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@@ -32,3 +32,15 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+models/mcpas/bestmodel_alphabetapeptide.hdf5 filter=lfs diff=lfs merge=lfs -text
+models/mcpas/bestmodel_alphabetaptptidemhc.hdf5 filter=lfs diff=lfs merge=lfs -text
+models/mcpas/bestmodel_alphapeptide.hdf5 filter=lfs diff=lfs merge=lfs -text
+models/mcpas/bestmodel_alphapeptidemhc.hdf5 filter=lfs diff=lfs merge=lfs -text
+models/mcpas/bestmodel_betapeptide.hdf5 filter=lfs diff=lfs merge=lfs -text
+models/mcpas/bestmodel_betapeptidemhc.hdf5 filter=lfs diff=lfs merge=lfs -text
+models/vdjdb/bestmodel_alphabetapeptide.hdf5 filter=lfs diff=lfs merge=lfs -text
+models/vdjdb/bestmodel_alphabetapeptidemhc.hdf5 filter=lfs diff=lfs merge=lfs -text
+models/vdjdb/bestmodel_alphapeptide.hdf5 filter=lfs diff=lfs merge=lfs -text
+models/vdjdb/bestmodel_alphapeptidemhc.hdf5 filter=lfs diff=lfs merge=lfs -text
+models/vdjdb/bestmodel_betapeptide.hdf5 filter=lfs diff=lfs merge=lfs -text
+models/vdjdb/bestmodel_betapeptidemhc.hdf5 filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,224 @@
+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_data.clear()
+
+
+
+st.caption('Developed By: Shashank Yadav : shashank[at]arizona.edu', unsafe_allow_html=True)

models/mcpas/bestmodel_alphabetapeptide.hdf5

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models/mcpas/bestmodel_alphabetaptptidemhc.hdf5

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models/mcpas/bestmodel_alphapeptide.hdf5

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models/mcpas/bestmodel_alphapeptidemhc.hdf5

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models/mcpas/bestmodel_betapeptide.hdf5

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models/mcpas/bestmodel_betapeptidemhc.hdf5

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models/vdjdb/bestmodel_alphabetapeptide.hdf5

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models/vdjdb/bestmodel_alphabetapeptidemhc.hdf5

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models/vdjdb/bestmodel_alphapeptide.hdf5

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models/vdjdb/bestmodel_alphapeptidemhc.hdf5

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models/vdjdb/bestmodel_betapeptide.hdf5

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models/vdjdb/bestmodel_betapeptidemhc.hdf5

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requirements.txt

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+numpy
+pandas
+tensorflow

sample_input_data.zip

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