app.py

import streamlit as st
import streamlit.components.v1 as components
import pandas as pd
import mols2grid
from ipywidgets import interact, widgets
import textwrap
import moses
from transformers import EncoderDecoderModel, RobertaTokenizer

from moses.metrics.utils import QED, SA, logP, NP, weight, get_n_rings
from moses.utils import mapper, get_mol

# @st.cache(allow_output_mutation=False, hash_funcs={Tokenizer: str})
from typing import List

from util import filter_dataframe


@st.cache_resource
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 count(smiles_list: List[str]):
    counts = []
    for smiles in smiles_list:
        counts.append(len(smiles))

    return counts


def remove_none_elements(mol_list, smiles_list):
    filtered_mol_list = []
    filtered_smiles_list = []
    indices = []
    for i, element in enumerate(mol_list):
        if element is not None:
            filtered_mol_list.append(element)
        else:
            indices.append(i)
    removed_len = len(indices)

    for i in range(len(smiles_list)):
        if i not in indices:
            filtered_smiles_list.append(smiles_list.__getitem__(i))

    return filtered_mol_list, filtered_smiles_list, removed_len


def format_list_numbers(lst):
    for i, value in enumerate(lst):
        lst[i] = float("{:.3f}".format(value))


def generate_molecules(model_name, num_mols, max_new_tokens, do_sample, num_beams, target, pool):
    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()
    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")
    # mol_list = list(map(MolFromSmiles, output_smiles))
    # print(mol_list)
    # QED_scores = list(map(QED.qed, mol_list))
    # print(QED_scores)
    # st.write(output_smiles)
    mol_list = mapper(pool)(get_mol, output_smiles)
    mol_list, output_smiles, removed_len = remove_none_elements(mol_list, output_smiles)
    if removed_len != 0:
        st.write(f"#### Note that: {removed_len} numbers of generated invalid molecules are discarded.")

    QED_scores = mapper(pool)(QED, mol_list)
    SA_scores = mapper(pool)(SA, mol_list)
    logP_scores = mapper(pool)(logP, mol_list)
    NP_scores = mapper(pool)(NP, mol_list)
    weight_scores = mapper(pool)(weight, mol_list)

    format_list_numbers(QED_scores)
    format_list_numbers(SA_scores)
    format_list_numbers(logP_scores)
    format_list_numbers(NP_scores)
    format_list_numbers(weight_scores)

    df_smiles = pd.DataFrame(
        {'SMILES': output_smiles, "QED": QED_scores, "SA": SA_scores, "logP": logP_scores, "NP": NP_scores,
         "Weight": weight_scores})

    return df_smiles


def warm_molgen_demo():
    with st.form("my_form"):
        with st.sidebar:
            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.",
            )
            do_sample = st.sidebar.selectbox(
                "Sampling?",
                (True, False),
                help="Whether or not to use sampling; use beam decoding otherwise.",
            )
            target = st.text_area(
                "Target Sequence",
                "MENTENSVDSKSIKNLEPKIIHGSESMDSGISLDNSYKMDYPEMGLCIIINNKNFHKSTG",
            )
            generate_new_molecules = st.form_submit_button("Generate Molecules")

    num_beams = None if do_sample is True else int(num_mols)

    pool = 1

    if generate_new_molecules:
        st.session_state.df = generate_molecules(model_name, num_mols, max_new_tokens, do_sample, num_beams,
                                                 target, pool)
    if 'df' not in st.session_state:
        st.session_state.df = generate_molecules(model_name, num_mols, max_new_tokens, do_sample, num_beams,
                                                 target, pool)
    df = st.session_state.df

    filtered_df = filter_dataframe(df)
    if filtered_df.empty:
        st.markdown(
            """
            <span style='color: blue; font-size: 30px;'>No molecules were found with specified properties.</span>
        """,
            unsafe_allow_html=True
        )
    else:
        raw_html = mols2grid.display(filtered_df, height=1000)._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!
"""
)

warm_molgen_demo()