🎭 Next Word Prediction with LSTM & Streamlit

A Deep Learning project that predicts the next word in a sequence of text, trained on Shakespeare's "Hamlet". This project utilizes Long Short-Term Memory (LSTM) networks for sequence modeling and is deployed as an interactive web application using Streamlit.

📌 Project Overview

The goal of this project is to develop a language model capable of understanding context and generating text in the style of Shakespeare. By analyzing sequences of words from Hamlet, the model learns to predict the most probable next word, demonstrating the power of RNNs in Natural Language Processing (NLP).
LIVE DEMO - https://nextword-predictionlstm-p62xzk3lsy7rv3lhkfalak.streamlit.app/

Key Features

• Deep Learning Model: Stacked LSTM architecture (Embedding -> LSTM -> Dropout -> Dense).
• Dataset: Complete text of Shakespeare's Hamlet.
• Visualization: TensorBoard integration for tracking training loss and accuracy.
• Web App: Interactive UI built with Streamlit for real-time predictions.
• Early Stopping: Implemented to prevent overfitting during training.

📂 Project Structure

Here is the organization of the repository:

File Name	Description
app.py	The main Streamlit application file for deployment.
experiemnt s.ipynb	Jupyter Notebook containing data preprocessing, model training, and evaluation code.
hamlet.txt	The raw text data source used for training.
next_word_lstm.h5	The trained Keras model saved in HDF5 format.
tokenizer.pickle	The tokenizer object used to convert text to sequences (saved for consistent inference).
requiremen ts.txt	List of Python dependencies required to run the project.

🚀 Installation & Setup

1. Clone the repository:

   git clone [https://github.com/your-username/next-word-prediction-lstm.git](https://github.com/your-username/next-word-prediction-lstm.git)
   cd next-word-prediction-lstm

2. Create a virtual environment (optional but recommended):

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

3. Install dependencies:

   pip install -r "requirements.txt"

   (Note: Ensure the filename matches exactly as it appears in the repo)

🖥️ Usage

Running the Web App

To launch the interactive Next Word Predictor:

streamlit run app.py

This will open the application in your default web browser (usually at http://localhost:8501).

Training the Model

If you want to retrain the model or experiment with hyperparameters:

1. Open experiemnt s.ipynb in Jupyter Notebook or Google Colab.
2. Run the cells sequentially to preprocess data, train the LSTM, and save the new model files.
3. Monitor training progress using TensorBoard (if configured in the notebook).

🧠 Model Architecture

The model is built using TensorFlow/Keras with the following layers:

1. Embedding Layer: Converts word indices into dense vectors of fixed size.
2. LSTM Layer 1: 150 units, returns sequences.
3. Dropout Layer: 20% dropout to prevent overfitting.
4. LSTM Layer 2: 100 units.
5. Dense Output Layer: Softmax activation to predict the probability of the next word among all vocabulary words.

📊 Screenshots

Streamlit Interface