Update app.py

app.py

@@ -1,15 +1,13 @@
 import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-# Load model directly
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-tokenizer = AutoTokenizer.from_pretrained("goldfish-models/haw_latn_full")
-model = AutoModelForCausalLM.from_pretrained("goldfish-models/haw_latn_full")
-
+# Load the model and tokenizer for English to Hawaiian Pidgin translation
+tokenizer = AutoTokenizer.from_pretrained("claudiatang/flan-t5-base-eng-hwp")
+model = AutoModelForSeq2SeqLM.from_pretrained("claudiatang/flan-t5-base-eng-hwp")
 
 def translate_to_hawaiian(text):
     # Add language direction instruction (this may improve translation)
-    input_text = f"translate English to Hawaiian: {text}"
+    input_text = f"translate English to Hawaiian Pidgin: {text}"
 
     # Encoding the input text for the model
     inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)
@@ -22,8 +20,8 @@ def translate_to_hawaiian(text):
     return translated_text
 
 # Streamlit interface
-st.title("Hawaiian Language Translator")
-st.write("This app translates English text to Hawaiian using the mT5 model.")
+st.title("Hawaiian Pidgin Translator")
+st.write("This app translates English text to Hawaiian Pidgin using a language model.")
 
 # Input text from the user
 text_input = st.text_area("Enter text to translate:")
@@ -32,4 +30,4 @@ text_input = st.text_area("Enter text to translate:")
 if text_input:
     translation = translate_to_hawaiian(text_input)
     st.subheader("Translation:")
-    st.write(translation)
+    st.write(translation)