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import streamlit as st
import streamlit.components.v1 as components
import pandas as pd
import mols2grid
import textwrap
import numpy as np
from transformers import EncoderDecoderModel, RobertaTokenizer
# @st.cache(allow_output_mutation=False, hash_funcs={Tokenizer: str})
@st.cache(suppress_st_warning=True)
def load_models():
# protein_tokenizer = RobertaTokenizer.from_pretrained("gokceuludogan/WarmMolGenTwo")
# mol_tokenizer = RobertaTokenizer.from_pretrained("seyonec/PubChem10M_SMILES_BPE_450k")
model1 = EncoderDecoderModel.from_pretrained("gokceuludogan/WarmMolGenOne")
model2 = EncoderDecoderModel.from_pretrained("gokceuludogan/WarmMolGenTwo")
return model1, model2#, protein_tokenizer, mol_tokenizer
def warmmolgen_demo():
protein_tokenizer = RobertaTokenizer.from_pretrained("gokceuludogan/WarmMolGenTwo")
mol_tokenizer = RobertaTokenizer.from_pretrained("seyonec/PubChem10M_SMILES_BPE_450k")
#model1, model2, protein_tokenizer, mol_tokenizer = load_models()
model1, model2 = load_models()
st.sidebar.subheader("Configurable parameters")
model_name = st.sidebar.selectbox(
"Model Selector",
options=[
"WarmMolGenOne",
"WarmMolGenTwo",
],
index=0,
)
num_mols = st.sidebar.number_input(
"Number of generated molecules",
min_value=0,
max_value=20,
value=10,
help="The number of molecules to be generated.",
)
max_new_tokens = st.sidebar.number_input(
"Maximum length",
min_value=0,
max_value=1024,
value=128,
help="The maximum length of the sequence to be generated.",
)
# temp = st.sidebar.slider(
# "Temperature",
# value=1.0,
# min_value=0.1,
# max_value=100.0,
# help="The value used to module the next token probabilities.",
# )
# top_k = st.sidebar.number_input(
# "Top k",
# value=10,
# help="The number of highest probability vocabulary tokens to keep for top-k-filtering.",
# )
# top_p = st.sidebar.number_input(
# "Top p",
# value=0.95,
# help=" If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.",
# )
do_sample = st.sidebar.selectbox(
"Sampling?",
(True, False),
help="Whether or not to use sampling; use beam decoding otherwise.",
)
# num_beams = st.sidebar.number_input(
# "Number of beams",
# min_value=0,
# max_value=20,
# value=0,
# help="The number of beams to use for beam search.",
# )
num_beams = None if do_sample is True else int(num_mols)
# repetition_penalty = st.sidebar.number_input(
# "Repetition Penalty",
# min_value=0.0,
# value=3.0,
# step=0.1,
# help="The parameter for repetition penalty. 1.0 means no penalty",
# )
# no_repeat_ngram_size = st.sidebar.number_input(
# "No Repeat N-Gram Size",
# min_value=0,
# value=3,
# help="If set to int > 0, all ngrams of that size can only occur once.",
# )
target = st.text_area(
"Target Sequence",
"MENTENSVDSKSIKNLEPKIIHGSESMDSGISLDNSYKMDYPEMGLCIIINNKNFHKSTG",
)
inputs = protein_tokenizer(target, return_tensors="pt")
model = model1 if model_name == 'WarmMolGenOne' else model2
outputs = model.generate(**inputs, decoder_start_token_id=mol_tokenizer.bos_token_id,
eos_token_id=mol_tokenizer.eos_token_id, pad_token_id=mol_tokenizer.eos_token_id,
max_length=int(max_new_tokens), num_return_sequences=int(num_mols),
do_sample=do_sample, num_beams=num_beams)
output_smiles = mol_tokenizer.batch_decode(outputs, skip_special_tokens=True)
st.write("### Generated Molecules")
#st.write(output_smiles)
df_smiles = pd.DataFrame({'SMILES': output_smiles})
#st.write(df_smiles)
raw_html = mols2grid.display(df_smiles, mapping={"SMILES": "SMILES"})._repr_html_()
components.html(raw_html, width=900, height=450, scrolling=True)
st.markdown("## How to Generate")
generation_code = f"""
from transformers import EncoderDecoderModel, RobertaTokenizer
protein_tokenizer = RobertaTokenizer.from_pretrained("gokceuludogan/{model_name}")
mol_tokenizer = RobertaTokenizer.from_pretrained("seyonec/PubChem10M_SMILES_BPE_450k")
model = EncoderDecoderModel.from_pretrained("gokceuludogan/{model_name}")
inputs = protein_tokenizer("{target}", return_tensors="pt")
outputs = model.generate(**inputs, decoder_start_token_id=mol_tokenizer.bos_token_id,
eos_token_id=mol_tokenizer.eos_token_id, pad_token_id=mol_tokenizer.eos_token_id,
max_length={max_new_tokens}, num_return_sequences={num_mols}, do_sample={do_sample}, num_beams={num_beams})
mol_tokenizer.batch_decode(outputs, skip_special_tokens=True)
"""
st.code(textwrap.dedent(generation_code)) # textwrap.dedent("".join("Halletcez")))
st.set_page_config(page_title="WarmMolGen Demo", page_icon="🔥", layout='wide')
st.markdown("# WarmMolGen Demo")
st.sidebar.header("WarmMolGen Demo")
st.markdown(
"""
This demo illustrates WarmMolGen models' generation capabilities.
Given a target sequence and a set of parameters, the models generate molecules targeting the given protein sequence.
Please enter an input sequence below 👇 and configure parameters from the sidebar 👈 to generate molecules!
See below for saving the output molecules and the code snippet generating them!
"""
)
warmmolgen_demo()
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