import logging
from typing import List
import numpy as np
import mols2grid
import pandas as pd
from rdkit import Chem

logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())


def draw_grid_predict(
    sequences: List[str], properties: np.array, property_names: List[str], domain: str
) -> str:
    """
    Uses mols2grid to draw a HTML grid for the prediction

    Args:
        sequences: Sequences for which properties are predicted.
        properties: Predicted properties. Array of shape (n_samples, n_properties).
        names: List of property names
        domain: Domain of the prediction (molecules or proteins).

    Returns:
        HTML to display
    """

    if domain not in ["Molecules", "Proteins"]:
        raise ValueError(f"Unsupported domain {domain}")

    if domain == "Proteins":
        converter = lambda x: Chem.MolToSmiles(Chem.MolFromFASTA(x))
    else:
        converter = lambda x: x

    smiles = []
    for sequence in sequences:
        try:
            seq = converter(sequence)
            smiles.append(seq)
        except Exception:
            logger.warning(f"Could not draw sequence {seq}")

    result = pd.DataFrame({"SMILES": smiles})
    if domain == "Proteins":
        result["Seqs"] = sequences
    for i, name in enumerate(property_names):
        result[name] = properties[:, i]
    n_cols = min(3, len(result))
    size = (140, 200) if len(result) > 3 else (600, 700)
    obj = mols2grid.display(
        result,
        tooltip=list(result.keys()),
        height=1100,
        n_cols=n_cols,
        name="Results",
        size=size,
    )
    return obj.data