import logging
import pathlib
import pickle
import gradio as gr
from typing import Dict, Any
import pandas as pd
from gt4sd.algorithms.generation.diffusion import (
    DiffusersGenerationAlgorithm,
    GeoDiffGenerator,
)
from utils import draw_grid_generate
from rdkit import Chem

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


def run_inference(prompt_file: str, prompt_id: int, number_of_samples: int):

    # Read file:
    with open(prompt_file.name, "rb") as f:
        prompts = pickle.load(f)

    if all(isinstance(x, int) for x in prompts.keys()):
        prompt = prompts[prompt_id]
    else:
        prompt = prompts

    config = GeoDiffGenerator(prompt=prompt)
    model = DiffusersGenerationAlgorithm(config)
    results = list(model.sample(number_of_samples))
    smiles = [Chem.MolToSmiles(m) for m in results]

    return draw_grid_generate(samples=smiles, n_cols=5)


if __name__ == "__main__":

    # Load metadata
    metadata_root = pathlib.Path(__file__).parent.joinpath("model_cards")

    examples = [
        [metadata_root.joinpath("mol_dct.pkl"), 0, 2],
        [metadata_root.joinpath("mol_dct.pkl"), 1, 2],
    ]

    with open(metadata_root.joinpath("article.md"), "r") as f:
        article = f.read()
    with open(metadata_root.joinpath("description.md"), "r") as f:
        description = f.read()

    demo = gr.Interface(
        fn=run_inference,
        title="GeoDiff",
        inputs=[
            gr.File(file_types=[".pkl"], label="GeoDiff prompt"),
            gr.Number(value=0, label="Prompt ID", precision=0),
            gr.Slider(minimum=1, maximum=5, value=2, label="Number of samples", step=1),
        ],
        outputs=gr.HTML(label="Output"),
        article=article,
        description=description,
        examples=examples,
    )
    demo.launch(debug=True, show_error=True)