Update app.py

app.py

@@ -3,32 +3,30 @@ 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 main():
+    tokenizer = AutoTokenizer.from_pretrained("Unbabel/TowerBase-13B-v0.1")
+    model = AutoModelForCausalLM.from_pretrained("Unbabel/TowerBase-13B-v0.1", device="cuda" if st.session_state.use_gpu else "cpu", load_in_4bit=True)
 
-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
+    languages = ["English", "Spanish", "Vietnamese", "French", "Portuguese"]
 
+    st.sidebar.title("Translation App")
+    st.sidebar.write("Choose source and target languages:")
 
-def main():
-    st.title("Language Translator")
+    source_lang_index = st.sidebar.selectbox("Source Language", languages)
+    target_lang_index = st.sidebar.selectbox("Target Language", languages)
 
-    source_lang = st.selectbox("Choose source language:", languages)
-    target_lang = st.selectbox("Choose target language:", languages)
+    source_lang = languages.index(source_lang_index)
+    target_lang = languages.index(target_lang_index)
 
-    text = st.text_area(f"Enter text in {source_lang}:", "")
+    text = st.text_area(f"Enter text in {source_lang_index}", "")
 
     if st.button("Translate"):
-        translated_text = translate_text(source_lang, target_lang, text)
-        st.text_area(f"Translation in {target_lang}:", translated_text)
+        input_text = f"{source_lang_index}: {text}\n{target_lang_index}:"
+        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)
+        st.write(f"Translation in {target_lang_index}: {translated_text}")
 
 if __name__ == "__main__":
     main()