QAT System Application

Overview

qat-system-app is a comprehensive QAT system Flask application that provides various features such as text processing, PDF handling, and natural language processing (NLP). The application is designed to streamline and enhance learning experiences. It integrates various libraries and frameworks to provide robust functionalities for text processing, data parsing, web interactions, and more, making it an essential tool for students, researchers and even faculty members.

Features

• Web Framework: Build web applications and interfaces using Flask.
• AI/ML: Machine Learning capabilities with PyTorch, text generation and language understanding using Hugging Face's transformers
• NLP: Natural language processing with NLTK and TextBlob
• Text Processing: Utilize nltk for advanced natural language processing.
• Data Parsing and Manipulation: Leverage lxml for XML and HTML parsing.
• Image Processing: Handle image files with Pillow.
• HTTP Requests: Simplify HTTP interactions with requests.
• Template Rendering: Use Jinja2 for versatile template rendering.
• DOCX file creation and manipulation with docx and python-docx
• Web server built with Flask

Installation

1. Clone the repository:

   git clone https://github.com/techgrandmaster/qat-sys-app.git
   cd qat-sys-app

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

Dependencies

• Flask~=3.0.3: A micro web framework written in Python. It is easy to set up and extendable via various plugins.

• PyPDF2~=3.0.1: A library for reading and manipulating PDF files. It supports tasks such as splitting and merging PDFs, extracting text, and more.

• docx~=0.2.4: A library for creating and updating .docx files. It simplifies the creation of Word documents programmatically.

• python-docx~=0.8.11: Another library for creating and updating Word (.docx) files. It includes more comprehensive features compared to docx.

• nltk~=3.9.1: The Natural Language Toolkit is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources.

• textblob~=0.18.0.post0: A library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.

• transformers~=4.45.2: Hugging Face's Transformers library provides thousands of pre-trained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, and text generation in over 100 languages.

• setuptools~=75.1.0: A package development and distribution library. It supplies utilities to build and distribute Python packages, especially those that have dependencies.

• torch~=2.4.1: PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. It enables efficient numerical computation and is widely used in the machine learning community for building and training neural networks.

Usage

1. Run the Flask application:

   export FLASK_APP=app.py
   flask run

   The server will start running at http://127.0.0.1:5000/.

2. Access the application via your web browser at the provided URL.

Contributing

Feel free to fork this repository and submit pull requests. For any issues, please open an issue on GitHub.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any questions or suggestions, please contact [email protected].