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from flask import Flask, request, render_template, jsonify
from predict import predict_language
import joblib
import tensorflow as tf
import h5py
model = tf.keras.models.load_model('models\\full_language_identifcation_modelf.h5')
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
CountVectorizer = joblib.load('models\\cv.joblib')
LabelEncoder = joblib.load('models\\le.joblib')
app = Flask(__name__)
@app.route('/', methods=['GET', 'POST'])
def predict():
if request.method == 'POST':
text = request.form['text']
prediction = predict_language(text, model, CountVectorizer, LabelEncoder) # Call your prediction function
return render_template('result.html', prediction=prediction, text=text)
return render_template('index.html')
if __name__ == '__main__':
app.run(debug=True) |