import os
import gradio as gr
from datasets import load_dataset
val_data_files = {"val": "contextual_val.csv"}
contextual_val = load_dataset("ucla-contextual/contextual_val", data_files=val_data_files)
print(contextual_val)
data = contextual_val['val']
print(data[0])
df = data.to_pandas()
df['image'] = df['image_url'].apply(lambda x: ' ')
cols = list(df.columns)
cols.insert(0, cols.pop(cols.index('image')))
cols.insert(4, cols.pop(cols.index('image_url')))
df = df.reindex(columns=cols)
LINES_NUMBER = 20
def display_df():
df_images = df.head(LINES_NUMBER)
return df_images
def display_next(dataframe, end):
start = int(end or len(dataframe))
end = int(start) + int(LINES_NUMBER)
global df
if end >= len(df) - 1:
start = 0
end = LINES_NUMBER
df = df.sample(frac=1)
print(f"Shuffle")
df_images = df.iloc[start:end]
assert len(df_images) == LINES_NUMBER
return df_images, end
initial_dataframe = display_df()
# Gradio Blocks
with gr.Blocks() as demo:
gr.Markdown("