app.py

import gradio as gr
import numpy as np
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
from tensorflow.keras.models import load_model

model = load_model('./nextwords11.h5')

tokenizer = pickle.load(open('./token11.pkl', 'rb'))

def predict(text,nw):
    seed_text = text
    next_words = int(nw)
    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

with gr.Blocks(css=".x {font-weight:bold}") as demo:
    text = gr.Textbox(label="Write Something :",elem_classes="x")
    nw = gr.inputs.Dropdown(choices=[1,2,3,4,5], label="Select no of words to be predicted")
    output = gr.Textbox(label="Output Box",elem_classes="x")
    predict_btn = gr.Button("Predict next Words !!",elem_classes="x")
    predict_btn.click(fn=predict, inputs=[text,nw], outputs=output)

demo.launch()