Upload app.py

app.py

@@ -0,0 +1,76 @@
+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)
+
+# Load the pre-trained BART tokenizer and model
+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)