🔤 Predicting_Next_Word

This project implements a Next Word Prediction model using LSTM (Long Short-Term Memory) networks trained on a sample text corpus. It allows users to input a sequence of words and predict the most likely next word using a trained deep learning model. The application is deployed using Streamlit.

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🧠 Project Highlights

• Trained on Shakespeare's Hamlet (sample dataset: hamlet.txt)
• Utilizes LSTM architecture for sequential text prediction
• Includes tokenizer for text preprocessing
• Deployed using Streamlit with real-time predictions
• Robust prediction pipeline using tokenizer.pickle and next_word_lstm.h5

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📁 Project Structure

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├── app.py                  # Streamlit app for predicting next word
├── experiments.ipynb       # Training, tuning, and evaluation notebook
├── hamlet.txt              # Training corpus (Shakespeare's Hamlet)
├── next\_word\_lstm.h5       # Trained LSTM model
├── tokenizer.pickle        # Tokenizer used for text preprocessing
├── README.md               # Project documentation

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🚀 Getting Started

1. Clone the Repository

git clone https://github.com/yourusername/Predicting_Next_Word.git
cd Predicting_Next_Word

2. Install Requirements

pip install -r requirements.txt

(If requirements.txt is not available, manually install: tensorflow, streamlit, numpy, pandas)

3. Run the Streamlit App

streamlit run app.py

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📦 How It Works

1. User enters a sequence of words.
2. The input is tokenized and padded.
3. The LSTM model (next_word_lstm.h5) predicts the next word's token.
4. Token is converted back to a word using the tokenizer.pickle.
5. The predicted word is shown on the interface.

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🧪 Model Training

Training and experiments are documented in experiments.ipynb. The training flow involves:

• Loading text corpus (hamlet.txt)
• Tokenizing sequences
• Building padded sequences
• Training an LSTM model with early stopping

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📚 Dataset

• File: hamlet.txt
• Source: Public domain literary text
• Preprocessed and tokenized for LSTM input

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⚙️ Environment

• Python 3.8+
• TensorFlow 2.x
• Streamlit
• NumPy, Pandas

Note: The app disables GPU/Metal acceleration by design for compatibility.

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🌐 Deployment

This project is fully compatible with:

• Streamlit Cloud
• Heroku
• Local deployment

Just make sure to include all required files and environment specs (requirements.txt, optionally runtime.txt).

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