import tensorflow as tf
import numpy as np
import pickle
import streamlit as st
import numpy as np

from tensorflow import keras
import string
import re
from tqdm import tqdm
import pandas as pd
import matplotlib.pyplot as plt
import pickle
import tensorflow as tf
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam

model = load_model('/nextwords11.h5')
tokenizer = pickle.load(open('/token11.pkl', 'rb'))

def main(text):
    seed_text = ""
    next_words = 3
    for _ in range(next_words):
        
        token_list = tokenizer.texts_to_sequences([seed_text])[0]
        token_list = pad_sequences([token_list], maxlen=86, padding='pre')
        predict_x=model.predict(token_list, verbose=0) 
        predicted=np.argmax(predict_x,axis=1)
        #predicted = model.predict_classes(token_list, verbose=0)
        output_word = ""
        for word, index in tokenizer.word_index.items():
            if index == predicted:
                output_word = word
                break
            
        seed_text += " " + output_word
  
    return seed_text

text = st.text_input("text", "Type Here")
if st.button("main"):
    result = prediction(sepal_length, sepal_width, petal_length, petal_width)
    st.success('The output is {}'.format(result))
    
if __name__=='__main__':
    main()