import json
import os
import random
import re
import sys
from datetime import datetime
from pathlib import Path
from typing import List, Optional
from uuid import uuid4

import gradio as gr
import numpy as np
import requests
from datasets import load_dataset
from huggingface_hub import (
    CommitScheduler,
    HfApi,
    InferenceClient,
    login,
    snapshot_download,
)
from PIL import Image
from glob import glob


session_token = os.environ.get("SessionToken")
login(token=session_token, add_to_git_credential=True)

DEFAILT_USERNAME_MESSAGE = "You must be logged in befor starting to label images."
REPO_URL = "glitchbench/GlitchBenchReviewData"
DATASET_URL = "glitchbench/GlitchBench"

SUBMIT_MESSAGE = "Submit the description"
SKIP_MESSAGE = "Skip, I can not spot the bug!"

glitchbench_dataset = load_dataset(DATASET_URL)["validation"]
dataset_size = len(glitchbench_dataset)

# map id to index:
id_to_index = {x["id"]: i for i, x in enumerate(glitchbench_dataset)}

JSON_DATASET_DIR = Path("local_dataset")
JSON_DATASET_DATA_DIR = JSON_DATASET_DIR / "data"
JSON_DATASET_PATH = JSON_DATASET_DATA_DIR / f"labels-{uuid4()}.json"


if not JSON_DATASET_DIR.exists():
    JSON_DATASET_DIR.mkdir()

if not JSON_DATASET_DATA_DIR.exists():
    JSON_DATASET_DATA_DIR.mkdir()

print("Downloading the dataset")
print(REPO_URL)

snapshot_download(
    repo_id=REPO_URL,
    allow_patterns="*.json",
    local_dir=JSON_DATASET_DIR,
    use_auth_token=session_token,
    repo_type="dataset",
)

scheduler = CommitScheduler(
    repo_id=REPO_URL,
    repo_type="dataset",
    folder_path=JSON_DATASET_DIR,
    path_in_repo="./",
    every=1,
    private=True,
)


def save_json(image_id: str, provided_description: str, username: str) -> None:
    with scheduler.lock:
        with JSON_DATASET_PATH.open("a") as f:
            json.dump(
                {
                    "username": username,
                    "image_id": image_id,
                    "user_description": provided_description,
                    "datetime": datetime.now().isoformat(),
                },
                f,
            )
            f.write("\n")


def set_username(profile: Optional[gr.OAuthProfile]) -> str:
    if profile is None:
        return DEFAILT_USERNAME_MESSAGE
    return profile["preferred_username"]


def start_labeling(username_label):
    if username_label == DEFAILT_USERNAME_MESSAGE:
        raise gr.Error("Please login first, then click start labeling")

    all_json_files = glob(str(JSON_DATASET_DATA_DIR / "*.json"))
    # read json files and keep records related to the current user
    all_user_records = []

    for json_file in all_json_files:
        with open(json_file) as f:
            for line in f:
                record = json.loads(line)
                if record["username"] == username_label:
                    all_user_records.append(record["image_id"])

    print(f"Found {len(all_user_records)} records for user {username_label}")

    # go throught all images in the dataset and exlcude those that are already labeled by the user
    remaining_indicies = set(range(dataset_size))
    solved_indices = [id_to_index[x] for x in all_user_records]
    remaining_indicies = remaining_indicies - set(solved_indices)

    print(f"Found {len(remaining_indicies)} remaining images for user {username_label}")

    return list(remaining_indicies), gr.Button(interactive=False)


def show_random_sample(username_label, remaining_batch):
    rindex = random.choice(remaining_batch)
    remaining_batch.remove(rindex)

    # get the image
    image = glitchbench_dataset[rindex]["image"]
    image_id = glitchbench_dataset[rindex]["id"]

    return image, image_id, "", remaining_batch


def write_user_description(username_label, image_id, user_description, skip_or_submit):
    if skip_or_submit == SKIP_MESSAGE:
        provided_description = "N/A"
    else:
        provided_description = user_description

    save_json(image_id, provided_description, username_label)


with gr.Blocks() as demo:
    gr.Markdown("## GlitchBench Dataset Labeling Tool")
    gr.Markdown("Help us to clean and label the GlitchBench dataset.")

    with gr.Row():
        username_label = gr.Text(label="Username", interactive=False)
        gr.LoginButton()
        gr.LogoutButton()

    start_button = gr.Button("Start Labeling")

    username_label.attach_load_event(set_username, None)

    glitch_image = gr.Image(label="Image")
    glitch_image_id = gr.Textbox(label="Image ID", visible=False)

    with gr.Row():
        user_description = gr.Textbox(lines=5, label="Description")
        with gr.Column():
            submit_button = gr.Button(SUBMIT_MESSAGE)
            Skip_btton = gr.Button(SKIP_MESSAGE)

    remaining_batch = gr.State()

    start_button.click(
        start_labeling, inputs=[username_label], outputs=[remaining_batch, start_button]
    ).then(
        show_random_sample,
        inputs=[username_label, remaining_batch],
        outputs=[glitch_image, glitch_image_id, user_description],
    )

    submit_button.click(
        write_user_description,
        inputs=[username_label, glitch_image_id, user_description, submit_button],
        outputs=[],
    ).then(
        show_random_sample,
        inputs=[username_label, remaining_batch],
        outputs=[glitch_image, glitch_image_id, user_description],
    )

    Skip_btton.click(
        write_user_description,
        inputs=[username_label, glitch_image_id, user_description, Skip_btton],
        outputs=[],
    ).then(
        show_random_sample,
        inputs=[username_label, remaining_batch],
        outputs=[glitch_image, glitch_image_id, user_description],
    )

demo.launch()