🧠 Next Word Predictor with Streamlit

This is a simple web app that uses a trained LSTM model to predict the next few words in a sentence using deep learning. It’s built with TensorFlow, Keras, and Streamlit, and supports live prediction with a modern UI.

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🚀 Features

• 🔮 Predict up to 5 next words using a trained model
• 🎯 Top 3 predicted completions (beam search style)
• 🖌️ Modern and responsive UI
• 🔁 Live prediction as you type
• ⚡ Fast loading with model caching

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🖥️ Live Demo

You can try the app here: streamlit.app

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📂 Project Structure

├── app.py                   # Streamlit app
├── NextWordPredictor.ipynb  # Training Model
├── nwp_model.keras          # Trained Keras model
├── tokenizer.pkl            # Tokenizer used for training
├── requirements.txt         # Python dependencies
└── README.md                # (This file)

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🛠️ How to Run Locally

1. Clone the repo:

git clone https://github.com/ArmanSinghOP/NWP.git
cd NWP

2. Install dependencies:

pip install -r requirements.txt

3. Run the app:

streamlit run app.py

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☁️ Deployment (Streamlit Cloud)

1. Push this repo to GitHub (public repo required for free tier)
2. Go to Streamlit Cloud
3. Click "New App" > Connect to your GitHub repo
4. Set app.py as the main file and deploy!

Make sure your model (.keras) and tokenizer.pkl are both under 100 MB each for free hosting.

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📦 requirements.txt

streamlit
tensorflow==2.15.0
numpy

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🧠 Model Info

The model is a trained LSTM built on a general English sentence corpus for word-level prediction. Make sure the tokenizer used here matches the one used during training.

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