Create app.py

app.py

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+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