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Create app.py
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app.py
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import math
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from datasets import load_dataset
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import gradio as gr
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import os
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auth_token = os.environ.get("auth_token")
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whoops = load_dataset("nlphuji/whoops", use_auth_token=auth_token)['test']
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# print(f"Loaded WHOOPS!, first example:")
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# print(whoops[0])
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dataset_size = len(whoops)
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IMAGE = 'image'
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IMAGE_DESIGNER = 'image_designer'
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DESIGNER_EXPLANATION = 'designer_explanation'
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CROWD_CAPTIONS = 'crowd_captions'
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CROWD_EXPLANATIONS = 'crowd_explanations'
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CROWD_UNDERSPECIFIED_CAPTIONS = 'crowd_underspecified_captions'
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QA = 'question_answering_pairs'
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IMAGE_ID = 'image_id'
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SELECTED_CAPTION = 'selected_caption'
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COMMONSENSE_CATEGORY = 'commonsense_category'
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left_side_columns = [IMAGE]
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right_side_columns = [x for x in whoops.features.keys() if x not in left_side_columns]
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enumerate_cols = [CROWD_CAPTIONS, CROWD_EXPLANATIONS, CROWD_UNDERSPECIFIED_CAPTIONS]
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emoji_to_label = {IMAGE_DESIGNER: 'π¨, π§βπ¨, π»', DESIGNER_EXPLANATION: 'π‘, π€, π§βπ¨',
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CROWD_CAPTIONS: 'π₯, π¬, π', CROWD_EXPLANATIONS: 'π₯, π‘, π€', CROWD_UNDERSPECIFIED_CAPTIONS: 'π₯, π¬, π',
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QA: 'β, π€, π‘', IMAGE_ID: 'π, π, πΎ', COMMONSENSE_CATEGORY: 'π€, π, π‘', SELECTED_CAPTION: 'π, π, π¬'}
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# batch_size = 16
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batch_size = 8
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target_size = (1024, 1024)
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def func(index):
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start_index = index * batch_size
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end_index = start_index + batch_size
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all_examples = [whoops[index] for index in list(range(start_index, end_index))]
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values_lst = []
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for example_idx, example in enumerate(all_examples):
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values = get_instance_values(example)
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values_lst += values
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return values_lst
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def get_instance_values(example):
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values = []
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for k in left_side_columns + right_side_columns:
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if k == IMAGE:
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value = example["image"].resize(target_size)
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elif k in enumerate_cols:
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value = list_to_string(example[k])
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elif k == QA:
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qa_list = [f"Q: {x[0]} A: {x[1]}" for x in example[k]]
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value = list_to_string(qa_list)
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else:
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value = example[k]
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values.append(value)
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return values
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def list_to_string(lst):
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return '\n'.join(['{}. {}'.format(i+1, item) for i, item in enumerate(lst)])
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demo = gr.Blocks()
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def get_col(example):
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instance_values = get_instance_values(example)
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with gr.Column():
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inputs_left = []
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assert len(left_side_columns) == len(
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instance_values[:len(left_side_columns)]) # excluding the image & designer
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for key, value in zip(left_side_columns, instance_values[:len(left_side_columns)]):
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if key == IMAGE:
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img_resized = example["image"].resize(target_size)
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# input_k = gr.Image(value=img_resized, label=example['commonsense_category'])
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input_k = gr.Image(value=img_resized)
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else:
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label = key.capitalize().replace("_", " ")
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input_k = gr.Textbox(value=value, label=f"{label} {emoji_to_label[key]}")
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inputs_left.append(input_k)
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with gr.Accordion("Click for details", open=False):
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text_inputs_right = []
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assert len(right_side_columns) == len(
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instance_values[len(left_side_columns):]) # excluding the image & designer
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for key, value in zip(right_side_columns, instance_values[len(left_side_columns):]):
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label = key.capitalize().replace("_", " ")
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text_input_k = gr.Textbox(value=value, label=f"{label} {emoji_to_label[key]}")
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text_inputs_right.append(text_input_k)
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return inputs_left, text_inputs_right
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with demo:
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gr.Markdown("# Slide to iterate WHOOPS!")
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with gr.Column():
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num_batches = math.ceil(dataset_size / batch_size)
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slider = gr.Slider(minimum=0, maximum=num_batches, step=1, label=f'Page (out of {num_batches})')
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with gr.Row():
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index = slider.value
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start_index = 0 * batch_size
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end_index = start_index + batch_size
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all_examples = [whoops[index] for index in list(range(start_index, end_index))]
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all_inputs_left_right = []
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for example_idx, example in enumerate(all_examples):
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inputs_left, text_inputs_right = get_col(example)
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inputs_left_right = inputs_left + text_inputs_right
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all_inputs_left_right += inputs_left_right
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slider.change(func, inputs=[slider], outputs=all_inputs_left_right)
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demo.launch()
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