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import math
from datasets import load_dataset
import gradio as gr
import os
auth_token = os.environ.get("auth_token")
whoops = load_dataset("nlphuji/whoops", use_auth_token=auth_token)['test']
# print(f"Loaded WHOOPS!, first example:")
# print(whoops[0])
dataset_size = len(whoops)
IMAGE = 'image'
IMAGE_DESIGNER = 'image_designer'
DESIGNER_EXPLANATION = 'designer_explanation'
CROWD_CAPTIONS = 'crowd_captions'
CROWD_EXPLANATIONS = 'crowd_explanations'
CROWD_UNDERSPECIFIED_CAPTIONS = 'crowd_underspecified_captions'
QA = 'question_answering_pairs'
IMAGE_ID = 'image_id'
SELECTED_CAPTION = 'selected_caption'
COMMONSENSE_CATEGORY = 'commonsense_category'
left_side_columns = [IMAGE]
right_side_columns = [x for x in whoops.features.keys() if x not in left_side_columns]
enumerate_cols = [CROWD_CAPTIONS, CROWD_EXPLANATIONS, CROWD_UNDERSPECIFIED_CAPTIONS]
emoji_to_label = {IMAGE_DESIGNER: '🎨, 🧑🎨, 💻', DESIGNER_EXPLANATION: '💡, 🤔, 🧑🎨',
CROWD_CAPTIONS: '👥, 💬, 📝', CROWD_EXPLANATIONS: '👥, 💡, 🤔', CROWD_UNDERSPECIFIED_CAPTIONS: '👥, 💬, 👎',
QA: '❓, 🤔, 💡', IMAGE_ID: '🔍, 📄, 💾', COMMONSENSE_CATEGORY: '🤔, 📚, 💡', SELECTED_CAPTION: '📝, 👌, 💬'}
# batch_size = 16
batch_size = 8
target_size = (1024, 1024)
def func(index):
start_index = index * batch_size
end_index = start_index + batch_size
all_examples = [whoops[index] for index in list(range(start_index, end_index))]
values_lst = []
for example_idx, example in enumerate(all_examples):
values = get_instance_values(example)
values_lst += values
return values_lst
def get_instance_values(example):
values = []
for k in left_side_columns + right_side_columns:
if k == IMAGE:
value = example["image"].resize(target_size)
elif 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()
def get_col():
instance_values = get_instance_values(example)
with gr.Column():
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:
img_resized = example["image"].resize(target_size)
input_k = gr.Image(value=img_resized, label=example['commonsense_category'])
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.Accordion("Click for details", open=False):
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)
return inputs_left, text_inputs_right
with demo:
gr.Markdown("# Slide to iterate WHOOPS!")
with gr.Column():
num_batches = math.ceil(dataset_size / batch_size)
slider = gr.Slider(minimum=0, maximum=num_batches, step=1, label=f'Page (out of {num_batches})')
with gr.Row():
index = slider.value
start_index = 0 * batch_size
end_index = start_index + batch_size
all_examples = [whoops[index] for index in list(range(start_index, end_index))]
all_inputs_left_right = []
for example_idx, example in enumerate(all_examples):
inputs_left, text_inputs_right = get_col()
inputs_left_right = inputs_left + text_inputs_right
all_inputs_left_right += inputs_left_right
slider.change(func, inputs=[slider], outputs=all_inputs_left_right)
demo.launch()
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