# Import necessary libraries
import streamlit as st

# scikit learn : https://scikit-learn.org/stable/
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
from sklearn.metrics.pairwise import linear_kernel

# nltk https://www.nltk.org/
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

# 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('''
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=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")
            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.")

st.markdown("For more information and updates, visit our [help page](https://huggingface.co/awacke1).")