Create translator_app.py

translator_app.py

@@ -0,0 +1,35 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import warnings
+
+warnings.simplefilter("ignore")
+
+tokenizer = AutoTokenizer.from_pretrained("Unbabel/TowerBase-13B-v0.1")
+model = AutoModelForCausalLM.from_pretrained("Unbabel/TowerBase-13B-v0.1", device_map="auto", load_in_4bit=True)
+
+languages = ["English", "Spanish", "Vietnamese", "French", "Portuguese"]
+
+
+def translate_text(source_lang, target_lang, text):
+    input_text = f"{source_lang}: {text}\n{target_lang}:"
+    inputs = tokenizer(input_text, return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=20)
+    translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return translated_text
+
+
+def main():
+    st.title("Language Translator")
+
+    source_lang = st.selectbox("Choose source language:", languages)
+    target_lang = st.selectbox("Choose target language:", languages)
+
+    text = st.text_area(f"Enter text in {source_lang}:", "")
+
+    if st.button("Translate"):
+        translated_text = translate_text(source_lang, target_lang, text)
+        st.text_area(f"Translation in {target_lang}:", translated_text)
+
+
+if __name__ == "__main__":
+    main()