MiniMed_EHR_Analyst_Spaces.py

import streamlit as st
import pandas as pd
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM

#Note this should be used always in compliance with applicable laws and regulations if used with real patient data.

# Load the tokenizer and model:
# Now is squarelike/llama2-ko-medical-7b
# formerly 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("squarelike/llama2-ko-medical-7b")
model = AutoModelForCausalLM.from_pretrained("squarelike/llama2-ko-medical-7b")

#Upload Patient Data
uploaded_file = st.file_uploader("Choose a CSV file", type="csv")

# Prepare the context
def prepare_context(data):
    # Format the data as a string
    data_str = data.to_string(index=False, header=False)

    # Tokenize the data
    input_ids = tokenizer.encode(data_str, return_tensors="pt")

    # Truncate the input if it's too long for the model
    max_length = tokenizer.model_max_length
    if input_ids.shape[1] > max_length:
        input_ids = input_ids[:, :max_length]

    return input_ids

if uploaded_file is not None:
    data = pd.read_csv(uploaded_file)
    st.write(data)

    # Generate text based on the context
    context = prepare_context(data)
    generated_text = pipeline('text-generation', model=model)(context)[0]['generated_text']
    st.write(generated_text)

    # Internally prompt the model to data analyze the EHR patient data
    prompt = "You are an Electronic Health Records analyst with nursing school training. Please analyze patient data that you are provided here. Give an organized, step-by-step, formatted health records analysis. You will always be truthful and if you do nont know the answer say you do not know."

    if prompt:
        # Tokenize the prompt
        input_ids = tokenizer.encode(prompt, return_tensors="pt")

        # Generate text based on the prompt
        generated_text = pipeline('text-generation', model=model)(input_ids=input_ids)[0]['generated_text']
        st.write(generated_text)
    else:
        st.write("Please enter patient data")
        
else:
    st.write("No file uploaded")