Spaces:
Runtime error
Runtime error
| from datasets import load_dataset | |
| import gradio as gr | |
| import os | |
| import random | |
| wmtis = load_dataset("nlphuji/wmtis")['test'] | |
| print(f"Loaded WMTIS, first example:") | |
| print(wmtis[0]) | |
| dataset_size = len(wmtis) - 1 | |
| IMAGE = 'image' | |
| IMAGE_DESIGNER = 'image_designer' | |
| DESIGNER_EXPLANATION = 'designer_explanation' | |
| CROWD_CAPTIONS = 'crowd_captions' | |
| CROWD_EXPLANATIONS = 'crowd_explanations' | |
| CROWD_UNDERSPECIFIED_CAPTIONS = 'crowd_underspecified_captions' | |
| # CROWD_NEGATIVE_EXPLANATIONS = 'crowd_negative_explanations' | |
| QA = 'question_answering_pairs' | |
| IMAGE_ID = 'image_id' | |
| left_side_columns = [IMAGE] | |
| # left_side_columns = [IMAGE, DESIGNER_EXPLANATION, IMAGE_DESIGNER, IMAGE_ID] | |
| right_side_columns = [x for x in wmtis.features.keys() if x not in left_side_columns and x not in [QA]] | |
| enumerate_cols = [CROWD_CAPTIONS, CROWD_EXPLANATIONS, CROWD_UNDERSPECIFIED_CAPTIONS] | |
| emoji_to_label = {IMAGE: '🖼️, 📷, 🌄', IMAGE_DESIGNER: '🎨, 🧑🎨, 💻', DESIGNER_EXPLANATION: '💡, 🤔, 🧑🎨', CROWD_CAPTIONS: '👥, 💬, 📝', CROWD_EXPLANATIONS: '👥, 💡, 🤔', CROWD_UNDERSPECIFIED_CAPTIONS: '👥, 💬, 👎', | |
| QA: '❓, 🤔, 💡', IMAGE_ID: '🔍, 📄, 💾'} | |
| def func(index): | |
| example = wmtis[index] | |
| values = get_instance_values(example) | |
| return values | |
| 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)]) | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown("# Slide to iterate WMTIS") | |
| with gr.Column(): | |
| slider = gr.Slider(minimum=0, maximum=dataset_size, step=1, label='index') | |
| with gr.Row(): | |
| # index = random.choice(range(0, dataset_size)) | |
| index = slider.label | |
| example = wmtis[index] | |
| instance_values = get_instance_values(example) | |
| with gr.Column(): | |
| # image_input = gr.Image(value=wmtis[index]["image"]) | |
| 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: | |
| input_k = gr.Image(value=wmtis[index]["image"], label=f"Image {emoji_to_label[key]}") | |
| 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.Column(): | |
| 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) | |
| slider.change(func, inputs=[slider], outputs=inputs_left + text_inputs_right) | |
| demo.launch() | |