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app.py
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app.py
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from flask import Flask, render_template, request
import numpy as np
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import tokenizer_from_json
import json
import gzip
import shutil
app = Flask(__name__)
# Load tokenizer
with open('tokenizer_eng.json', 'r') as f:
tokenizer_eng_json = json.load(f)
tokenizer_eng = tokenizer_from_json(tokenizer_eng_json)
with open('tokenizer_ger.json', 'r') as f:
tokenizer_ger_json = json.load(f)
tokenizer_ger = tokenizer_from_json(tokenizer_ger_json)
# Load model
loaded_model = load_model("language_translation_model.keras")
max_len = 53 # Define your maximum sequence length here
@app.route('/', methods=['GET', 'POST'])
def translate():
if request.method == 'POST':
user_input = request.form['english_sentence']
# Tokenize and pad the input sequence
input_seq = tokenizer_eng.texts_to_sequences([user_input])
input_seq = pad_sequences(input_seq, maxlen=max_len, padding='post')
# Predict the output sequence
predicted_seq = loaded_model.predict(input_seq)
predicted_text = []
for word_index in np.argmax(predicted_seq, axis=-1)[0]:
if word_index != 0: # Ignore padding index
word = tokenizer_ger.index_word.get(word_index, '<OOV>')
predicted_text.append(word)
# Join the predicted words into a sentence
german_translation = ' '.join(predicted_text)
return render_template('index.html', user_input=user_input, german_translation=german_translation)
return render_template('index.html')
if __name__ == '__main__':
app.run(debug=True)