Delete app.py

app.py

@@ -1,31 +0,0 @@
-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