Create app.py

app.py

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+import streamlit as st
+from transformers import MarianMTModel, MarianTokenizer
+
+# Title and Description
+st.set_page_config(page_title='Language Translation App', layout='centered')
+st.title('Language Translation App')
+st.write('Translate text between multiple languages using an open-source model.')
+
+# Model Selection
+@st.cache_resource
+def load_model(src_lang, tgt_lang):
+    model_name = f'Helsinki-NLP/opus-mt-{src_lang}-{tgt_lang}'
+    tokenizer = MarianTokenizer.from_pretrained(model_name)
+    model = MarianMTModel.from_pretrained(model_name)
+    return tokenizer, model
+
+# Sidebar for Language Selection
+st.sidebar.header('Language Selection')
+src_lang = st.sidebar.selectbox('Select Source Language', ['en', 'fr', 'es', 'de', 'it','pt','ru','zh','ja','ar'], index=0)
+tgt_lang = st.sidebar.selectbox('Select Target Language', ['en', 'fr', 'es', 'de', 'it','pt','ru','zh','ja','ar'], index=1)
+
+# Main Input Area
+st.write('### Enter text to translate:')
+text = st.text_area('Text Input', placeholder='Type your text here...')
+
+# Translation Button
+if st.button('Translate'):
+    if text.strip() == '':
+        st.warning('Please enter text to translate.')
+    else:
+        try:
+            tokenizer, model = load_model(src_lang, tgt_lang)
+            inputs = tokenizer.encode(text, return_tensors='pt')
+            translated = model.generate(inputs, max_length=1000, num_beams=5, early_stopping=True)
+            result = tokenizer.decode(translated[0], skip_special_tokens=True)
+            st.success('### Translated Text:')
+            st.write(result)
+        except Exception as e:
+            st.error(f'Error: {e}')
+
+# Footer
+st.sidebar.write('Powered by Helsinki-NLP/opus-mt')
+st.sidebar.write('Built with Streamlit')
+
+# To run the app, save this file and run `streamlit run filename.py` in your terminal.