#!/usr/bin/env python

import json
import click
import itertools
from autotagger import Autotagger
from pathlib import Path
from more_itertools import ichunked

@click.command(help="Automatically generate tags for an image.", context_settings=dict(max_content_width=140))
@click.option("-t", "--threshold", default=0.01, type=float, show_default=True, help="The minimum tag confidence level.")
@click.option("-n", "--limit", default=50, type=int, show_default=True, help="The maximum number of tags to return per image.")
@click.option("-b", "--batch", "bs", default=128, type=int, show_default=True, help="The number of images to process per batch.")
@click.option("--group-tags/--flatten-tags", default=True, show_default=True, help="Output rows in {filename, tags} format or {filename, tag, score} format.")
@click.option("-m", "--model", default="models/model.pth", type=click.Path(exists=True), show_default=True, help="The model to use.")
@click.argument("file", nargs=-1, type=click.Path(exists=True, allow_dash=True), required=True)
def main(file, threshold, limit, bs, group_tags, model):
    autotagger = Autotagger(model)

    for filepaths in ichunked(get_filepaths(file), bs):
        filepaths = list(filepaths)
        predictions = autotagger.predict(filepaths, threshold=threshold, limit=limit, bs=bs)

        for filepath, tags in zip(filepaths, predictions):
            if group_tags:
                data = { "filename": filepath, "tags": tags }
                click.echo(json.dumps(data))
            else:
                for tag, score in tags.items():
                    data = { "filename": filepath, "tag": tag, "score": score }
                    click.echo(json.dumps(data))

def get_filepaths(paths):
    files = (recurse_dir(p) if Path(p).is_dir() else iter([p]) for p in paths)
    return itertools.chain(*files)

def recurse_dir(directory):
    return (path for path in Path(directory).glob("**/*") if not path.is_dir())

if __name__ == "__main__":
    main()