#!/usr/bin/env python

from __future__ import annotations

import gradio as gr
import PIL.Image
import zipfile
from genTag import genTag
from cropImage import cropImage
from checkIgnore import is_ignore
from createTagDom import create_tag_dom

def predict(image: PIL.Image.Image, score_threshold: float):
    result_threshold = genTag(image, score_threshold)
    result_html = ''
    for label, prob in result_threshold.items():
        result_html += create_tag_dom(label, is_ignore(label, 1), prob)
    result_html = '<div>' + result_html + '</div>'
    result_filter = {key: value for key, value in result_threshold.items() if not is_ignore(key, 1)}
    result_text = '<div id="m5dd_result">' + ', '.join(result_filter.keys()) + '</div>'
    crop_image = cropImage(image)
    return result_html, result_text, crop_image

def predict_batch(zip_file, score_threshold: float, progress=gr.Progress()):
    result = ''
    with zipfile.ZipFile(zip_file) as zf:
        for file in progress.tqdm(zf.namelist()):
            print(file)
            if file.endswith(".png") or file.endswith(".jpg") or file.endswith(".jpeg"):
                image_file = zf.open(file)
                image = PIL.Image.open(image_file)
                image = image.convert("RGB")
                result_threshold = genTag(image, score_threshold)
                result_filter = {key: value for key, value in result_threshold.items() if not is_ignore(key, 2)}
                tag = ', '.join(result_filter.keys())
                result = result + str(file) + '\n' + str(tag) + '\n\n'
    return result

with gr.Blocks(css="style.css", js="script.js") as demo:
    with gr.Tab(label='Single'):
        with gr.Row():
            with gr.Column(scale=1):
                image = gr.Image(label='Upload a image',
                                 type='pil',
                                 elem_classes='m5dd_image',
                                 sources=["upload", "clipboard"])
                score_threshold = gr.Slider(label='Score threshold',
                                            minimum=0,
                                            maximum=1,
                                            step=0.1,
                                            value=0.3)
                run_button = gr.Button('Run')
                with gr.Accordion(label="Crop Image", open=False):
                    crop_image = gr.Image(elem_classes='m5dd_image2',
                                          format='jpg',
                                          show_label=False,
                                          show_share_button=False,
                                          container=False)
                result_text = gr.HTML(value="")
            with gr.Column(scale=2):
                result_html = gr.HTML(value="")
    with gr.Tab(label='Batch'):
        with gr.Row():
            with gr.Column(scale=1):
                batch_file = gr.File(label="Upload a ZIP file containing images",
                                     file_types=['.zip'])
                score_threshold2 = gr.Slider(label='Score threshold',
                                             minimum=0,
                                             maximum=1,
                                             step=0.1,
                                             value=0.3)
                run_button2 = gr.Button('Run')
            with gr.Column(scale=2):
                result_text2 = gr.Textbox(lines=20,
                                          max_lines=20,
                                          label='Result',
                                          show_copy_button=True,
                                          autoscroll=False)

    run_button.click(
        fn=predict,
        inputs=[image, score_threshold],
        outputs=[result_html, result_text, crop_image],
        api_name='predict',
    )
    run_button2.click(
        fn=predict_batch,
        inputs=[batch_file, score_threshold2],
        outputs=[result_text2],
        api_name='predict_batch',
    )

if __name__ == "__main__":
    demo.queue(max_size=20).launch()