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| 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 pseudolab/K23_MiniMed by Tonic (Note: This model has been struggling to operate without config errors) | |
| # Config issues persist with this model, unfortunately. It may not be ready for use. | |
| tokenizer = AutoTokenizer.from_pretrained("AutoTokenizer.from_pretrained("pseudolab/K23_MiniMed) | |
| model = AutoModelForCausalLM.from_pretrained("pseudolab/K23_MiniMed) | |
| #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") |