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import streamlit as st
from transformers import AutoTokenizer, EsmModel
import torch
import json
def embed(aa_seq, tokenizer, model):
inputs = tokenizer(aa_seq, return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state.to_list()
return last_hidden_states
# selecing and loading a model
model_name = st.selectbox(
'Choose a model',
["facebook/esm2_t6_8M_UR50D", "facebook/esm2_t48_15B_UR50D"])
st.write('You selected model:', model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = EsmModel.from_pretrained(model_name)
#aa_seq_input = st.text_input('Type AA sequance here')
#uploading AA sequences file
uploaded_file = st.file_uploader("Upload JSON with AA sequences", type='json')
if uploaded_file is not None:
data = json.load(uploaded_file)
#st.write(data)
def embed_upload_file(upload_dict_dania):
# upload_dict_dania = {
# 'uid1': ['aa', 'aan']
# }
# output = {
# 'uid1': {'aa':[[[0.1298, ....]]], 'aan':[[[0.1298, ....]]]}
# }
output = {}
for uid, seqs in upload_dict_dania.items():
output[uid] = {}
for seq in seqs:
output[uid][seq] = embed(seq)
json_data = json.dumps(output)
st.download_button(
label = "Download JSON file",
data = json_data,
file_name = "esm-2 last hidden states.json",
mime = 'application/json'
)
if st.button('Get embedding'):
embed_upload_file(data)
st.write('Also, Dania is not gay')
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