import os
import re
import requests
import json
import subprocess
from io import BytesIO
import uuid

from math import ceil
from tqdm import tqdm
from pathlib import Path

from db import Database

DB_FOLDER = Path("diffusers-gallery-data")

database = Database(DB_FOLDER)


CLASSIFIER_URL = "https://radames-aesthetic-style-nsfw-classifier.hf.space/run/inference"
ASSETS_URL = "https://d26smi9133w0oo.cloudfront.net/diffusers-gallery/"


def main():

    with database.get_db() as db:
        cursor = db.cursor()
        cursor.execute("""
            SELECT *
            FROM models
        """)
        results = list(cursor.fetchall())

    for row in tqdm(results):
        row_id = row['id']
        # keep json data on row_data
        row_data = json.loads(row['data'])
        print("updating row", row_id)
        images = row_data['images']

        # filter nones
        images = [i for i in images if i is not None]
        if len(images) > 0:
            # classifying only the first image
            images_urls = [ASSETS_URL + images[0]]
            response = requests.post(CLASSIFIER_URL, json={"data": [
                {"urls": images_urls},  # json urls: list of images urls
                False,  # enable/disable gallery image output
                None,  # single image input
                None,  # files input
            ]}).json()

            # data response is array data:[[{img0}, {img1}, {img2}...], Label, Gallery],
            class_data = response['data'][0][0]
            class_data_parsed = {row['label']: round(
                row['score'], 3) for row in class_data}

            # update row data with classificator data
            row_data['class'] = class_data_parsed
        else:
            row_data['class'] = {}
        with database.get_db() as db:
            cursor = db.cursor()
            cursor.execute("UPDATE models SET data = ? WHERE id = ?",
                           [json.dumps(row_data), row_id])
            db.commit()


if __name__ == "__main__":
    main()