Update app.py

app.py

@@ -1,5 +1,6 @@
 import streamlit as st
 import pandas as pd
+from PIL import Image
 import re
 
 def summarize_function(notes):
@@ -10,7 +11,7 @@ def summarize_function(notes):
     st.write('Summary: ')
     return gen_text
 
-st.markdown("<h1 style='text-align: center; color: #489DDB;'>GPT Clinical Notes Summarizer 0.1v</h1>", unsafe_allow_html=True)
+st.markdown("<h1 style='text-align: center; color: #489DDB;'>GPT Clinical Notes Summarizer</h1>", unsafe_allow_html=True)
 st.markdown("<h6 style='text-align: center; color: #489DDB;'>by Bryan Mildort</h1>", unsafe_allow_html=True)
 
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
@@ -21,17 +22,15 @@ from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 # device_map = infer_auto_device_map(model, dtype="float16")
 # st.write(device_map)
 
-@st.cache(allow_output_mutation=True)
+# @st.cache(allow_output_mutation=True)
 def load_model():
     model = AutoModelForCausalLM.from_pretrained("bryanmildort/gpt_neo_notes", low_cpu_mem_usage=True)
     # model = model.to(device)
     tokenizer = AutoTokenizer.from_pretrained("bryanmildort/gpt_neo_notes")
-    return pipeline("text-generation", model=model, tokenizer=tokenizer)   
-    
-
+    return pipeline("text-generation", model=model, tokenizer=tokenizer)
+   
 pipe = load_model()
 
-
 notes_df = pd.read_csv('notes_small.csv')
 examples_tuple = ()
 for i in range(len(notes_df)):
@@ -47,3 +46,10 @@ if st.button('Summarize'):
     parsed_input = re.sub(r'\n+', '\n',parsed_input)
     final_input = f"""[Notes]:\n{parsed_input}\n[Summary]:\n"""
     st.write(summarize_function(final_input))
+
+st.sidebar.markdown("<h1 style='text-align: center; color: #489DDB;'>GPT Clinical Notes Summarizer 0.1v</h1>", unsafe_allow_html=True)
+st.sidebar.markdown("<h6 style='text-align: center; color: #489DDB;'>The model for this application was created with generous support of the Google TPU Research Cloud (TPU). This demo is for investigative research purposes only. The model is assumed to have several limiations and biases, so please oversee responses with human moderation. It is not intended for production ready enterprises and is displayed to illustrate the capabilities of Large Language Models for healthcare research.</h1>", unsafe_allow_html=True)
+tower = Image.open('howard_social.png')
+seal =  Image.open('Howard_University_seal.svg.png')
+st.sidebar.image(tower)
+st.sidebar.image(seal)