Using trained model for prediction of tags - Python and keras

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Python2.7

Keras

I have dataset with two columns, post and tags ("some text","tag"), and I have successfully trained model with 98% accuracy.The problem is how can I now input some other text and let model to predict what tag will it be? I've searched tutorials but I didn't find in any of them (in few there is testing but it is not applicable in this example) how the model is tested with data outside of dataset like text input so model could predict. This is what I have so far....

import keras 
import numpy as np
from keras.preprocessing.text import Tokenizer
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Input, Dense, Dropout, Embedding, LSTM, Flatten
from keras.models import Model
from keras.utils import to_categorical
from keras.callbacks import ModelCheckpoint
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
data = pd.read_csv('dataset3.csv')
print(data.head(10))
print(data.tags.value_counts())
data['target'] = data.tags.astype('category').cat.codes
data['num_words'] = data.post.apply(lambda x : len(x.split()))
bins=[0,50,75, np.inf]
data['bins']=pd.cut(data.num_words, bins=[0,100,300,500,800, np.inf], labels=['0-100', '100-300', '300-500','500-800' ,'>800'])
word_distribution = data.groupby('bins').size().reset_index().rename(columns={0:'counts'})
word_distribution.head()
num_class = len(np.unique(data.tags.values))
y = data['target'].values
MAX_LENGTH = 500
tokenizer = Tokenizer()
tokenizer.fit_on_texts(data.post.values)
post_seq = tokenizer.texts_to_sequences(data.post.values)
post_seq_padded = pad_sequences(post_seq, maxlen=MAX_LENGTH)
X_train, X_test, y_train, y_test = train_test_split(post_seq_padded, y, test_size=0.05)
vocab_size = len(tokenizer.word_index) + 1
inputs = Input(shape=(MAX_LENGTH, ))
embedding_layer = Embedding(vocab_size,
                            128,
                            input_length=MAX_LENGTH)(inputs)
x = Flatten()(embedding_layer)
x = Dense(32, activation='relu')(x)

predictions = Dense(num_class, activation='softmax')(x)
model = Model(inputs=[inputs], outputs=predictions)
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['acc'])

model.summary()
filepath="weights-simple.hdf5"
checkpointer = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
history = model.fit([X_train], batch_size=64, y=to_categorical(y_train), verbose=1, validation_split=0.25, 
          shuffle=True, epochs=5, callbacks=[checkpointer])
df = pd.DataFrame({'epochs':history.epoch, 'accuracy': history.history['acc'], 'validation_accuracy': history.history['val_acc']})
g = sns.pointplot(x="epochs", y="accuracy", data=df, fit_reg=False)
g = sns.pointplot(x="epochs", y="validation_accuracy", data=df, fit_reg=False, color='green')
predicted = model.predict(X_test)
predicted = np.argmax(predicted, axis=1)
accuracy_score(y_test, predicted)   

What I have tried:

I need to input text so the model will predict what tag it is. I've tried searching the internet for similar models but they all are explained till training and not testing with outside data. I'm beginner with Keras and machine learning but this fascinates me and I want to learn but I'm stuck. I know these are basics but i really don't know what to do next?

I would appreciate help.
Thank you