#! /usr/bin/env bash

function image_classifier() {
    abcli_image_classifier $@
}

function abcli_image_classifier() {
    local task=$(abcli_unpack_keyword "$1" help)

    if [ "$task" == "help" ] ; then
        abcli_help_line "$abcli_cli_name image_classifier describe object_1" \
            "describe model object_1."
        abcli_help_line "$abcli_cli_name image_classifier install" \
            "install image_classifier."
        abcli_help_line "$abcli_cli_name image_classifier predict object_1 object_2" \
            "run image_classifier model object_1 predict on data object_2."
        abcli_help_line "$abcli_cli_name image_classifier save name_1 object_1 [force]" \
            "[force] save image_classifier in object_1 as name_1."
        abcli_help_line "$abcli_cli_name image_classifier train object_1" \
            "train image_classifier on data object_1."

        if [ "$(abcli_keyword_is $2 verbose)" == true ] ; then
            python3 -m fashion_mnist.image_classifier --help
        fi
        return
    fi

    if [[ $(type -t abcli_image_classifier_$task) == "function" ]] ; then
        abcli_image_classifier_$task ${@:2}
        return
    fi

    if [ "$task" == "describe" ] ; then
        local model_object_name="$2"

        abcli_download $model_object_name

        python3 -m image_classifier \
            describe \
            --model_path $abcli_object_root/$model_object_name \
            ${@:3}

        return
    fi

    if [ "$task" == "install" ] ; then
        conda install -y -c anaconda seaborn
        return
    fi

    if [ "$task" == "release" ] ; then
        local model_name=$(abcli_clarify_arg "$2" image-classifier)

        abcli_huggingface release image-classifier $model_name ${@:3}
        return
    fi

    abcli_log_error "-fashion_mnist: image-classifier: $task: command not found."
}

function abcli_image_classifier_predict() {
    local model_object=$(abcli_clarify_object "$1")
    local data_object=$(abcli_clarify_object "$2")

    abcli_download object $model_object
    abcli_download object $data_object

    abcli_log "image_classifier($model_object).predict($data_object)"

    if [ ! -f "$abcli_object_root/$data_object/test_images.pyndarray" ] ; then
        python3 -m image_classifier \
            preprocess \
            --infer_annotation 0 \
            --model_path $abcli_object_root/$model_object \
            --objects $abcli_object_root/$data_object \
            --output_path $abcli_object_root/$data_object \
            --purpose predict \
            ${@:3}
    fi

    cp -v ../$data_object/*.pyndarray .
    cp -v ../$model_object/image_classifier/model/class_names.json .

    python3 -m image_classifier \
        predict \
        --data_path $abcli_object_root/$data_object \
        --model_path $abcli_object_root/$model_object \
        --output_path $abcli_object_path \
        ${@:4}

        abcli_tag set . image_classifier,predict
}

function abcli_image_classifier_train() {
    local data_object=$(abcli_clarify_object "$1" $abcli_object_name)

    abcli_download object $data_object

    local options=$2
    local do_color=$(abcli_option_int "$options" "color" 0)
    local do_convnet=$(abcli_option_int "$options" "convnet" 0)
    local do_validate=$(abcli_option_int "$options" "validate" 0)

    local extra_args=""
    if [ "$do_validate" == 1 ] ; then
        local extra_args="--epochs 2"
    fi

    abcli_log "image_classifier.train($data_object): $options"

    python3 -m image_classifier \
        train \
        --color $do_color \
        --convnet $do_convnet \
        --data_path $abcli_object_root/$data_object \
        --model_path $abcli_object_path \
        $extra_args \
        ${@:3}

    abcli_tag set . image_classifier,train
}