Update app.py (#2)

- Update app.py (8a1d1fcc38c182381df6e3ae64c149247786ddb5)


Co-authored-by: Joseph Pollack <[email protected]>

app.py

@@ -1,13 +1,18 @@
 import streamlit as st
 import pandas as pd
-from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+from transformers import pipeline, AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM
+from peft import PeftModel, PeftConfig
 
 #Note this should be used always in compliance with applicable laws and regulations if used with real patient data.
 
-# Load the tokenizer and model: pseudolab/K23_MiniMed by Tonic (Note: This is a large model and will take a while to download)
-# Config issues persist with this model, unfortunately.  It may not be ready for use.
-tokenizer = AutoTokenizer.from_pretrained("pseudolab/K23_MiniMed")
-model = AutoModelForCausalLM.from_pretrained("pseudolab/K23_MiniMed")
+# Instantiate the Tokenizer
+tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True, padding_side="left")
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = 'left'
+# Load the PEFT model
+peft_config = PeftConfig.from_pretrained("pseudolab/K23_MiniMed")
+peft_model = MistralForCausalLM.from_pretrained("https://huggingface.co/HuggingFaceH4/zephyr-7b-beta", trust_remote_code=True)
+peft_model = PeftModel.from_pretrained(peft_model, "pseudolab/K23_MiniMed")
 
 #Upload Patient Data
 uploaded_file = st.file_uploader("Choose a CSV file", type="csv")