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import gradio as gr
from transformers import pipeline
import networkx as nx
import numpy as np
import re
import nltk
from nltk.tokenize import sent_tokenize
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
# Download NLTK data
nltk.download('punkt')
# Load Transformer model for abstractive summarization
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
# Extractive summarization using TextRank
def extractive_summarization(text, num_sentences=3):
sentences = sent_tokenize(text)
# Handle cases where input text is too short
if len(sentences) <= num_sentences:
return "Text is too short for extractive summarization."
try:
vectorizer = TfidfVectorizer(stop_words="english")
sentence_vectors = vectorizer.fit_transform(sentences)
# Handle cases where vectorization fails due to low variation in text
if sentence_vectors.shape[0] < num_sentences:
return "Insufficient unique content for extractive summarization."
similarity_matrix = cosine_similarity(sentence_vectors)
graph = nx.from_numpy_array(similarity_matrix)
scores = nx.pagerank(graph)
ranked_sentences = sorted(((scores[i], s) for i, s in enumerate(sentences)), reverse=True)
return " ".join([s for _, s in ranked_sentences[:num_sentences]])
except Exception as e:
return f"Error in extractive summarization: {str(e)}"
# Abstractive summarization using BART
def abstractive_summarization(text, length):
if len(text.split()) < 30:
return "Text is too short for summarization."
max_length = {"short": 50, "medium": 100, "long": 150}[length]
summary = summarizer(text, max_length=max_length, min_length=30, do_sample=False)[0]['summary_text']
return summary
# Main function
def summarize_text(text, method, length):
if method == "Abstractive (BART)":
return abstractive_summarization(text, length)
else:
num_sentences = {"short": 2, "medium": 4, "long": 6}[length]
return extractive_summarization(text, num_sentences)
# Function to process file upload
def process_file(file):
return file.read().decode("utf-8")
# UI with Gradio
with gr.Blocks(theme=gr.themes.Soft()) as iface:
gr.Markdown("# 📄 AI-Powered Text Summarizer")
gr.Markdown("Summarize long articles, news, and research papers using advanced NLP models.")
with gr.Row():
method_choice = gr.Radio(["Abstractive (BART)", "Extractive (TextRank)"], label="Summarization Type", value="Abstractive (BART)")
length_choice = gr.Radio(["short", "medium", "long"], label="Summary Length", value="medium")
text_input = gr.Textbox(lines=8, placeholder="Paste long text here...", label="Input Text")
file_input = gr.File(label="Or Upload a .txt file")
summarize_button = gr.Button("Summarize ✨")
summary_output = gr.Textbox(lines=6, label="Summarized Text", interactive=False)
file_input.change(process_file, inputs=file_input, outputs=text_input)
summarize_button.click(summarize_text, inputs=[text_input, method_choice, length_choice], outputs=summary_output)
# Launch app
if __name__ == "__main__":
iface.launch()
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