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Overview:

This project focuses on automatic text generation using Natural Language Processing (NLP). The app is designed to generate texts based on a trained machine learning model, using free novel samples as the dataset. A Seq2Seq (Sequence to Sequence) model is employed to handle the task, with character vectorization for processing text.

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Key Features:

1. Seq2Seq Model for Text Generation:

The Sequence to Sequence (Seq2Seq) architecture is used to generate text by learning patterns from the input data, capturing the context of the text to produce coherent sequences.

2. Character Vectorization:

Text data is converted into vectors to allow the model to process the information at a granular level, focusing on character-by-character generation to ensure accuracy in text output.

3. Training Data:

The model is trained using a free novel sample, helping it learn various writing styles and sentence structures for automatic text generation.

4. Validation:

The generated text is validated by evaluating the coherence and structure of the output, ensuring the model produces logical and fluent results.

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