Predict the Next Word

A Django-based web application that predicts the next word in a 50-word sentence using a TensorFlow model. The interface follows a cyberpunk aesthetic with neon animations and a dynamic particle background.

Table of Contents

• Overview
• Features
• Technologies
• Setup and Installation
• Usage
• Project Structure
• Design Highlights
• Contributing
• License

Overview

"Predict the Next Word" is a web app built with Django and TensorFlow. Users input a 50-word sentence, and the app predicts the next word using a trained ML model. The frontend includes a futuristic cyberpunk design with glitch effects and interactive backgrounds.

Features

• Predicts the next word(s) for a 50-word sentence
• Displays real-time word count with warning if over 50
• Cyberpunk-themed UI with neon/glitch styling
• Responsive design for both mobile and desktop
• Visual "Processing..." feedback during predictions

Technologies

• Backend: Django 5.2.3, Python 3.8+
• ML: TensorFlow (for predictions)
• Frontend: HTML, CSS, JavaScript
• Libraries: Font Awesome, Roboto Mono font
• Database: SQLite (default)

Setup and Installation

1. Clone the repository: git clone cd myproject

2. Set up virtual environment: python -m venv venv On Windows: .\venv\Scripts\activate On Linux/macOS: source venv/bin/activate

3. Install dependencies: pip install django==5.2.3 tensorflow

4. Apply database migrations: python manage.py migrate

5. Ensure static files exist: style.css in myapp/static/css/ script.js in myapp/static/js/

6. Check settings.py includes: STATIC_URL = '/static/' STATICFILES_DIRS = [BASE_DIR / 'myapp/static']

7. Set up templates: Place index.html in myapp/templates/ Or update settings.py to: TEMPLATES = [ { 'DIRS': [BASE_DIR / 'templates'], ... } ]

8. Start development server: python manage.py runserver

9. Access app at: http://127.0.0.1:8000/

Usage

• Open http://127.0.0.1:8000/ in your browser
• Enter a 50-word sentence in the input field
• Word counter turns red if you exceed 50 words
• Click "Predict" to see the TensorFlow model's suggestion
• Result appears with glitch animation

Note: Sentence must be exactly 50 words for accurate output.

Project Structure

myproject/
├── manage.py
├── requirements.txt
├── README.md
├── .gitignore
├── myproject/
│   ├── __init__.py
│   ├── settings.py
│   ├── urls.py
│   ├── wsgi.py
├── myapp/
│   ├── __init__.py
│   ├── admin.py
│   ├── apps.py
│   ├── models.py
│   ├── views.py
│   ├── tests.py
│   ├── urls.py
│   ├── onehotencoder.pkl
│   ├── sustain.py
│   ├── migrations/
│   │   ├── __init__.py
│   │   ├── 0001_initial.py
│   ├── static/
│   │   ├── css/
│   │   │   ├── style.css
│   │   ├── js/
│   │   │   ├── script.js
│   │   ├── images/
│   └── templates/
│       ├── index.html

Design Highlights

• Cyberpunk Colors: Neon pink (#ff6fd7), neon blue (#5bc0f8)
• Particle Background: Simulates neural network activity
• Holographic Container: Glassmorphism with neon glow
• Animations: Glitch text, pulsing titles, hover swipes
• Typography: Uses Roboto Mono for technical look
• Mobile Friendly: Fully responsive on all screen sizes

Contributing

1. Fork the repo
2. Create a branch: git checkout -b feature-name
3. Make your changes
4. Commit: git commit -m "Add feature"
5. Push: git push origin feature-name
6. Open a pull request

Follow PEP 8 and include tests if applicable.

License

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

Optional: Disable TensorFlow oneDNN Warning

If you need to disable oneDNN optimizations for reproducibility: On Windows: set TF_ENABLE_ONEDNN_OPTS=0 On Linux/macOS: export TF_ENABLE_ONEDNN_OPTS=0 Run this before launching the app.