Update app.py

app.py

@@ -3,10 +3,16 @@ import streamlit as st
 x = st.slider('Select a value')
 st.write(x, 'squared is', x * x)
 
-# Load the T5-small model and tokenizer
-model_name = "t5-small"
-model = T5ForConditionalGeneration.from_pretrained(Lexim011/NISTER)
-tokenizer = T5Tokenizer.from_pretrained(Lexim011/NISTER)
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+from datasets import load_dataset
+
+# Load the T5-small model and tokenizer (or your custom model)
+model_name = "Lexim011/NISTER"  # Ensure this is the correct string identifier for your model
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+
+# Load your dataset
+dataset = load_dataset("Lexim011/Compliance")
 
 # Load your dataset (replace with your dataset path or identifier)
 dataset = load_dataset("Lexim011/Compliance")