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from datasets import load_dataset
import gradio as gr
import os
import random

wmtis = load_dataset("nlphuji/wmtis")['test']
print(f"Loaded WMTIS, first example:")
print(wmtis[0])
dataset_size = len(wmtis) - 1

IMAGE = 'image'
IMAGE_DESIGNER = 'image_designer'
DESIGNER_EXPLANATION = 'designer_explanation'
CROWD_CAPTIONS = 'crowd_captions'
CROWD_EXPLANATIONS = 'crowd_explanations'
CROWD_UNDERSPECIFIED_CAPTIONS = 'crowd_underspecified_captions'
# CROWD_NEGATIVE_EXPLANATIONS = 'crowd_negative_explanations'
QA = 'question_answering_pairs'
IMAGE_ID = 'image_id'
left_side_columns = [IMAGE]
# left_side_columns = [IMAGE, DESIGNER_EXPLANATION, IMAGE_DESIGNER, IMAGE_ID]
right_side_columns = [x for x in wmtis.features.keys() if x not in left_side_columns and x not in [QA]]
enumerate_cols = [CROWD_CAPTIONS, CROWD_EXPLANATIONS, CROWD_UNDERSPECIFIED_CAPTIONS]
emoji_to_label = {IMAGE: 'πŸ–ΌοΈ, πŸ“·, πŸŒ„', IMAGE_DESIGNER: '🎨, πŸ§‘β€πŸŽ¨, πŸ’»', DESIGNER_EXPLANATION: 'πŸ’‘, πŸ€”, πŸ§‘β€πŸŽ¨', CROWD_CAPTIONS: 'πŸ‘₯, πŸ’¬, πŸ“', CROWD_EXPLANATIONS: 'πŸ‘₯, πŸ’‘, πŸ€”', CROWD_UNDERSPECIFIED_CAPTIONS: 'πŸ‘₯, πŸ’¬, πŸ‘Ž',
                  QA: '❓, πŸ€”, πŸ’‘', IMAGE_ID: 'πŸ”, πŸ“„, πŸ’Ύ'}
def func(index):
    example = wmtis[index]
    values = get_instance_values(example)
    return values


def get_instance_values(example):
    values = []
    for k in left_side_columns + right_side_columns:
        if k in enumerate_cols:
            value = list_to_string(example[k])
        elif k == QA:
            qa_list = [f"Q: {x[0]} A: {x[1]}" for x in example[k]]
            value = list_to_string(qa_list)
        else:
            value = example[k]
        values.append(value)
    return values
def list_to_string(lst):
    return '\n'.join(['{}. {}'.format(i+1, item) for i, item in enumerate(lst)])

demo = gr.Blocks()

with demo:
    gr.Markdown("# Slide to iterate WMTIS")

    with gr.Column():
        slider = gr.Slider(minimum=0, maximum=dataset_size, step=1, label='index')
        with gr.Row():
            # index = random.choice(range(0, dataset_size))
            index = slider.label
            example = wmtis[index]
            instance_values = get_instance_values(example)
            with gr.Column():
                # image_input = gr.Image(value=wmtis[index]["image"])
                inputs_left = []
                assert len(left_side_columns) == len(
                    instance_values[:len(left_side_columns)])  # excluding the image & designer
                for key, value in zip(left_side_columns, instance_values[:len(left_side_columns)]):
                    if key == IMAGE:
                        input_k = gr.Image(value=wmtis[index]["image"], label=f"Image {emoji_to_label[key]}")
                    else:
                        label = key.capitalize().replace("_", " ")
                        input_k = gr.Textbox(value=value, label=f"{label} {emoji_to_label[key]}")
                    inputs_left.append(input_k)
            with gr.Column():
                text_inputs_right = []
                assert len(right_side_columns) == len(instance_values[len(left_side_columns):]) # excluding the image & designer
                for key, value in zip(right_side_columns, instance_values[len(left_side_columns):]):
                    label = key.capitalize().replace("_", " ")
                    text_input_k = gr.Textbox(value=value, label=f"{label} {emoji_to_label[key]}")
                    text_inputs_right.append(text_input_k)

    slider.change(func, inputs=[slider], outputs=inputs_left + text_inputs_right)

demo.launch()