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import streamlit as st
import numpy as np
import pandas as pd
import os
os.environ["CUDA_VISIBLE_DEVICES"]="-1" ### load on cpu if GPU is making issue
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from tensorflow.keras.models import load_model
import time
# from PIL import Image
st.set_page_config(page_title="TCR-ESM",page_icon="dna")
hide_streamlit_style = """
<style>
#root > div:nth-child(1) > div > div > div > div > section > div {padding-top: 2rem;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
# image = Image.open('TCR-ESM.png')
# st.image(image)
st.title('TCR-ESM')
st.subheader('a webserver accompanying our work on predicting TCR-peptide-MHC binding with large protein model (ESM1v) embeddings')
dataset = st.radio("Please select the Training Databse",('MCPAS', 'VDJDB'), horizontal=True)
task = st.radio("Please select the Prediction Task",("TCR\u03B1-TCR\u03B2-Peptide-MHC", "TCR\u03B1-TCR\u03B2-Peptide", "TCR\u03B1-Peptide-MHC",
"TCR\u03B2-Peptide-MHC", "TCR\u03B1-Peptide", "TCR\u03B2-Peptide"), horizontal=True)
with open("sample_input_data.zip", "rb") as file:
btn = st.download_button(label="Download Sample Input Data",data=file,file_name="sample_input_data.zip", mime="application/octet-stream")
# st.download_button('Download Sample Input Data', open('tcresm_sample_input.zip'))
############## get numpy files
if task == "TCR\u03B1-TCR\u03B2-Peptide-MHC":
alpha = st.file_uploader("Choose the .npy file containing TCR\u03B1 Embeddings", key=101)
beta = st.file_uploader("Choose the .npy file containing TCR\u03B2 Embeddings", key=103)
pepti = st.file_uploader("Choose the .npy file containing Peptide Embeddings", key=109)
mhc = st.file_uploader("Choose the .npy file containing MHC Embeddings", key=113)
shorttask = 'abpm'
group = (alpha,beta,pepti,mhc)
elif task == "TCR\u03B1-TCR\u03B2-Peptide":
alpha = st.file_uploader("Choose the .npy file containing TCR\u03B1 Embeddings", key=127)
beta = st.file_uploader("Choose the .npy file containing TCR\u03B2 Embeddings", key=131)
pepti = st.file_uploader("Choose the .npy file containing Peptide Embeddings", key=137)
shorttask = 'abp'
group = (alpha,beta,pepti)
elif task == "TCR\u03B1-Peptide-MHC":
alpha = st.file_uploader("Choose the .npy file containing TCR\u03B1 Embeddings", key=139)
pepti = st.file_uploader("Choose the .npy file containing Peptide Embeddings", key=149)
mhc = st.file_uploader("Choose the .npy file containing MHC Embeddings", key=151)
shorttask = 'apm'
group = (alpha,pepti,mhc)
elif task == "TCR\u03B2-Peptide-MHC":
beta = st.file_uploader("Choose the .npy file containing TCR\u03B2 Embeddings", key=157)
pepti = st.file_uploader("Choose the .npy file containing Peptide Embeddings", key=163)
mhc = st.file_uploader("Choose the .npy file containing MHC Embeddings", key=167)
shorttask = 'bpm'
group = (beta,pepti,mhc)
elif task == "TCR\u03B1-Peptide":
alpha = st.file_uploader("Choose the .npy file containing TCR\u03B1 Embeddings", key=173)
pepti = st.file_uploader("Choose the .npy file containing Peptide Embeddings", key=179)
shorttask = 'ap'
group = (alpha,pepti)
elif task == "TCR\u03B2-Peptide":
beta = st.file_uploader("Choose the .npy file containing TCR\u03B2 Embeddings", key=181)
pepti = st.file_uploader("Choose the .npy file containing Peptide Embeddings", key=191)
shorttask = 'bp'
group = (beta,pepti)
##################### ML predict function
# @st.cache_data
def predict_on_batch_output(dataset,shorttask,group):
if dataset == 'MCPAS':
dataset='mcpas'
elif dataset== 'VDJDB':
dataset ='vdjdb'
if dataset=='mcpas' and shorttask=='abp':
#load data
alpha, beta, pep = group
alpha_np, beta_np, pep_np = np.load(alpha), np.load(beta), np.load(pep)
#load model
model = load_model('models/mcpas/bestmodel_alphabetapeptide.hdf5',compile=False)
#predict_on_batch
output = model.predict_on_batch([alpha_np, beta_np, pep_np])
elif dataset=='mcpas' and shorttask=='abpm':
#load data
alpha, beta, pep, mhc = group
alpha_np, beta_np, pep_np, mhc_np = np.load(alpha), np.load(beta), np.load(pep), np.load(mhc)
#load model
model = load_model('models/mcpas/bestmodel_alphabetaptptidemhc.hdf5',compile=False)
#predict_on_batch
output = model.predict_on_batch([alpha_np, beta_np, pep_np, mhc_np])
elif dataset=='mcpas' and shorttask=='ap':
#load data
alpha, pep, = group
alpha_np, pep_np, = np.load(alpha), np.load(pep)
#load model
model = load_model('models/mcpas/bestmodel_alphapeptide.hdf5',compile=False)
#predict_on_batch
output = model.predict_on_batch([alpha_np,pep_np])
elif dataset=='mcpas' and shorttask=='bp':
#load data
beta, pep = group
beta_np, pep_np = np.load(beta), np.load(pep)
#load model
model = load_model('models/mcpas/bestmodel_betapeptide.hdf5',compile=False)
#predict_on_batch
output = model.predict_on_batch([beta_np, pep_np])
elif dataset=='mcpas' and shorttask=='apm':
#load data
alpha, pep, mhc = group
alpha_np, pep_np, mhc_np = np.load(alpha), np.load(pep), np.load(mhc)
#load model
model = load_model('models/mcpas/bestmodel_alphapeptidemhc.hdf5',compile=False)
#predict_on_batch
output = model.predict_on_batch([alpha_np, pep_np, mhc_np])
elif dataset=='mcpas' and shorttask=='bpm':
#load data
beta, pep, mhc = group
beta_np, pep_np, mhc_np = np.load(beta), np.load(pep), np.load(mhc)
#load model
model = load_model('models/mcpas/bestmodel_betapeptidemhc.hdf5',compile=False)
#predict_on_batch
output = model.predict_on_batch([beta_np, pep_np, mhc_np])
elif dataset=='vdjdb' and shorttask=='abp':
#load data
alpha, beta, pep = group
alpha_np, beta_np, pep_np = np.load(alpha), np.load(beta), np.load(pep)
#load model
model = load_model('models/vdjdb/bestmodel_alphabetapeptide.hdf5',compile=False)
#predict_on_batch
output = model.predict_on_batch([alpha_np, beta_np, pep_np])
elif dataset=='vdjdb' and shorttask=='abpm':
#load data
alpha, beta, pep, mhc = group
alpha_np, beta_np, pep_np, mhc_np = np.load(alpha), np.load(beta), np.load(pep), np.load(mhc)
#load model
model = load_model('models/vdjdb/bestmodel_alphabetapeptidemhc.hdf5',compile=False)
#predict_on_batch
output = model.predict_on_batch([alpha_np, beta_np, pep_np, mhc_np])
elif dataset=='vdjdb' and shorttask=='ap':
#load data
alpha, pep, = group
alpha_np, pep_np, = np.load(alpha), np.load(pep)
#load model
model = load_model('models/vdjdb/bestmodel_alphapeptide.hdf5',compile=False)
#predict_on_batch
output = model.predict_on_batch([alpha_np, pep_np])
elif dataset=='vdjdb' and shorttask=='bp':
#load data
beta, pep = group
beta_np, pep_np = np.load(beta), np.load(pep)
#load model
model = load_model('models/vdjdb/bestmodel_betapeptide.hdf5',compile=False)
#predict_on_batch
output = model.predict_on_batch([beta_np, pep_np])
elif dataset=='vdjdb' and shorttask=='apm':
#load data
alpha, pep, mhc = group
alpha_np, pep_np, mhc_np = np.load(alpha), np.load(pep), np.load(mhc)
#load model
model = load_model('models/vdjdb/bestmodel_alphapeptidemhc.hdf5',compile=False)
#predict_on_batch
output = model.predict_on_batch([alpha_np, pep_np, mhc_np])
elif dataset=='vdjdb' and shorttask=='bpm':
#load data
beta, pep, mhc = group
beta_np, pep_np, mhc_np = np.load(beta), np.load(pep), np.load(mhc)
#load model
model = load_model('models/vdjdb/bestmodel_betapeptidemhc.hdf5',compile=False)
#predict_on_batch
output = model.predict_on_batch([beta_np, pep_np, mhc_np])
# return np.around(output.squeeze(), 4)
val = np.squeeze(output)
return val
# @st.cache_data
def convert_df(df):
# IMPORTANT: Cache the conversion to prevent computation on every rerun
return df.to_csv().encode('utf-8')
#####################
if st.button('Submit'):
# with st.spinner('Wait for it...'):
# time.sleep(0.5)
# res = predict_on_batch_output(dataset,shorttask,group)
# st.write("Binding Probabilities")
# st.dataframe((np.round(res, 4)))
# csv = convert_df(pd.DataFrame(np.round(res, 4), columns=['output']))
# st.download_button(label="Download Predictions",data=csv,file_name='tcresm_predictions.csv', mime='text/csv')
try:
res = predict_on_batch_output(dataset,shorttask,group)
with st.spinner('Calculating ...'):
time.sleep(0.5)
st.write("Binding Probabilities")
st.dataframe((np.round(res, 4)), use_container_width=500, height=500)
csv = convert_df(pd.DataFrame(np.round(res, 4), columns=['output']))
st.download_button(label="Download Predictions",data=csv,file_name='tcresm_predictions.csv', mime='text/csv')
except:
st.error('Please ensure you have uploaded the files before pressing the Submit button', icon="🚨")
if st.button("Clear All"):
# Clear values from *all* all in-memory and on-disk data caches:
# i.e. clear values from both square and cube
st.cache.clear()
st.caption('Developed By: Shashank Yadav : shashank[at]arizona.edu', unsafe_allow_html=True) |