import datetime
import os.path
import sys
import uuid
from os import PathLike

import gradio as gr
import pandas as pd
import torch

from config import APP_CONFIG
from data_repository import REPOSITORY_INSTANCE, ModelScoringResult
from designs_submission_validations import validate_github_link, validate_user_designs
from domain_constants import SCORE_NAMES_MAP

sys.path.append(os.path.join(os.path.dirname(__file__), "bike_bench_internal/src/"))
from bikebench.benchmarking.benchmarking_utils import evaluate_designs
from bikebench.data_loading import data_loading

COLUMNS = list(data_loading.load_bike_bench_train().columns)

def compute_scores(user_gen_designs: pd.DataFrame) -> ModelScoringResult:
    user_gen_designs = pd.DataFrame(user_gen_designs, columns=COLUMNS)
    designs_length = len(user_gen_designs)
    if designs_length < 10_000:
        raise Exception(f"Too few designs to evaluate. Expected > 10,000, got {designs_length}")
    data_tens = torch.tensor(user_gen_designs.values, dtype=torch.float32)
    main_scores, detailed_scores, all_evaluation_scores = evaluate_designs(data_tens)
    return ModelScoringResult(
        uuid=str(uuid.uuid4()),
        submission_time=datetime.datetime.now(),
        design_quality=main_scores[SCORE_NAMES_MAP["design_quality"]],
        diversity_dpp=main_scores[SCORE_NAMES_MAP["diversity_dpp"]],
        mean_novelty=main_scores[SCORE_NAMES_MAP["mean_novelty"]],
        sim_to_data_mmd=main_scores[SCORE_NAMES_MAP["sim_to_data_mmd"]],
        mean_violations=main_scores[SCORE_NAMES_MAP["mean_violations"]],
        binary_validity=main_scores[SCORE_NAMES_MAP["binary_validity"]],
    )


def process_generated_designs(github_link: str, file: PathLike[str]):
    validate_github_link(github_link)
    with open(file, 'r') as user_file:
        user_gen_designs = pd.read_csv(user_file)
    validate_user_designs(user_gen_designs)
    scores = compute_scores(user_gen_designs)
    REPOSITORY_INSTANCE.add_row(scores)
    return f"File uploaded successfully, uuid {scores.uuid}"


def build_approval_app():
    pass


def build_app():
    with gr.Blocks() as gradio_app:
        with gr.Tab("Bike Bench Leaderboard"):
            gr.Markdown("Hello beautiful people!")
            gr.Dataframe(REPOSITORY_INSTANCE.get_data_to_display, label="Scores of Previous Files")

        with gr.Tab("Upload File"):
            gr.Interface(
                fn=process_generated_designs,
                inputs=[
                    gr.Textbox(label="Github Link"),
                    gr.File(label="Upload a file"),
                ],
                outputs="text",
                title="Bike Bench Leaderboard",
                description="Upload a file to see the result."
            )
    return gradio_app


build_app().launch(debug=(not APP_CONFIG.production))