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)