🤖 Next Word Prediction using LSTM

This project is a simple implementation of a next word prediction model using a Long Short-Term Memory (LSTM) network. The model is trained on a custom dataset of FAQs to generate text that mimics the style of the source text.

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✨ Features

• Text Preprocessing: Cleans and tokenizes the input text data.
• Sequence Generation: Creates n-gram sequences for model training.
• LSTM Model: A simple Keras-based LSTM model to learn text patterns.
• Text Generation: Predicts and generates a sequence of words based on a starting phrase.

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(Demo)

Here's a quick look at the model in action after training:

🛠️ Tech Stack

• Python 3.10
• TensorFlow 2.12
• NumPy 1.23
• Keras (as part of TensorFlow)

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⚙️ Setup and Installation

Follow these steps to set up the project locally.

1. Clone the repository:

   git clone [https://github.com/jsonusuman351/Nextwordpredict.git](https://github.com/jsonusuman351/Nextwordpredict.git)
   cd Nextwordpredict

2. Create and activate a virtual environment:

   # Create the venv using Python 3.10
   conda create -p venv python==3.10.0
   
   
   # Activate it
   conda activate venv/

3. Install the required packages:

   pip install -r requirements.txt

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🚀 Usage

Once the setup is complete, you can run the project.

1. Train the Model: This script will preprocess the data, build the LSTM model, train it, and save the trained model and tokenizer in the saved_models/ directory.

   python src/train.py

2. Generate Text: After training, run this script to see the predictions. You can change the starting text inside the predict.py file.

   python src/predict.py

   Example Output:

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   Seed Text: 'what is the duration'
   Generated Text: 'what is the duration of the course is 7 months so the total course'
   --------------------------------------------------
   

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📂 Project Structure

Click to view the folder structure

Nextwordpredict/
│
├── .gitignore
├── README.md
├── requirements.txt
│
├── data/
│   └── faqs.txt
│
├── saved_models/
│   └── .gitkeep
│
└── src/
    ├── __init__.py
    ├── data_preprocessing.py
    ├── model.py
    ├── train.py
    └── predict.py

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