A streamlined web application that processes uploaded PDFs to deliver instant textual analysis, including AI-powered summarization and sentiment detection.
QuickRead AI is an intelligent PDF analysis tool built with Python and Streamlit. It extracts text from PDF documents and provides:
- Smart Summarization - AI-powered document summaries
- Sentiment Analysis - Emotional tone detection
- Key Points Extraction - Main ideas at a glance
- Text Statistics - Word count, readability metrics
- PDF Export - Download summaries as professional reports
Try it now: https://quickreaed.streamlit.app/
- Python - Core programming language
- Streamlit - Web framework for rapid development
- NLTK & TextBlob - Natural Language Processing
- PyPDF2 & pdfplumber - PDF text extraction
- FPDF - PDF report generation
- Clone and setup
git clone <repository-url>
cd QuickRead
python -m venv venv
source venv/bin/activate
Install dependencies
bash
pip install -r requirements.txt
Run the application
bash
streamlit run app.py
Open browser → http://localhost:8501
How to Use
Upload a PDF file using the sidebar
Select analysis options (summary, sentiment, key points)
Click "Analyze Document"
View instant results and download PDF summary
Project Structure
text
QuickRead/
├── app.py # Main application
├── requirements.txt # Dependencies
├── README.md # Documentation
└── utils/
├── pdf_processor.py # PDF text extraction
└── nlp_analyzer.py # AI analysis engine
Key Features
Smart PDF Processing
Dual extraction methods for reliability
Handles various PDF formats
Automatic error recovery
Intelligent Analysis
Document type detection (academic, resume, technical)
Context-aware summarization
Accurate sentiment scoring
Professional Output
Clean, readable summaries
Downloadable PDF reports
Structured insights
Use Cases
Academic - Research paper summaries, assignment analysis
Professional - Business reports, resume processing
Personal - Document organization, learning aid
Privacy
Local processing only
No data stored or sent externally
Secure file handling
Contributing
Contributions welcome! Feel free to submit issues and pull requests.
License
MIT License - see LICENSE file for details.