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| from flask import Flask, render_template, request, jsonify | |
| import numpy as np | |
| import pickle | |
| from tensorflow.keras.models import load_model | |
| from tensorflow.keras.utils import pad_sequences | |
| app = Flask(__name__) | |
| encoder_model = load_model("encoder_model.keras") | |
| decoder_model = load_model("decoder_model.keras") | |
| with open("encoder_tokenizer.pkl","rb") as f: | |
| encoder_tokenizer = pickle.load(f) | |
| with open("decoder_tokenizer.pkl","rb") as f: | |
| decoder_tokenizer = pickle.load(f) | |
| with open("config.pkl","rb") as f: | |
| config = pickle.load(f) | |
| input_maxln = config["input_mxlen"] | |
| output_maxln = config["output_mxlen"] | |
| def translate(sentence): | |
| sequence = encoder_tokenizer.texts_to_sequences([sentence]) | |
| sequence = pad_sequences(sequence, | |
| maxlen=input_maxln, | |
| padding="post") | |
| states = encoder_model.predict(sequence, verbose=0) | |
| start = decoder_tokenizer.word_index["<start>"] | |
| end = decoder_tokenizer.word_index["<end>"] | |
| target_seq = np.array([[start]]) | |
| translated = [] | |
| while True: | |
| output, h, c = decoder_model.predict( | |
| [target_seq] + states, | |
| verbose=0 | |
| ) | |
| token = np.argmax(output[0, -1, :]) | |
| if token == end: | |
| break | |
| word = decoder_tokenizer.index_word.get(token, "") | |
| if word: | |
| translated.append(word) | |
| target_seq = np.array([[token]]) | |
| states = [h, c] | |
| if len(translated) > output_maxln: | |
| break | |
| return " ".join(translated) | |
| def home(): | |
| return render_template("index.html") | |
| def translate_api(): | |
| text = request.json["text"] | |
| prediction = translate(text) | |
| return jsonify({ | |
| "translation": prediction | |
| }) | |
| if __name__ == "__main__": | |
| app.run(debug=True) |