Create app.py

app.py

@@ -0,0 +1,25 @@
+from transformers import MBartForConditionalGeneration, MBart50Tokenizer
+import streamlit as st
+
+@st.cache(allow_output_mutation=True, suppress_st_warning=True)
+def download_model():
+    model_name = "facebook/mbart-large-50-many-to-many-mmt"
+    model = MBartForConditionalGeneration.from_pretrained(model_name)
+    tokenizer = MBart50Tokenizer.from_pretrained(model_name)
+    return model, tokenizer
+
+st.title('Hindi to English Translator')
+text = st.text_area("Enter Text:", value='', height=None, max_chars=None, key=None)
+model, tokenizer = download_model()
+
+if st.button('Translate to English'):
+    if text == '':
+        st.write('Please enter Hindi text for translation') 
+    else: 
+        model_name = "facebook/mbart-large-50-many-to-many-mmt"
+        tokenizer.src_lang = "hi_IN"
+        encoded_hindi_text = tokenizer(text, return_tensors="pt")
+        generated_tokens = model.generate(**encoded_hindi_text, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
+        out = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+        st.write('', str(out).strip('][\''))
+else: pass