test.py

import numpy as np
import tensorflow as tf
import pickle
import re

# Your input sentence
sentence = ['When stole book in class and the teacher caught me the rest of the class laughed at my attempt ']

print("[INFO]: Loading Classes")
# Load class names
classNames = np.load("./data/class_names.npy")

# Load tokenizer pickle file


print("[INFO]: Loading Tokens")
with open('./data/tokenizer.pickle', 'rb') as handle:
        Tokenizer = pickle.load(handle)


# Load model


print("[INFO]: Loading Model")
model = tf.keras.models.load_model("./data/model_final.model")




print("[INFO]: Preprocessing")
# Preprocess Text
MAX_LENGTH = maxlen = 100


def preprocess_text(sen):
    # Removing html tags
    sentence = remove_tags(sen)

    # Remove punctuations and numbers
    sentence = re.sub('[^a-zA-Z]', ' ', sentence)

    # Single character removal
    sentence = re.sub(r"\s+[a-zA-Z]\s+", ' ', sentence)

    # Removing multiple spaces
    sentence = re.sub(r'\s+', ' ', sentence)

    return sentence
TAG_RE = re.compile(r'<[^>]+>')

def remove_tags(text):
    return TAG_RE.sub('', text)






# Tokenize and pad sentence
sentence_processed = Tokenizer.texts_to_sequences(sentence)
sentence_processed = np.array(sentence_processed)
sentence_padded = tf.keras.preprocessing.sequence.pad_sequences(sentence_processed, padding='post', maxlen=MAX_LENGTH)



print("""[INFO]: Prediction\n\t{}""".format(sentence[0]))
# Get prediction for sentence
result = model.predict(sentence_padded)
print("-"*20)
# Show prediction
print("[INFO]: Emotion class for given text is: {}".format(classNames[np.argmax(result)]))