Create app.py

app.py

@@ -0,0 +1,41 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+model_name2 = "Mhassanen/nllb-200-600M-En-Ar-finetuned"
+tokenizer = AutoTokenizer.from_pretrained(model_name2, src_lang="eng_Latn", tgt_lang="arz_Arab")
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name2)
+
+def translate2(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True)
+    translated_tokens = model.generate(**inputs)
+    translated_text = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
+    return translated_text[0]
+
+
+# st.set_page_config(page_title="", page_icon="logo.png")
+
+with st.sidebar:
+    # st.image("logo.png", width=70)
+    # st.markdown('<div style="position: absolute; left: 5px;"></div>', unsafe_allow_html=True)
+    
+    st.markdown("## About")
+    st.markdown("---")
+
+    st.markdown('''
+  This App powered by [Mhassanen/nllb-200-600M-En-Ar-finetuned](https://huggingface.co/Mhassanen/nllb-200-600M-En-Ar-finetuned) Language model
+    ''')
+    # st.markdown("---")
+
+
+st.title("English to Arabic Translation")
+
+text_to_translate = st.text_area("Enter text in English:")
+
+if st.button("Translate"):
+    if text_to_translate:
+        with st.spinner("Translating..."):
+            translation = translate2(text_to_translate)
+            st.success("Translation completed!")
+            st.text_area("Translated text in Arabic:", translation, height=200)
+    else:
+        st.warning("Please enter some text to translate.")