Update app.py

app.py

@@ -1,12 +1,17 @@
 import streamlit as st
 from transformers import pipeline
-from transformers import AutoTokenizer, T5ForConditionalGeneration
 
-model_name = "allenai/unifiedqa-t5-small" # you can specify the model size here
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = T5ForConditionalGeneration.from_pretrained(model_name)
+translator = pipeline("translation", model="Helsinki-NLP/opus-mt-mul-en")
+def main():
+    st.title("Yelp review")
 
-def run_model(input_string, **generator_args):
-    input_ids = tokenizer.encode(input_string, return_tensors="pt")
-    res = model.generate(input_ids, **generator_args)
-    return tokenizer.batch_decode(res, skip_special_tokens=True)
+    with st.form("translator"):
+        text = st.text_area('enter some text:')
+        # clicked==True only when the button is clicked
+        clicked = st.form_submit_button("Submit")
+        if clicked:
+          results = translator([text])
+          st.json(results)
+
+if __name__ == "__main__":
+    main()