import logging
import pathlib
from typing import List

import gradio as gr
import numpy as np
import pandas as pd
from gt4sd.algorithms.conditional_generation.paccmann_rl import (
    PaccMannRL,
    PaccMannRLOmicBasedGenerator,
    PaccMannRLProteinBasedGenerator,
)
from gt4sd.algorithms.generation.paccmann_vae import PaccMannVAE, PaccMannVAEGenerator
from gt4sd.algorithms.registry import ApplicationsRegistry

from utils import draw_grid_generate

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

TITLE = "MoLeR"


def run_inference(
    algorithm_version: str,
    inference_type: str,
    protein_target: str,
    omics_target: str,
    temperature: float,
    length: float,
    number_of_samples: int,
):
    if inference_type == "Unbiased":
        algorithm_class = PaccMannVAEGenerator
        model_class = PaccMannVAE
        target = None
    elif inference_type == "Conditional":
        if "Protein" in algorithm_version:
            algorithm_class = PaccMannRLProteinBasedGenerator
            target = protein_target
        elif "Omic" in algorithm_version:
            algorithm_class = PaccMannRLOmicBasedGenerator
            try:
                test_target = [float(x) for x in omics_target.split(" ")]
            except Exception:
                raise ValueError(
                    f"Expected 2128 space-separated omics values, got {omics_target}"
                )
            if len(test_target) != 2128:
                raise ValueError(
                    f"Expected 2128 omics values, got {len(target)}: {target}"
                )
            target = omics_target
        else:
            raise ValueError(f"Unknown algorithm version {algorithm_version}")
        model_class = PaccMannRL
    else:
        raise ValueError(f"Unknown inference type {inference_type}")

    config = algorithm_class(
        algorithm_version.split("_")[-1],
        temperature=temperature,
        generated_length=length,
    )
    model = model_class(config, target=target)
    samples = list(model.sample(number_of_samples))

    return draw_grid_generate(samples=samples, n_cols=5)


if __name__ == "__main__":

    # Preparation (retrieve all available algorithms)
    all_algos = ApplicationsRegistry.list_available()
    algos = [
        x["algorithm_application"].split("Based")[0].split("PaccMannRL")[-1]
        + "_"
        + x["algorithm_version"]
        for x in list(filter(lambda x: "PaccMannRL" in x["algorithm_name"], all_algos))
    ]

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

    examples = pd.read_csv(metadata_root.joinpath("examples.csv"), header=None).fillna(
        ""
    )

    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="PaccMannRL",
        inputs=[
            gr.Dropdown(algos, label="Algorithm version", value="Protein_v0"),
            gr.Radio(
                choices=["Conditional", "Unbiased"],
                label="Inference type",
                value="Conditional",
            ),
            gr.Textbox(
                label="Protein target",
                placeholder="MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTT",
                lines=1,
            ),
            gr.Textbox(
                label="Gene expression target",
                placeholder=f"{' '.join(map(str, np.round(np.random.rand(2128), 2)))}",
                lines=1,
            ),
            gr.Slider(minimum=0.5, maximum=2, value=1, label="Decoding temperature"),
            gr.Slider(
                minimum=5,
                maximum=400,
                value=100,
                label="Maximal sequence length",
                step=1,
            ),
            gr.Slider(
                minimum=1, maximum=50, value=10, label="Number of samples", step=1
            ),
        ],
        outputs=gr.HTML(label="Output"),
        article=article,
        description=description,
        examples=examples.values.tolist(),
    )
    demo.launch(debug=True, show_error=True)