Update app.py

app.py

@@ -1,29 +1,22 @@
-import streamlit as st
-from transformers import T5ForConditionalGeneration, T5Tokenizer, AutoModelForSeq2SeqLM, AutoTokenizer
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-# Load models and tokenizers
-@st.cache_resource
 def load_models():
     question_model_name = "mrm8488/t5-base-finetuned-question-generation-ap"
     recipe_model_name = "flax-community/t5-recipe-generation"
     instruct_model_name = "norallm/normistral-7b-warm-instruct"
     
-    # Load question generation model and tokenizer
-    question_model = T5ForConditionalGeneration.from_pretrained(question_model_name)
-    question_tokenizer = T5Tokenizer.from_pretrained(question_model_name)
+    question_model = AutoModelForSeq2SeqLM.from_pretrained(question_model_name)
+    question_tokenizer = AutoTokenizer.from_pretrained(question_model_name)
     
-    # Load recipe generation model and tokenizer
     recipe_model = AutoModelForSeq2SeqLM.from_pretrained(recipe_model_name)
     recipe_tokenizer = AutoTokenizer.from_pretrained(recipe_model_name)
     
-    # Load instruction-based model and tokenizer
-    instruct_model = AutoModelForSeq2SeqLM.from_pretrained(instruct_model_name)
+    # Load instruct model as causal language model
+    instruct_model = AutoModelForCausalLM.from_pretrained(instruct_model_name)
     instruct_tokenizer = AutoTokenizer.from_pretrained(instruct_model_name)
     
     return (question_model, question_tokenizer), (recipe_model, recipe_tokenizer), (instruct_model, instruct_tokenizer)
 
-(question_model, question_tokenizer), (recipe_model, recipe_tokenizer), (instruct_model, instruct_tokenizer) = load_models()
-
 # Function to generate a question from a given passage
 def generate_question(text, model, tokenizer):
     input_text = f"generate question: {text}"