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 numpy as np
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(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 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()