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import gradio as gr | |
from datasets import load_dataset | |
import random | |
import math | |
from datasets import load_dataset | |
import gradio as gr | |
import os | |
mydataset_private = load_dataset("glitchbench/GlitchBench")["validation"] | |
dataset_size = len(mydataset_private) | |
GRID_SIZE = (2, 2) | |
def get_item_data(image_index): | |
item = mydataset_private[image_index] | |
return item | |
def show_random_samples(): | |
total = GRID_SIZE[0] * GRID_SIZE[1] | |
random_indexes = random.sample(range(dataset_size), total) | |
all_examples = [get_item_data(index) for index in random_indexes] | |
all_inputs_left_right = [] | |
for example_idx, example in enumerate(all_examples): | |
all_inputs_left_right.append(example["image"]) | |
all_inputs_left_right.append(example["source"]) | |
all_inputs_left_right.append(example["glitch-type"]) | |
all_inputs_left_right.append(example["reddit"]) | |
all_inputs_left_right.append("Secrect") | |
return all_inputs_left_right | |
def make_grid(grid_size): | |
list_of_components = [] | |
with gr.Row(): | |
for row_counter in range(grid_size[0]): | |
with gr.Column(): | |
for col_counter in range(grid_size[1]): | |
item_image = gr.Image() | |
with gr.Accordion("Click for details", open=False): | |
item_glitch_source = gr.Textbox(label="Glitch Source") | |
item_reddit = gr.Textbox(label="Glitch Type") | |
item_id = gr.Textbox(label="Reddit ID") | |
item_description = gr.Textbox(label="Description") | |
list_of_components.append(item_image) | |
list_of_components.append(item_glitch_source) | |
list_of_components.append(item_reddit) | |
list_of_components.append(item_id) | |
list_of_components.append(item_description) | |
return list_of_components | |
with gr.Blocks(title="GltichBench") as browser: | |
gr.Markdown("## GlitchBench dataset explorer") | |
with gr.Column(): | |
random_btn = gr.Button("Random Sample") | |
with gr.Row(): | |
grid = make_grid(GRID_SIZE) | |
random_btn.click(show_random_samples, inputs=[], outputs=[*grid]) | |
browser.launch() | |