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Smart Summarizer - Flask Web Application
Professional UI matching Figma design
"""
from flask import Flask, render_template, request, jsonify
import sys
from pathlib import Path
import os
from werkzeug.utils import secure_filename
import PyPDF2
from docx import Document as DocxDocument
# Add project root to path
sys.path.append(str(Path(__file__).parent.parent))
from models.textrank import TextRankSummarizer
from models.bart import BARTSummarizer
from models.pegasus import PEGASUSSummarizer
app = Flask(__name__)
app.config['SECRET_KEY'] = 'your-secret-key-here'
app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024 # 16MB max file size
app.config['UPLOAD_FOLDER'] = 'uploads'
# Create uploads folder if it doesn't exist
os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
# Allowed file extensions
ALLOWED_EXTENSIONS = {'txt', 'md', 'text', 'pdf', 'docx', 'doc'}
def allowed_file(filename):
return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
# Initialize models (lazy loading)
models = {}
def get_model(model_name):
"""Load and cache models"""
if model_name not in models:
if model_name == "textrank":
models[model_name] = TextRankSummarizer()
elif model_name == "bart":
models[model_name] = BARTSummarizer(device='cpu')
elif model_name == "pegasus":
models[model_name] = PEGASUSSummarizer(device='cpu')
return models[model_name]
@app.route('/')
def home():
"""Home page"""
return render_template('home.html')
@app.route('/single-summary')
def single_summary():
"""Single summary page"""
return render_template('single_summary.html')
@app.route('/comparison')
def comparison():
"""Model comparison page"""
return render_template('comparison.html')
@app.route('/batch')
def batch():
"""Batch processing page"""
return render_template('batch.html')
@app.route('/evaluation')
def evaluation():
"""Evaluation page"""
return render_template('evaluation.html')
@app.route('/api/summarize', methods=['POST'])
def summarize():
"""API endpoint for summarization"""
try:
data = request.json
text = data.get('text', '')
model_name = data.get('model', 'bart').lower()
if not text or len(text.split()) < 10:
return jsonify({
'success': False,
'error': 'Please provide at least 10 words of text'
}), 400
# Get model
model = get_model(model_name)
# Calculate target summary length (approximately 20-25% of original)
input_words = len(text.split())
target_length = max(30, min(150, int(input_words * 0.22))) # 22% compression
# Generate summary based on model type
if model_name == 'textrank':
# For TextRank, calculate number of sentences to achieve similar compression
sentences = text.count('.') + text.count('!') + text.count('?')
num_sentences = max(2, int(sentences * 0.3)) # ~30% of sentences
result = model.summarize_with_metrics(text, num_sentences=num_sentences)
else:
# For BART and PEGASUS, use word-based limits
result = model.summarize_with_metrics(
text,
max_length=target_length,
min_length=max(20, int(target_length * 0.5))
)
return jsonify({
'success': True,
'summary': result['summary'],
'metadata': result['metadata']
})
except Exception as e:
return jsonify({
'success': False,
'error': str(e)
}), 500
@app.route('/api/compare', methods=['POST'])
def compare():
"""API endpoint for comparing all three models"""
try:
data = request.json
text = data.get('text', '')
if not text or len(text.split()) < 10:
return jsonify({
'success': False,
'error': 'Please provide at least 10 words of text'
}), 400
results = {}
# Calculate consistent target length for all models
input_words = len(text.split())
target_length = max(30, min(150, int(input_words * 0.22)))
sentences = text.count('.') + text.count('!') + text.count('?')
num_sentences = max(2, int(sentences * 0.3))
# Run all three models
for model_name in ['textrank', 'bart', 'pegasus']:
try:
model = get_model(model_name)
if model_name == 'textrank':
result = model.summarize_with_metrics(text, num_sentences=num_sentences)
else:
result = model.summarize_with_metrics(
text,
max_length=target_length,
min_length=max(20, int(target_length * 0.5))
)
results[model_name] = {
'summary': result['summary'],
'metadata': result['metadata']
}
except Exception as e:
results[model_name] = {
'error': str(e)
}
return jsonify({
'success': True,
'results': results
})
except Exception as e:
return jsonify({
'success': False,
'error': str(e)
}), 500
@app.route('/api/upload', methods=['POST'])
def upload_file():
"""API endpoint for file upload"""
try:
if 'file' not in request.files:
return jsonify({
'success': False,
'error': 'No file provided'
}), 400
file = request.files['file']
if file.filename == '':
return jsonify({
'success': False,
'error': 'No file selected'
}), 400
if not allowed_file(file.filename):
return jsonify({
'success': False,
'error': 'Invalid file type. Please upload .txt, .md, .pdf, .docx, or .doc files'
}), 400
# Extract text based on file type
filename = secure_filename(file.filename)
file_ext = filename.rsplit('.', 1)[1].lower()
try:
if file_ext in ['txt', 'md', 'text']:
# Plain text files
text = file.read().decode('utf-8')
elif file_ext == 'pdf':
# PDF files
pdf_reader = PyPDF2.PdfReader(file)
text = ''
for page in pdf_reader.pages:
text += page.extract_text() + '\n'
elif file_ext in ['docx', 'doc']:
# Word documents
doc = DocxDocument(file)
text = '\n'.join([paragraph.text for paragraph in doc.paragraphs])
else:
return jsonify({
'success': False,
'error': 'Unsupported file format'
}), 400
except UnicodeDecodeError:
return jsonify({
'success': False,
'error': 'File encoding not supported. Please use UTF-8 encoded files'
}), 400
except Exception as e:
return jsonify({
'success': False,
'error': f'Error reading file: {str(e)}'
}), 400
if not text or len(text.split()) < 10:
return jsonify({
'success': False,
'error': 'File content is too short. Please provide at least 10 words'
}), 400
return jsonify({
'success': True,
'text': text,
'filename': filename,
'word_count': len(text.split())
})
except Exception as e:
return jsonify({
'success': False,
'error': str(e)
}), 500
if __name__ == '__main__':
import os
# Get port from environment variable (Hugging Face Spaces uses 7860)
port = int(os.environ.get('PORT', 7860))
# Check if running in production
debug = os.environ.get('FLASK_ENV') != 'production'
# Bind to 0.0.0.0 for cloud deployment
app.run(host='0.0.0.0', port=port, debug=debug)
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