https://github.com/mohammadmoataz2/deeptextclassifier

DeepTextClassifier is a powerful Application for categorizing textual documents into predefined categories using advanced deep learning techniques. It facilitates document organization, decision-making, and workflow automation.
https://github.com/mohammadmoataz2/deeptextclassifier

application deeplearning fastapi nlp nltk python tensorflow

Last synced: about 2 months ago
JSON representation

DeepTextClassifier is a powerful Application for categorizing textual documents into predefined categories using advanced deep learning techniques. It facilitates document organization, decision-making, and workflow automation.

• Host: GitHub
• URL: https://github.com/mohammadmoataz2/deeptextclassifier
• Owner: MohammadMoataz2
• Created: 2024-05-20T14:19:45.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-05-20T14:38:59.000Z (about 2 years ago)
• Last Synced: 2025-06-29T12:39:02.724Z (12 months ago)
• Topics: application, deeplearning, fastapi, nlp, nltk, python, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 26.8 MB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# DeepTextClassifier: Deep Learning Document Categorization Application

## Overview

**DeepTextClassifier** is a powerful Application for categorizing textual documents into predefined categories using advanced deep learning techniques. It facilitates document organization, decision-making, and workflow automation.

![Blue and Yellow Modern Data Analysis Presentation](https://github.com/MohammadMoataz2/X-rayGuardian/assets/123085286/72c445f7-eb90-40db-8a89-52613d4ac0c9)

### Benefits

- Efficient document categorization

- Enhanced organization and accessibility of textual data

- Improved decision-making processes

- Automation of document classification tasks

![image](https://github.com/MohammadMoataz2/X-rayGuardian/assets/123085286/6d199cd6-565b-4b59-ab6a-de4f8d0ab080)

## Data Processing Pipeline

### Technology Used

- NLP (Natural Language Processing)

- NLTK (Natural Language Toolkit)

- TensorFlow

- Pandas

- Matplotlib and Seaborn

![image](https://github.com/MohammadMoataz2/X-rayGuardian/assets/123085286/2f9bcb2e-0b4d-4d52-b3e5-da39a6c3a813)

## Model Building

### Technology Used

- Deep Learning

- TensorFlow Keras

- Embedding Layers

- LSTM (Long Short-Term Memory)

- Adam Optimizer

## Web Application

### Technology Used

- FastAPI

- HTML/CSS

- JavaScript

- Python

- File Handling Libraries

- AJAX (Asynchronous JavaScript and XML)

## How to Run

To run the application, follow these steps:

1. Install the required dependencies listed in the requirements.txt file.

2. Run the FastAPI server using the command: `python -m uvicorn main:app --reload`.

3. Access the application by opening the index.html file in a web browser.

## Contributions and Requirements

Contributions are welcome. Please refer to the project's GitHub repository for guidelines and requirements.