pages/2_✨_ChemBERTaLM.py

import streamlit as st
import streamlit.components.v1 as components
import pandas as pd
import mols2grid
import textwrap
from transformers import RobertaForCausalLM, RobertaTokenizer, pipeline

# @st.cache(allow_output_mutation=False, hash_funcs={Tokenizer: str})
@st.cache(suppress_st_warning=True)
def load_models():
    model = RobertaForCausalLM.from_pretrained("gokceuludogan/ChemBERTaLM")
    return model


def chembertalm_demo():
    tokenizer = RobertaTokenizer.from_pretrained("gokceuludogan/ChemBERTaLM")
    model = load_models()
    generator = pipeline("text-generation", model=model, tokenizer=tokenizer)

    st.sidebar.subheader("Configurable parameters")

    num_mols = st.sidebar.number_input(
        "Number of generated molecules",
        min_value=0,
        max_value=200,
        value=20,
        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 = True # 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_input(
    #     "Input Sequence",
    #     "",
    # )
    target = ""
    params = {'do_sample': do_sample, 'num_return_sequences': num_mols, 'max_length': max_new_tokens}
    outputs = generator(target, **params)
    output_smiles = [output["generated_text"] for output in outputs]
    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 RobertaForCausalLM, RobertaTokenizer, pipeline

    tokenizer = RobertaTokenizer.from_pretrained("gokceuludogan/ChemBERTaLM")
    model = RobertaForCausalLM.from_pretrained("gokceuludogan/ChemBERTaLM")
    generator = pipeline("text-generation", model=model, tokenizer=tokenizer)

    params = {params}
    outputs = generator("{target}", **params)
    output_smiles = [output["generated_text"] for output in outputs]
    """
    st.code(textwrap.dedent(generation_code)) # textwrap.dedent("".join("Halletcez")))

st.set_page_config(page_title="ChemBERTaLM Demo", page_icon="✨", layout='wide')
st.markdown("# ChemBERTaLM Demo")
st.sidebar.header("ChemBERTaLM Demo")
st.markdown(
    """
    This demo illustrates ChemBERTaLM models' generation capabilities. 


    Given a set of parameters, ChemBERTaLM generates a collection of molecules.


    Please configure parameters from the sidebar 👈 to generate molecules!


    See below for saving the output molecules and the code snippet generating them!
"""
)

chembertalm_demo()