|
from flask import Flask, render_template, request, redirect, url_for, flash, jsonify |
|
from PyPDF2 import PdfReader |
|
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
|
|
|
import os |
|
|
|
app = Flask(__name__) |
|
app.secret_key = "supersecretkey" |
|
|
|
UPLOAD_FOLDER = 'uploads' |
|
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER |
|
|
|
if not os.path.exists(UPLOAD_FOLDER): |
|
os.makedirs(UPLOAD_FOLDER) |
|
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn") |
|
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn") |
|
|
|
def extract_text_from_pdf(pdf_path): |
|
reader = PdfReader(pdf_path) |
|
text = "" |
|
for page in reader.pages: |
|
text += page.extract_text() |
|
return text |
|
|
|
@app.route('/') |
|
def index(): |
|
return render_template('index.html') |
|
|
|
@app.route('/upload', methods =['POST']) |
|
def upload_file(): |
|
if 'file' not in request.files: |
|
flash("No File Path") |
|
return redirect(url_for('index')) |
|
file = request.files['file'] |
|
|
|
|
|
if file.filename == '': |
|
flash("Not Selected File") |
|
return redirect(url_for('index')) |
|
|
|
if file and file.filename.endswith('.pdf'): |
|
file.save(os.path.join(app.config['UPLOAD_FOLDER'], file.filename)) |
|
flash("File Successfully Uploaded") |
|
file.save(file) |
|
text = extract_text_from_pdf(file) |
|
|
|
inputs = tokenizer(text, max_length=1024, return_tensors="pt", truncation=True) |
|
summary_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=300, early_stopping=True) |
|
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) |
|
|
|
return render_template('index.html', summary=summary) |
|
|
|
|
|
else: |
|
flash("Only PDF file are alllowed") |
|
return redirect(url_for('index')) |
|
|
|
@app.route('/summarize', methods=['POST']) |
|
def summarize_text(): |
|
data = request.json |
|
text = data.get('text', '') |
|
|
|
|
|
if text: |
|
inputs = tokenizer(text, max_length=1024, return_tensors="pt", truncation=True) |
|
summary_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=300, early_stopping=True) |
|
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) |
|
|
|
return jsonify({'summary': summary}) |
|
|
|
return jsonify({'summary': ''}), 400 |
|
|
|
if __name__ == "__main__": |
|
app.run(debug=True) |
