Language Translator (English to French Case Study) Bag of Characters Encoder-Decoder Model

This project is a character-level sequence-to-sequence model for translating English to French. The model is built using TensorFlow and Keras, leveraging LSTM layers for the encoder-decoder architecture. The project also includes a Tkinter-based graphical user interface (GUI) to interact with the model and translate text in real-time.

Overview

The project consists of an English-to-French language translator using the following key components:

• Data Preprocessing: Prepares the text data by tokenizing and vectorizing the input and target texts.
• Model Architecture: Implements a sequence-to-sequence LSTM model with attention mechanism to handle character-level translation.
• GUI: A simple Tkinter-based interface for inputting English sentences and receiving translated French sentences.

Features

• Character-level tokenization using CountVectorizer.
• Padding of input and target sequences to match the required length for training.
• Encoder-Decoder architecture using LSTM layers.
• Model training with categorical_crossentropy loss and adam optimizer.

Installation

To get started, you need to set up your environment with the necessary dependencies.

1. Clone this repository to your local machine:

   git clone https://github.com/Olamide643/Language-Translator.git

2. Navigate to the project directory:

   cd language-translator

To run this project, you will need:

• Python 3.x
• The following libraries:
  • numpy
  • tensorflow
  • scikit-learn

You can install the dependencies using:

pip install numpy tensorflow scikit-learn

Usage

1. Data Preparation: Make sure to provide a dataset in the format of a tab-separated file containing English-French sentence pairs. Modify the data_path variable in the script to point to your dataset (fra.txt by default).

2. Model Training: To train the model, run the following command:

   python train.py

   This script preprocesses the data, trains the model using LSTM layers, and saves the trained model and tokenizer to disk.

3. Running the GUI: Once the model is trained, you can start the Tkinter GUI by running:

   python gui.py

   Enter an English sentence into the provided text box, and the model will return the corresponding French translation.

Example

# Sample input data (input_texts and target_texts need to be prepared)
input_texts = ['hello', 'world']
target_texts = ['hola', 'mundo']

# Call the function to prepare data
en_in_data, dec_in_data, dec_tr_data = bagofcharacters(input_texts, target_texts)

# Train the model
model.fit([en_in_data, dec_in_data], dec_tr_data, batch_size=64, epochs=100, validation_split=0.2)

Contributing

If you'd like to contribute to this project, feel free to fork the repository and submit a pull request.

Sample