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import streamlit as st
import pickle
import pandas as pd
import numpy
import tensorflow as tf
import tensorflow_text as tf_text
from metaphone import doublemetaphone
import re
with open('vocab_data.pkl', 'rb') as fp:
hin_vocab = pickle.load(fp)
vocab_keys=[l for l in hin_vocab]
#all_data_vocab_53k_mixed_batch_v2
reloaded = tf.saved_model.load("translator")
def t_text(line):
line=re.sub("[.!?\\-\'\"]", "",line).lower().strip()
string=''
for j in line.split(' '):
if doublemetaphone(j)[0]+'*'+doublemetaphone(j[::-1])[0]+'*'+j[:2]+'*'+j[len(j)-1:] in vocab_keys:
string=string+list(hin_vocab[doublemetaphone(j)[0]+'*'+doublemetaphone(j[::-1])[0]+'*'+j[:2]+'*'+j[len(j)-1:]])[0]+' '
else:
string=string+j+' '
return string.lower().strip()
st.header("Hinglish-English Translator")
st.subheader("Please enter your text!")
st.text("")
input = st.text_area("Enter here")
if st.button('Check Now!'):
#transformed_sms = transform_text(input)
#vector_input = tfidf.transform([transformed_sms])
#result = model.predict(vector_input)[0]
#if result == 1:
# st.error("Spam")
#else:
# st.success("Not Spam")
st.write(reloaded.tf_translate(
tf.constant([
t_text(input)
]))['text'][0].numpy().decode())
#st.write(t_text(input))
#st.write("Thank you! I hope you liked it. ")
#st.write("Check out this Repo's [GitHub Link](https://github.com/RohanHBTU/spam_classifier)")
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