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(example): 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']) input_k = gr.Image(value=img_resized) 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(example) 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()