Update app.py

app.py

@@ -1,21 +1,29 @@
 import streamlit as st
-from transformers import T5Tokenizer, T5ForConditionalGeneration
+from transformers import T5Tokenizer, T5ForConditionalGeneration, AutoTokenizer, AutoModelForSeq2SeqLM
 
 # Create the app layout
 st.title("Text Translation")
 input_text = st.text_input("Enter text to translate:")
 # Create a list of options for the select box
 options = ["English", "Romanian", "German", "French", "Spanish"]
-models = ["t5-base", "t5-small", "t5-large", "google/mt5-base", "helsinki-nlp/opus-mt-de-ro"]
+langs = {"English":"en", "Romanian":"ro", "German":"de", "French":"fr", "Spanish":"sp"}
+models = ["t5-base", "t5-small", "t5-large", "google/mt5-base", "Helsinki-NLP"]
+
 # Create the select box
 sselected_language = st.selectbox("Select a source language:", options)
 tselected_language = st.selectbox("Select a target language:", options)
+sl = langs['sselected_language']  
+tl = langs['tselected_language']
 model_name = st.selectbox("Select a model:", models)
 st.session_state["sselected_language"] = sselected_language
 st.session_state["tselected_language"] = tselected_language
 st.session_state["model_name"] = model_name
 tokenizer = T5Tokenizer.from_pretrained(model_name)
 model = T5ForConditionalGeneration.from_pretrained(model_name)
+if model_name == 'Helsinki-NLP':
+    model_name = f"Helsinki-NLP/opus-mt-{sl}-{tl}"
+    tokenizer = AutoTokenizer.from_pretrained(f"Helsinki-NLP/opus-mt-{sl}-{tl}")
+    model = AutoModelForSeq2SeqLM.from_pretrained(f"Helsinki-NLP/opus-mt-{sl}-{tl}")
 st.write("Selected language combination:", sselected_language, " - ", tselected_language, "Selected model:", model_name)
 submit_button = st.button("Translate")
 translated_textarea = st.text("")