Create app.py

app.py

@@ -0,0 +1,262 @@
+# Import necessary libraries
+import streamlit as st
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.cluster import KMeans
+from sklearn.metrics.pairwise import linear_kernel, cosine_similarity
+import nltk
+from nltk.corpus import stopwords
+from nltk import FreqDist
+import re
+import os
+import base64
+from graphviz import Digraph
+from io import BytesIO
+import networkx as nx
+import matplotlib.pyplot as plt
+
+# 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/awacke1",
+        'About': "https://huggingface.co/awacke1"
+    }
+)
+
+st.markdown('''
+1. 🔍 **Transcript Insights Using Exploratory Data Analysis (EDA)** 📊 - Unveil hidden patterns 🕵️‍♂️ and insights 🧠 in your transcripts. 🏆.
+2. 📜 **Natural Language Toolkit (NLTK)** 🛠️:- your compass 🧭 in the vast landscape of NLP.
+3. 📺 **Transcript Analysis** 📈:Speech recognition 🎙️ and thematic extraction 🌐, audiovisual content to 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=word)  # Add word as edge label
+    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}', label=before_word)  # Add before_word as edge label
+        if after_word:
+            graph.edge(f'high{index}', f'after{index}', label=after_word)  # Add after_word as edge label
+    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]
+
+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
+
+def plot_cluster_words(cluster_sentences):
+    for i, cluster in enumerate(cluster_sentences):
+        cluster_text = " ".join(cluster)
+        words = re.findall(r'\b[a-z]{4,}\b', cluster_text)
+        word_freq = FreqDist(words)
+        top_words = [word for word, _ in word_freq.most_common(20)]
+        
+        vectorizer = TfidfVectorizer()
+        X = vectorizer.fit_transform(top_words)
+        word_vectors = X.toarray()
+        
+        similarity_matrix = cosine_similarity(word_vectors)
+        
+        G = nx.from_numpy_array(similarity_matrix)
+        pos = nx.spring_layout(G, k=0.5)
+        
+        plt.figure(figsize=(8, 6))
+        nx.draw_networkx(G, pos, node_size=500, font_size=12, font_weight='bold', with_labels=True, labels={i: word for i, word in enumerate(top_words)}, node_color='skyblue', edge_color='gray')  # Add word labels to nodes
+        plt.axis('off')
+        plt.title(f"Cluster {i+1} Word Arrangement")
+        
+        st.pyplot(plt)
+        
+        st.markdown(f"**Cluster {i+1} Details:**")
+        st.markdown(f"Top Words: {', '.join(top_words)}")
+        st.markdown(f"Number of Sentences: {len(cluster)}")
+        st.markdown("---")
+
+# 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")
+        clusters = ""
+        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])
+            clusters += f"Cluster {i+1} (High Info Words: {high_info_words})\n"
+            clustered_text += f"Cluster {i+1} (High Info Words: {high_info_words}):\n{cluster_text}\n\n"
+
+        st.text_area("Clusters", value=clusters, height=200)
+        st.text_area("Clustered Sentences", value=clustered_text, height=200)
+
+        # 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.")
+    
+    plot_cluster_words(clustered_sentences)
+
+st.markdown("For more information and updates, visit our [help page](https://huggingface.co/awacke1).")