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