Next Word Prediction with LSTM

Screenshot of the Streamlit Application Interface

Overview

This project implements an LSTM-based Next Word Prediction model trained on Shakespeare's Hamlet text. The model predicts the next most likely words based on user input, demonstrating the capabilities of sequence modeling in Natural Language Processing (NLP). The solution is deployed as an interactive web application using Streamlit.

Key Features

• LSTM Neural Network: Uses Long Short-Term Memory networks for sequence prediction
• Real-time Predictions: Provides instant word suggestions as users type
• User-friendly Interface: Clean and intuitive web interface powered by Streamlit
• Modular Codebase: Well-organized project structure for maintainability

Installation

1. Clone the repository:

git clone https://github.com/yourusername/sentence-predictor.git
cd sentence-predictor

2.Create and activate virtual environment:

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

3.Install dependencies:

pip install -r requirements.txt

4.Run the Streamlit application:

streamlit run src/app.py

##Project Structure

sentence-predictor/
├── data/                   # Text data for training
│   └── hamlet.txt          
├── models/                 # Pretrained models and tokenizer
│   ├── next_word_lstm.h5
│   └── tokenizer.pkl
├── notebooks/              # Jupyter notebook for experimentation
│   └── experiments.ipynb  
├── src/                    # Streamlit application source code
│   └── app.py              
├── assets/                 # Screenshots and visual assets
│   └── screenshot.jpeg
├── requirements.txt        # Dependency list
└── README.md               # Project documentation

Model Architecture

The LSTM model architecture consists of:

Embedding Layer (100 dimensions)

LSTM Layer (150 units)

Dropout Layer (0.2 rate)

Dense Output Layer (Softmax activation)

Contributing

Contributions are welcome! Please follow these steps:

Fork the project

Create your feature branch (git checkout -b feature/AmazingFeature)

Commit your changes (git commit -m 'Add some AmazingFeature')

Push to the branch (git push origin feature/AmazingFeature)

Open a Pull Request