#!/usr/bin/env python3
from os import PathLike, listdir, remove
from os.path import isfile, join, exists
from mimetypes import guess_type
from base64 import b64encode

import pandas as pd
import numpy as np
from PIL import Image
from PIL import ImageFile
from tqdm import tqdm

from uform import get_model
from usearch.index import Index, MetricKind
from usearch.io import save_matrix, load_matrix

ImageFile.LOAD_TRUNCATED_IMAGES = True


def is_image(path: PathLike) -> bool:
    if not isfile(path):
        return False
    try:
        Image.open(path)
        return True
    except Exception:
        return False


def image_to_data(path: PathLike) -> str:
    """Convert a file (specified by a path) into a data URI."""
    if not exists(path):
        raise FileNotFoundError
    mime, _ = guess_type(path)
    with open(path, 'rb') as fp:
        data = fp.read()
        data64 = b64encode(data).decode('utf-8')
        return f'data:{mime}/jpg;base64,{data64}'


def trim_extension(filename: str) -> str:
    return filename.rsplit('.', 1)[0]


names = sorted(f for f in listdir('images') if is_image(join('images', f)))
names = [trim_extension(f) for f in names]

table = pd.read_table('images.tsv') if exists(
    'images.tsv') else pd.read_table('images.csv')
table = table[table['photo_id'].isin(names)]
table = table.sort_values('photo_id')
table.reset_index()
table.to_csv('images.csv', index=False)

names = list(set(table['photo_id']).intersection(names))
names_to_delete = [f for f in listdir(
    'images') if trim_extension(f) not in names]
names = list(table['photo_id'])

if len(names_to_delete) > 0:
    print(f'Plans to delete: {len(names_to_delete)} images without metadata')
    for name in names_to_delete:
        remove(join('images', name))

if not exists('images.fbin'):
    model = get_model('unum-cloud/uform-vl-english')
    vectors = []

    for name in tqdm(names, desc='Vectorizing images'):
        image = Image.open(join('images', name + '.jpg'))
        image_data = model.preprocess_image(image)
        image_embedding = model.encode_image(image_data).detach().numpy()
        vectors.append(image_embedding)

    image_mat = np.vstack(vectors)
    save_matrix(image_mat, 'images.fbin')

if not exists('images.txt'):

    datas = []
    for name in tqdm(names, desc='Encoding images'):
        data = image_to_data(join('images', name + '.jpg'))
        datas.append(data)

    with open('images.txt', 'w') as f:
        f.write('\n'.join(datas))


if not exists('images.usearch'):
    image_mat = load_matrix('images.fbin')
    count = image_mat.shape[0]
    ndim = image_mat.shape[1]
    index = Index(ndim=ndim, metric=MetricKind.Cos)

    for idx in tqdm(range(count), desc='Indexing vectors'):
        index.add(idx, image_mat[idx, :].flatten())

    index.save('images.usearch')