#!/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, b64decode
from io import BytesIO
import re

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_onnx
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 data_to_image(data_uri: str) -> Image:
    """Convert a base64-encoded data URI to a Pillow Image."""
    base64_str = re.search(r"base64,(.*)", data_uri).group(1)
    image_data = b64decode(base64_str)
    image = Image.open(BytesIO(image_data))
    return image


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") and 0:
    model, processor = get_model_onnx(
        "unum-cloud/uform-vl-english-small",
        device="cpu",
        dtype="fp32",
    )
    vectors = []

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

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

if not exists("images.base64.txt"):

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

    with open("images.base64.txt", "w") as f:
        f.write("\n".join(datas))

if not exists("images.names.txt"):
    with open("images.names.txt", "w") as f:
        f.write("\n".join(names))

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")