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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 | |
| 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 | |
| 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).flatten() | |
| # # res = predict_output(dataset,shorttask,group) | |
| # print(type(res), res.shape) | |
| # 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(['Sample Number','Output'], use_container_width=500, height=500) | |
| val_df = pd.DataFrame({'Sample Number': [item+1 for item in range(0,res.shape[0])], 'Binding Probability': res.tolist()}) | |
| val_df = val_df.set_index(['Sample Number']) | |
| # st.dataframe(np.round(res, 4), use_container_width=500, height=500) | |
| st.dataframe(val_df, use_container_width=500, height=500) | |
| # csv = convert_df(pd.DataFrame(np.round(res, 4), columns=['output'])) | |
| csv = convert_df(val_df) | |
| 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_data.clear() | |
| st.caption('Developed By: Shashank Yadav : shashank[at]arizona.edu', unsafe_allow_html=True) | |