#!/usr/bin/env python3
from os import listdir, path, PathLike, remove
from os.path import isfile, join

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
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:
        return False


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 path.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]

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))

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.concatenate(vectors)
save_matrix(image_mat, 'images.fbin')

index = Index(ndim=256, metric='cos')
image_mat = load_matrix('images.fbin')

for idx, vector in tqdm(enumerate(vectors), desc='Indexing vectors'):
    index.add(idx, vector.flatten())

index.save('images.usearch')