(text_to_download.encode()) | |
| buffer.seek(0) | |
| b64 = base64.b64encode(buffer.read()).decode() | |
| href = f'<a href="data:file/txt;base64,{b64}" download="{filename}" style="margin-top:20px;">{button_label}</a>' | |
| return href | |
| def get_high_info_words_per_cluster(cluster_sentences, num_words=5): | |
| cluster_high_info_words = [] | |
| for cluster in cluster_sentences: | |
| cluster_text = " ".join(cluster) | |
| high_info_words = extract_high_information_words(cluster_text, num_words) | |
| cluster_high_info_words.append(high_info_words) | |
| return cluster_high_info_words | |
| # 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("📝 Sentence Clustering", expanded=True): | |
| sentences = [line.strip() for line in file_text.split('\n') if len(line.strip()) > 10] | |
| num_sentences = len(sentences) | |
| st.write(f"Total Sentences: {num_sentences}") | |
| num_clusters = st.slider("Number of Clusters", min_value=2, max_value=10, value=5) | |
| clustered_sentences = cluster_sentences(sentences, num_clusters) | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.subheader("Original Text") | |
| original_text = "\n".join(sentences) | |
| st.text_area("Original Sentences", value=original_text, height=400) | |
| with col2: | |
| st.subheader("Clustered Text") | |
| clustered_text = "" | |
| cluster_high_info_words = get_high_info_words_per_cluster(clustered_sentences) | |
| for i, cluster in enumerate(clustered_sentences): | |
| cluster_text = "\n".join(cluster) | |
| high_info_words = ", ".join(cluster_high_info_words[i]) | |
| clustered_text += f"Cluster {i+1} (High Info Words: {high_info_words}):\n{cluster_text}\n\n" | |
| st.text_area("Clustered Sentences", value=clustered_text, height=400) | |
| # Verify that all sentences are accounted for in the clustered output | |
| clustered_sentences_flat = [sentence for cluster in clustered_sentences for sentence in cluster] | |
| if set(sentences) == set(clustered_sentences_flat): | |
| st.write("✅ All sentences are accounted for in the clustered output.") | |
| else: | |
| st.write("❌ Some sentences are missing in the clustered output.") | |
| st.markdown("For more information and updates, visit our [help page](https://huggingface.co/awacke1).") |