import logging
import pathlib
import gradio as gr
import pandas as pd
from gt4sd.algorithms.generation.pgt import (
    PGT,
    PGTCoherenceChecker,
    PGTEditor,
    PGTGenerator,
)
from gt4sd.algorithms.registry import ApplicationsRegistry


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

MODEL_FN = {
    "PGTGenerator": PGTGenerator,
    "PGTEditor": PGTEditor,
    "PGTCoherenceChecker": PGTCoherenceChecker,
}


def run_inference(
    model_type: str,
    generator_task: str,
    editor_task: str,
    checker_task: str,
    prompt: str,
    second_prompt: str,
    length: int,
    k: int,
    p: float,
):

    kwargs = {"max_length": length, "top_k": k, "top_p": p}

    if model_type == "PGTGenerator":
        config = PGTGenerator(task=generator_task, input_text=prompt, **kwargs)
    elif model_type == "PGTEditor":
        config = PGTEditor(input_type=editor_task, input_text=prompt, **kwargs)
    elif model_type == "PGTCoherenceChecker":
        config = PGTCoherenceChecker(
            coherence_type=checker_task, input_a=prompt, input_b=second_prompt, **kwargs
        )

    model = PGT(config)
    text = list(model.sample(1))[0]

    return text


if __name__ == "__main__":

    # Preparation (retrieve all available algorithms)
    all_algos = ApplicationsRegistry.list_available()
    algos = [
        x["algorithm_application"]
        for x in list(filter(lambda x: "PGT" 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"), sep="|", header=None
    ).fillna("")
    print("Examples: ", examples.values.tolist())

    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()

    gen_tasks = [
        "title-to-abstract",
        "abstract-to-title",
        "abstract-to-claim",
        "claim-to-abstract",
    ]

    demo = gr.Interface(
        fn=run_inference,
        title="Patent Generative Transformer",
        inputs=[
            gr.Dropdown(algos, label="Model type", value="PGTGenerator"),
            gr.Dropdown(gen_tasks, label="Generator task", value="title-to-abstract"),
            gr.Dropdown(["abstract", "claim"], label="Editor task", value="abstract"),
            gr.Dropdown(
                ["title-abstract", "title-claim", "abstract-claim"],
                label="Checker task",
                value="title-abstract",
            ),
            gr.Textbox(
                label="Primary Text prompt",
                placeholder="Artificial intelligence and machine learning infrastructure",
                lines=5,
            ),
            gr.Textbox(
                label="Secondary text prompt (only coherence checker)",
                placeholder="",
                lines=1,
            ),
            gr.Slider(
                minimum=5, maximum=1024, value=512, label="Maximal length", step=1
            ),
            gr.Slider(minimum=2, maximum=500, value=50, label="Top-k", step=1),
            gr.Slider(minimum=0.5, maximum=1.0, value=0.95, label="Top-p"),
        ],
        outputs=gr.Textbox(label="Output"),
        article=article,
        description=description,
        examples=examples.values.tolist(),
    )
    demo.launch(debug=True, show_error=True)