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"
    }
)

# 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 cluster_sentences(sentences, num_clusters):
    # Filter sentences with length over 10 characters
    sentences = [sentence for sentence in sentences if len(sentence) > 10]

    # 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

# Main code for UI
uploaded_file = st.file_uploader("📁 Choose a .txt file", type=['txt'])

if uploaded_file:
    file_text = uploaded_file.read().decode("utf-8")
else:
    file_text = ""

if file_text:
    text_without_timestamps = remove_timestamps(file_text)
    sentences = [sentence.strip() for sentence in text_without_timestamps.split('.') if len(sentence.strip()) > 10]

    with st.expander("📝 Sentence Clustering"):
        num_clusters = st.slider("Number of Clusters", min_value=2, max_value=10, value=5)
        clustered_sentences = cluster_sentences(sentences, num_clusters)

        for i, cluster in enumerate(clustered_sentences):
            st.text_area(f"Cluster {i+1}", value="\n".join(cluster), height=100)
            
            # Input for custom filename
            default_filename = f"Cluster_{i+1}_Output.txt"
            filename = st.text_input("Enter filename for download:", value=default_filename, key=f"filename_{i}")
            
            # Download button
            download_link = get_text_file_download_link("\n".join(cluster), filename, f"💾 Save Cluster {i+1}")
            st.markdown(download_link, unsafe_allow_html=True)

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