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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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whoops = load_dataset("nlphuji/whoops")['test'] |
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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 = 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(): |
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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)]) |
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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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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):]) |
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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() |
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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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