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| import math | |
| from datasets import load_dataset | |
| import gradio as gr | |
| import os | |
| # auth_token = os.environ.get("auth_token") | |
| auth_token = os.environ.get("HF_TOKEN") | |
| Visual_Riddles = load_dataset("nitzanguetta/Visual_Riddles", token=auth_token, trust_remote_code=True)['test'].shuffle() | |
| # print(f"Loaded WHOOPS!, first example:") | |
| # print(whoops[0]) | |
| dataset_size = len(Visual_Riddles) | |
| IMAGE = 'Image' | |
| QUESTION = 'Question' | |
| ANSWER = "Answer" | |
| CAPTION = "Image caption" | |
| PROMPT = "Prompt" | |
| MODEL_NAME = "Model name" | |
| HINT = "Hint" | |
| ATTRIBUTION = "Attribution" | |
| DLI = "Difficulty Level Index" | |
| CATEGORY = "Category" | |
| DESIGNER = "Designer" | |
| left_side_columns = [IMAGE] | |
| right_side_columns = [x for x in Visual_Riddles.features.keys() if x not in left_side_columns] | |
| right_side_columns.remove('Image file name') | |
| # right_side_columns.remove('Question') | |
| # enumerate_cols = [CROWD_CAPTIONS, CROWD_EXPLANATIONS, CROWD_UNDERSPECIFIED_CAPTIONS] | |
| emoji_to_label = {IMAGE: '🎨, 🧑🎨, 💻', ANSWER: '💡, 🤔, 🧑🎨', QUESTION: '❓, 🤔, 💡', CATEGORY: '🤔, 📚, 💡', | |
| CAPTION: '📝, 👌, 💬', PROMPT: '📝, 💻', MODEL_NAME: '🎨, 💻', HINT:'🤔, 🔍', | |
| ATTRIBUTION: '🔍, 📄', DLI:"🌡️, 🤔, 🎯", DESIGNER:"🧑🎨"} | |
| # 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 = [Visual_Riddles[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): | |
| # with gr.Accordion(example[QUESTION], 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("_", " ") | |
| num_lines = max(1, len(value) // 50 + (len(value) % 50 > 0)) # Assuming ~50 chars per line | |
| text_input_k = gr.Textbox(value=value, label=f"{label} {emoji_to_label[key]}", lines=num_lines) | |
| text_inputs_right.append(text_input_k) | |
| return inputs_left, text_inputs_right | |
| with demo: | |
| gr.Markdown("# Slide to iterate Visual Riddles") | |
| 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 = [Visual_Riddles[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() | |
| credentials = [ | |
| ("ViRi", "6JuneNeurIPS") | |
| ] | |
| # Launch the interface with password protection | |
| demo.launch(auth=credentials) | |
| # import math | |
| # from datasets import load_dataset | |
| # import gradio as gr | |
| # import os | |
| # | |
| # # Set up environment variables and load dataset | |
| # auth_token = os.environ.get("HF_TOKEN") | |
| # Visual_Riddles = load_dataset("nitzanguetta/Visual_Riddles", token=auth_token, trust_remote_code=True)['test'] | |
| # dataset_size = len(Visual_Riddles) | |
| # | |
| # # Define attributes | |
| # IMAGE = 'Image' | |
| # QUESTION = 'Question' | |
| # ANSWER = "Answer" | |
| # CAPTION = "Image caption" | |
| # PROMPT = "Prompt" | |
| # MODEL_NAME = "Model name" | |
| # HINT = "Hint" | |
| # ATTRIBUTION = "Attribution" | |
| # DLI = "Difficulty Level Index" | |
| # CATEGORY = "Category" | |
| # DESIGNER = "Designer" | |
| # | |
| # left_side_columns = [IMAGE] | |
| # right_side_columns = [x for x in Visual_Riddles.features.keys() if x not in left_side_columns] | |
| # right_side_columns.remove('Image file name') | |
| # | |
| # emoji_to_label = { | |
| # IMAGE: '🎨, 🧑🎨, 💻', ANSWER: '💡, 🤔, 🧑🎨', QUESTION: '❓, 🤔, 💡', CATEGORY: '🤔, 📚, 💡', | |
| # CAPTION: '📝, 👌, 💬', PROMPT: '📝, 💻', MODEL_NAME: '🎨, 💻', HINT:'🤔, 🔍', | |
| # ATTRIBUTION: '🔍, 📄', DLI:"🌡️, 🤔, 🎯", DESIGNER:"🧑🎨" | |
| # } | |
| # | |
| # batch_size = 8 | |
| # target_size = (1024, 1024) | |
| # | |
| # def func(index): | |
| # start_index = index * batch_size | |
| # end_index = start_index + batch_size | |
| # all_examples = [Visual_Riddles[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 | |
| # | |
| # # Define functions to handle data and interface | |
| # def get_instance_values(example): | |
| # values = [] | |
| # for k in left_side_columns + right_side_columns: | |
| # if k == IMAGE: | |
| # value = example["Image"].resize(target_size) | |
| # else: | |
| # value = example[k] | |
| # values.append(value) | |
| # return values | |
| # | |
| # def get_col(example): | |
| # instance_values = get_instance_values(example) | |
| # inputs_left, text_inputs_right = [], [] | |
| # with gr.Column() as col: | |
| # 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) | |
| # 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): | |
| # for key, value in zip(right_side_columns, instance_values[len(left_side_columns):]): | |
| # label = key.capitalize().replace("_", " ") | |
| # num_lines = max(1, len(value) // 50 + (len(value) % 50 > 0)) | |
| # text_input_k = gr.Textbox(value=value, label=f"{label} {emoji_to_label[key]}", lines=num_lines) | |
| # text_inputs_right.append(text_input_k) | |
| # return inputs_left, text_inputs_right | |
| # | |
| # # Create the Gradio Blocks interface | |
| # with gr.Blocks() as demo: | |
| # with gr.Row(): | |
| # gr.Markdown("# Visual Riddles Explorer") | |
| # with gr.Column(): | |
| # num_batches = math.ceil(dataset_size / batch_size) | |
| # slider = gr.Slider(minimum=0, maximum=num_batches - 1, step=1, label=f'Page (out of {num_batches})') | |
| # slider.change(lambda x: get_col(Visual_Riddles[x * batch_size]), inputs=[slider], outputs=[gr.Row()]) | |
| # | |
| # # Define credentials for authentication | |
| # credentials = [ | |
| # ("user", "Aa123"), | |
| # ("username2", "password2") | |
| # ] | |
| # | |
| # # Launch the interface with password protection | |
| # demo.launch(auth=credentials) | |