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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: 'πΌοΈ, π·, π', IMAGE_DESIGNER: 'π¨, π§βπ¨, π»', DESIGNER_EXPLANATION: 'π‘, π€, π§βπ¨', CROWD_CAPTIONS: 'π₯, π¬, π', CROWD_EXPLANATIONS: 'π₯, π‘, π€', CROWD_UNDERSPECIFIED_CAPTIONS: 'π₯, π¬, π', | |
QA: 'β, π€, π‘', IMAGE_ID: 'π, π, πΎ'} | |
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() | |