keras-language-translator-web-api

A simple language translator implemented in Keras with Flask serving web

The language translator is built based on seq2seq models, and can infer based on either character-level or word-level.

The seq2seq model is implemented using LSTM encoder-decoder on Keras.

Usage

Run the following command to install the keras, flask and other dependency modules:

sudo pip install -r requirements.txt

The translator models are trained using eng-to-french and eng-to-chinese data set and are available in the translator_train/models directory. During runtime, the flask app will load these trained models to perform the translation.

Currently only the eng-to-chinese and eng-to-french translations models are provided as examples, you can go to http://www.manythings.org/anki/ to download more datasets for the translator training and use the scripts in the translator_train to generate new seq2seq for other language translation

Training (Optional)

As the trained models are already included in the "translator_train/models" folder in the project, the training is not required. However, if you like to tune the parameters of the seq2seq and retrain the models, you can use the following command to run the training:

cd translator_train
python eng_to_cmn_char_seq2seq_train.py

The above commands will train seq2seq model using eng-to-chinese dataset on the character-level and store the trained model in "translator_train/models/eng-to-cmn/eng-to-cmn-char-**"

If you like to train other models, you can use the same command above on another train python scripts:

• eng_to_cmn_word_translator_train.py: train on eng-to-chinese on word-level (one hot encoding)
• eng_to_cmn_glove_translator_train.py: train on eng-to-chinese on word-level (GloVe encoding)
• eng_to_fra_char_translator_train.py: train on eng-to-french on character-level
• eng_to_fra_word_translator_train.py: train on eng-to-french on word-level (one hot encoding)
• eng_to_fra_glove_translator_train.py: train on eng-to-french on word-level (GloVe encoding)

Running Web Api Server

Goto translator_web directory and run the following command:

python flaskr.py

Now navigate your browser to http://localhost:5000 and you can try out various predictors built with the following trained seq2seq models:

• Character-level seq2seq models
• Word-level seq2seq models (one hot encoding)
• Word-level seq2seq models (GloVe encoding)

Invoke Web Api

To translate an english sentence to other languages using web api, after the flask server is started, run the following curl POST query in your terminal:

curl -H 'Content-Type application/json' -X POST -d '{"level":"level_type", "sentence":"your_sentence_here", "target_lang":"target_language"}' http://localhost:5000/translate_eng

The level_type can be "char" or "word", the target_lang can be "chinese" or "french"

(Note that same results can be obtained by running a curl GET query to http://localhost:5000/translate_eng?sentence=your_sentence_here&level=level_type&target_lang=target_language)

For example, you can translate the sentence "Be nice." by running the following command:

curl -H 'Content-Type: application/json' -X POST -d '{"level":"word", "sentence":"Be nice.", "target_lang":"chinese"}' http://localhost:5000/translate_eng

And the following will be the json response:

{
    "level": "word",
    "sentence": "Be nice.",
    "target_lang": "chinese",
    "translated": "和气点。"
}

Here are some examples for eng translation using some other configuration options:

curl -H 'Content-Type: application/json' -X POST -d '{"level":"char", "sentence":"Be nice.", "target_lang":"chinese"}' http://localhost:5000/translate_eng
curl -H 'Content-Type: application/json' -X POST -d '{"level":"word-glove", "sentence":"Be nice.", "target_lang":"chinese"}' http://localhost:5000/translate_eng
curl -H 'Content-Type: application/json' -X POST -d '{"level":"word", "sentence":"Be nice.", "target_lang":"french"}' http://localhost:5000/translate_eng
curl -H 'Content-Type: application/json' -X POST -d '{"level":"word-glove", "sentence":"Be nice.", "target_lang":"french"}' http://localhost:5000/translate_eng
curl -H 'Content-Type: application/json' -X POST -d '{"level":"char", "sentence":"Be nice.", "target_lang":"french"}' http://localhost:5000/translate_eng