app.py

import streamlit as st
from mtranslate import translate
import pandas as pd
import os
from gtts import gTTS
import base64
import pandas as pd
from transformers import pipeline,AutoTokenizer, AutoModelForSeq2SeqLM
import pickle
import torch

# Load a pretrained tokenizer for the source and target languages
tokenizer = AutoTokenizer.from_pretrained("KigenCHESS/marian-finetuned-kde4-en-to-fr")

# load the model 
model = AutoModelForSeq2SeqLM.from_pretrained("KigenCHESS/marian-finetuned-kde4-en-to-fr", from_tf=True)

# Set up the translation pipeline using the loaded model
translator = pipeline("translation", model=model, tokenizer=tokenizer)

# layout
st.title("Language-Translation")
st.markdown("In Python 🐍 with Streamlit")
st.markdown("by DR Andrew Kipkebut")
inputtext = st.text_area("INPUT",height=200)

#the correct translation 
speech_lang = {
    "sw": "Swahili",
}

selected_lang = None
for lang_code, lang_name in speech_lang.items():
    if st.button(lang_name):
        selected_lang = lang_code
        break

#to create two columns 
c1,c2 = st.columns([4,3])

#I/0
if len(inputtext) > 0 :
    try:
        output = translator(inputtext)
        translated_text = output[0]['translation_text']
        with c1:
            st.text_area("PREDICTED TRANSLATED TEXT", translated_text, height=200)

        #the translation below is the correct one 
        output = translate(inputtext,selected_lang)
        with c2:
            st.text_area("CORRECT TRANSLATED TEXT",output,height=200)
    except Exception as e:
        st.error(e