from pickle import load
from keras.models import load_model
from keras.utils import to_categorical
from keras.preprocessing.sequence import pad_sequences


# generate a sequence of characters with a language model
def generate_seq(model, mapping, seq_length, seed_text, n_chars):
    in_text = seed_text
    # generate a fixed number of characters
    for _ in range(n_chars):
        # encode the characters as integers
        encoded = [mapping[char] for char in in_text]
        # truncate sequences to a fixed length
        encoded = pad_sequences([encoded], maxlen=seq_length, truncating='pre')
        # one hot encode
        encoded = to_categorical(encoded, num_classes=len(mapping))
        # predict character
        yhat = model.predict_classes(encoded, verbose=0)
        # reverse map integer to character
        out_char = ''
        for char, index in mapping.items():
            if index == yhat:
                out_char = char
                break
        # append to input
        in_text += char
    return in_text


# load the model
model = load_model('model.h5')

# load the mapping
mapping = load(open('mapping.pkl', 'rb'))

print(generate_seq(model, mapping, 10, 'Mar', 7))