app.py

import gradio as gr
import tensorflow as tf
import numpy as np
import joblib
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences

model = tf.keras.models.load_model("best_model.h5")
english_tokenizer = joblib.load('english_tokenizer')
hindi_tokenizer = joblib.load('hindi_tokenizer')

def generate_text(txts):
    txts = txts.split()
    tmp_tok = english_tokenizer.texts_to_sequences(txts)
    tmp_pad = pad_sequences(tmp_tok, padding='post', maxlen=100, truncating='post')
    tmp_preds = model.predict(tmp_pad,verbose=0)
    res = []
    for pred in tmp_preds:
        text2 = hindi_tokenizer.sequences_to_texts([[np.argmax(p)] for p in pred])
        res.append("".join(text2))
    return " ".join(res)

demo = gr.Interface(fn=generate_text, 
                    inputs="text", 
                    outputs="text",
                    examples=[['Agra'],['Namaste'],['Shadab Sayeed'],['Mumbai']])
    
demo.launch()