QuestionAnsweringWebApp

所属分类:模式识别(视觉/语音等)
开发工具:CSS
文件大小:0KB
下载次数:0
上传日期:2023-11-13 10:13:53
上 传 者sh-1993
说明:  一个可操作的web应用程序,能够响应阿拉伯语和英语的事实查询,使用Python编程语言构建...
(An operational web application capable of responding to factual queries in both Arabic and English, built using the Python programming language)

文件列表:
LICENSE (1072, 2023-11-27)
app.py (81, 2023-11-27)
application/ (0, 2023-11-27)
application/__init__.py (78, 2023-11-27)
application/__pycache__/ (0, 2023-11-27)
application/__pycache__/__init__.cpython-310.pyc (254, 2023-11-27)
application/__pycache__/__init__.cpython-39.pyc (252, 2023-11-27)
application/__pycache__/processing.cpython-310.pyc (3411, 2023-11-27)
application/__pycache__/processing.cpython-39.pyc (3425, 2023-11-27)
application/__pycache__/routes.cpython-310.pyc (1045, 2023-11-27)
application/__pycache__/routes.cpython-39.pyc (1051, 2023-11-27)
application/processing.py (4202, 2023-11-27)
application/routes.py (792, 2023-11-27)
application/static/ (0, 2023-11-27)
application/static/ensias.png (265459, 2023-11-27)
application/static/script.js (2419, 2023-11-27)
application/static/scriptar.js (2463, 2023-11-27)
application/static/style.css (3294, 2023-11-27)
application/static/stylear.css (3328, 2023-11-27)
application/templates/ (0, 2023-11-27)
application/templates/index.html (1190, 2023-11-27)
application/templates/indexar.html (1312, 2023-11-27)
requirements.txt (272, 2023-11-27)

#

Open Domain Question Answering Web Application

## An operational web application capable of responding to factual queries in both Arabic and English, built using the Python programming language. This project is a part of my Introduction to Natural Language Processing course at [ENSIAS](https://fr.wikipedia.org/wiki/%C3%89cole_nationale_sup%C3%A9rieure_d%27informatique_et_d%27analyse_des_syst%C3%A8mes), [Mohammed V University](https://en.wikipedia.org/wiki/Mohammed_V_University) instructed by Professor [Si Lhoussain Aouragh](https://www.aouragh.ma/CV_2015.html). When selecting a project for the class, I chose to focus on Question and Answering, a part of NLP. Back then, ChatGPT and Language Models were quite popular, and I was very interested in understanding how these technologies function. However, these topics were rather advanced, so my professor advised me to proceed gradually. I began with a basic Q&A project, aiming to grasp the fundamentals of NLP including comprehension, information retrieval, and answer formulation. Every part of this project is sample code which shows how to do the following: * Create an open-domain Arabic and English question answering system using Python. * Implement a Document Retriever using wikipedia api. * Implement a Document Reader using Transformers including both [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) and [AraElectra-Arabic-SQuADv2-QA](https://huggingface.co/ZeyadAhmed/AraElectra-Arabic-SQuADv2-QA) through api calls to the HuggingFace server. * Create a Messenger like web application using Flask, HTML, CSS and JavaScript. ## Getting Started (Ubuntu/Debian) * Install Git ```bash sudo apt update sudo apt install git * Navigate to the Directory ```bash cd path/to/desired/location * Clone this repository ```bash git clone https://github.com/Heyyassinesedjari/QuestionAnsweringWebApp.git * Install Conda ```bash wget https://repo.anaconda.com/miniconda/Miniconda3-4.12.0-Linux-x86_64.sh bash Miniconda3-4.12.0-Linux-x86_64.sh source ~/.bashrc * Creating a Conda Environment ```bash conda create --name myenv python=3.9.12 * Activate Conda Environment and Install all requirements ```bash conda activate myenv conda install --file path_to_requirements.txt * Hover over to the 9th line of this [file](https://github.com/Heyyassinesedjari/QuestionAnsweringWebApp/blob/main/application/processing.py#L9C3-L9C3) and update 'Your_Hugging_Face_API_key' in the authorization field of the headers variable with your actual Hugging Face API key. * Run the App ```bash python app.py ## High-level functional explanation ### Q&A System Architecture Diagram ### Document Retriever Architecture Diagram ### Document Reader Architecture Diagram ### Application Sequence Diagram ### Video Demo https://github.com/Heyyassinesedjari/QuestionAnsweringWebApp/assets/94799575/5bd7fd0f-f1a5-409f-91ef-5dafd6d35a80 ### Project Defense Presentation (Google Slides) https://docs.google.com/presentation/d/1vQKFpJJx6TmtOgJ8upJHEEu_9QU-tWXH/edit?usp=sharing&ouid=100061785569173216725&rtpof=true&sd=true

近期下载者

相关文件


收藏者