SAFlaskFinal.py

#The details of this code can be found in my video here - #https://www.youtube.com/watch?v=hIq4bVT2ghk

import tensorflow_datasets as tfds
import tensorflow as tf
from flask import Flask, jsonify, make_response, request
from healthcheck import HealthCheck

import logging

app = Flask(__name__)
padding_size = 1000
model = tf.keras.models.load_model('sentiment_analysis.hdf5')
text_encoder = tfds.features.text.TokenTextEncoder.load_from_file("sa_encoder.vocab")

logging.basicConfig(filename="flask.log", level=logging.DEBUG,
                    format='%(asctime)s %(levelname)s %(name)s %(threadName)s : %(message)s')
logging.info('Model and Vocabalory loaded.......')

health = HealthCheck(app, "/hcheck")


def howami():
    return True, "I am Good"


health.add_check(howami)


def pad_to_size(vec, size):
    zeros = [0] * (size - len(vec))
    vec.extend(zeros)
    return vec


def predict_fn(predict_text, pad_size):
    encoded_text = text_encoder.encode(predict_text)
    encoded_text = pad_to_size(encoded_text, pad_size)
    encoded_text = tf.cast(encoded_text, tf.int64)
    predictions = model.predict(tf.expand_dims(encoded_text, 0))

    return (predictions.tolist())


@app.route('/seclassifier', methods=['POST'])
def predict_sentiment():
    text = request.get_json()['text']
    print(text)
    predictions = predict_fn(text, padding_size)
    sentiment = 'positive' if float(''.join(map(str, predictions[0]))) > 0 else 'Negative'
    app.logger.info("Prediction :" + str(predictions[0]) + "sentiment :" + sentiment)
    return jsonify({'predictions ': predictions, 'sentiment ': sentiment})


if __name__ == "__main__":
    app.run(host='0.0.0.0', port='5000')