app.py

import gradio as gr
from datasets import load_dataset
from PIL import Image
import io
import time
import os
from datetime import datetime, timedelta
import json

access_token = os.environ.get("HUGGINGFACE_TOKEN")

# Global variables
dataset = None
dataset_size = "Unknown"
last_refresh_time = None
REFRESH_INTERVAL = timedelta(hours=24)


def load_and_prepare_dataset():
    global dataset, dataset_size, last_refresh_time

    dataset = load_dataset(
        "taesiri/PhotoshopRequest-DailyDump",
        split="train",
        streaming=True,
        token=access_token,
    )

    # Get dataset info
    dataset_info = dataset.info
    dataset_size = (
        dataset_info.splits["train"].num_examples
        if dataset_info.splits.get("train")
        else "Unknown"
    )

    last_refresh_time = datetime.now()


def check_and_refresh_dataset():
    global last_refresh_time
    current_time = datetime.now()
    if (
        last_refresh_time is None
        or (current_time - last_refresh_time) >= REFRESH_INTERVAL
    ):
        load_and_prepare_dataset()


# Initial dataset load
load_and_prepare_dataset()

# Load and prepare the dataset
dataset = load_dataset(
    "taesiri/PhotoshopRequest-DailyDump",
    split="train",
    streaming=True,
    token=access_token,
)

# Get dataset info
dataset_info = dataset.info
dataset_size = (
    dataset_info.splits["train"].num_examples
    if dataset_info.splits.get("train")
    else "Unknown"
)

BUFFER_SIZE = 1
sample_iterator = None
sample_count = 0


def reshuffle_dataset():
    global sample_iterator, sample_count
    seed = int(time.time())  # Convert time to an integer
    shuffled_dataset = dataset.shuffle(seed=seed, buffer_size=BUFFER_SIZE)
    sample_iterator = iter(shuffled_dataset)
    sample_count = 0


reshuffle_dataset()  # Initial shuffle


def get_next_sample():
    check_and_refresh_dataset()
    global sample_count

    if sample_count >= BUFFER_SIZE:
        reshuffle_dataset()

    sample = next(sample_iterator)
    sample_count += 1
    print(sample)
    
    post_id = sample["post_id"]
    title = sample["title"]
    reddit_url = f"https://www.reddit.com/r/PhotoshopRequest/comments/{post_id}"

    selftext = ""
    try:
        selftext = json.loads(sample["json_data"])["post"]["selftext"]
    except:
        print("No selftext found")

    markdown_text = f"# {title}\n\n{selftext}\n\n[View post on r/PhotoshopRequest]({reddit_url})"

    return (
        markdown_text,
        sample["source_image"],
        sample["edited_image"],
    )


with gr.Blocks() as demo:
    gr.Markdown("# PhotoshopRequest Dataset Sampler")

    gr.Markdown(
        """
    This is a preview of the PhotoshopRequest dataset. Each sample represents a Photoshop editing request post.
    Click the 'Sample New Item' button to retrieve a random sample from the dataset.
    """
    )

    post_info = gr.Markdown()

    with gr.Row():
        source_image = gr.Image(label="Source Image")
        edited_image = gr.Image(label="Edited Image")

    sample_button = gr.Button("Sample New Item")

    info_md = gr.Markdown()

    def update_info():
        return f"""
    <div style="text-align: center;">
    <hr>
    Dataset Size: {dataset_size} items<br>
    Last Refreshed: {last_refresh_time.strftime('%Y-%m-%d %H:%M:%S UTC') if last_refresh_time else 'Unknown'}
    </div>
    """

    sample_button.click(
        get_next_sample, outputs=[post_info, source_image, edited_image]
    ).then(update_info, outputs=[info_md])

if __name__ == "__main__":
    demo.launch()