Upload app.py

app.py

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+from transformers import MBartForConditionalGeneration, MBart50Tokenizer, MarianTokenizer, MarianMTModel
+import streamlit as st
+
+@st.cache(allow_output_mutation=True, suppress_st_warning=True)
+
+def download_model():
+    model_name = f'Helsinki-NLP/opus-mt-en-ur'
+    model = MarianMTModel.from_pretrained(model_name)
+    tokenizer = MarianTokenizer.from_pretrained(model_name)
+    return model, tokenizer
+
+st.title('English to Urdu Translator')
+st.markdown("[Developd By: Aziz Ahmad](https://www.linkedin.com/in/theazizahmad/)", unsafe_allow_html=True)
+
+text = st.text_area("Enter Text:", value='', height=None, max_chars=None, key=None)
+model, tokenizer = download_model()
+
+if st.button('Translate to Urdu'):
+    if text == '':
+        st.write('Please enter English text for translation') 
+    else: 
+      
+         # Tokenize the text
+        batch = tokenizer(text, return_tensors="pt", padding=True)
+        # tokenized text maximum allowed size of 512
+        batch["input_ids"] = batch["input_ids"][:, :512]
+        batch["attention_mask"] = batch["attention_mask"][:, :512]
+        translation_encoded = model.generate(**batch)
+        translation = tokenizer.batch_decode(translation_encoded, skip_special_tokens=True)
+        st.write('', str(translation).strip('][\''))
+else: pass