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| import random | |
| import gradio as gr | |
| from datasets import load_dataset | |
| whoops = load_dataset("nlphuji/whoops")['test'] | |
| print(f"Loaded WMTIS, first example:") | |
| print(whoops[0]) | |
| dataset_size = len(whoops) | |
| print(f"all dataset size: {dataset_size}") | |
| IMAGE = 'image' | |
| IMAGE_DESIGNER = 'image_designer' | |
| DESIGNER_EXPLANATION = 'designer_explanation' | |
| CROWD_CAPTIONS = 'crowd_captions' | |
| CROWD_EXPLANATIONS = 'crowd_explanations' | |
| CROWD_UNDERSPECIFIED_CAPTIONS = 'crowd_underspecified_captions' | |
| SELECTED_CAPTION = 'selected_caption' | |
| COMMONSENSE_CATEGORY = 'commonsense_category' | |
| QA = 'question_answering_pairs' | |
| IMAGE_ID = 'image_id' | |
| left_side_columns = [IMAGE] | |
| # right_side_columns = [x for x in whoops.features.keys() if x not in left_side_columns and x not in [QA]] | |
| 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: '📝, 👌, 💬'} | |
| target_size = (1024, 1024) | |
| MAX_LINES = 30 | |
| 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)]) | |
| def plot_image(index): | |
| example = whoops_sample[index] | |
| instance_values = get_instance_values(example) | |
| 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 = whoops_sample[index]["image"] | |
| img_resized = img.resize(target_size) | |
| gr.Image(value=img_resized, label=whoops_sample[index]['commonsense_category']) | |
| else: | |
| label = key.capitalize().replace("_", " ") | |
| gr.Textbox(value=value, label=f"{label} {emoji_to_label[key]}") | |
| with gr.Accordion("Click for details", open=False): | |
| 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("_", " ") | |
| gr.Textbox(value=value, label=f"{label} {emoji_to_label[key]}", max_lines=MAX_LINES) | |
| columns_number = 4 | |
| # rows_number = int(dataset_size / columns_number) | |
| # rows_number = 25 | |
| rows_number = 20 | |
| whoops_sample = whoops.shuffle().select(range(0, columns_number * rows_number)) | |
| index = 0 | |
| with gr.Blocks() as demo: | |
| gr.Markdown(f"# WHOOPS! Dataset Explorer") | |
| for row_num in range(0, rows_number): | |
| with gr.Row(): | |
| for col_num in range(0, columns_number): | |
| with gr.Column(): | |
| plot_image(index) | |
| index += 1 | |
| demo.launch() | |