Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| from datasets import load_dataset | |
| import json | |
| import random | |
| from datetime import datetime | |
| import os | |
| from PIL import Image | |
| import io | |
| import numpy as np | |
| # Get access token from environment | |
| access_token = os.environ.get("HUGGINGFACE_TOKEN") | |
| class DatasetViewer: | |
| def __init__(self): | |
| self.dataset = None | |
| self.dataset_size = 0 | |
| self.last_refresh_time = None | |
| self.max_display_size = (800, 600) # Maximum width and height for displayed images | |
| self.load_dataset() | |
| def resize_image(self, image): | |
| """Resize image keeping aspect ratio with a maximum size constraint""" | |
| if isinstance(image, np.ndarray): | |
| # Convert numpy array to PIL Image | |
| image = Image.fromarray(image) | |
| elif isinstance(image, bytes): | |
| # Convert bytes to PIL Image | |
| image = Image.open(io.BytesIO(image)) | |
| # Calculate scaling factor to fit within max dimensions | |
| width_ratio = self.max_display_size[0] / image.width | |
| height_ratio = self.max_display_size[1] / image.height | |
| scale_factor = min(width_ratio, height_ratio) | |
| # Only resize if image is larger than max dimensions | |
| if scale_factor < 1: | |
| new_width = int(image.width * scale_factor) | |
| new_height = int(image.height * scale_factor) | |
| image = image.resize((new_width, new_height), Image.Resampling.LANCZOS) | |
| # Convert back to numpy array for gradio | |
| return np.array(image) | |
| def load_dataset(self): | |
| """Load the complete dataset into memory""" | |
| # Load the full dataset (non-streaming) | |
| self.dataset = load_dataset( | |
| "taesiri/PhotoshopRequest-DailyDump-January-2025-RandomSample", | |
| split="train", | |
| token=access_token | |
| ) | |
| self.dataset_size = len(self.dataset) | |
| self.last_refresh_time = datetime.now() | |
| def get_next_samples(self, num_samples=5): | |
| """Get random samples from the dataset""" | |
| # Generate random indices | |
| indices = random.sample(range(self.dataset_size), min(num_samples, self.dataset_size)) | |
| results = [] | |
| for idx in indices: | |
| sample = self.dataset[idx] | |
| # Get post information | |
| post_id = sample["post_id"] | |
| title = sample["title"] | |
| reddit_url = f"https://www.reddit.com/r/PhotoshopRequest/comments/{post_id}" | |
| # Extract selftext if available | |
| selftext = "" | |
| try: | |
| selftext = json.loads(sample["json_data"])["post"]["selftext"] | |
| except: | |
| print(f"No selftext found for post {post_id}") | |
| # Create markdown text | |
| markdown_text = f"# {title}\n\n{selftext}\n\n[View post on r/PhotoshopRequest]({reddit_url})" | |
| # Append the triple (post_info, source_image, edited_image) | |
| results.append(markdown_text) | |
| # Resize images before adding to results | |
| source_image = self.resize_image(sample["source_image"]) | |
| edited_image = self.resize_image(sample["edited_image"]) | |
| results.append(source_image) | |
| results.append(edited_image) | |
| return tuple(results) | |
| def get_info(self): | |
| """Return dataset information""" | |
| return f""" | |
| <div style="text-align: center;"> | |
| <hr> | |
| Dataset Size: {self.dataset_size} items<br> | |
| Last Refreshed: {self.last_refresh_time.strftime('%Y-%m-%d %H:%M:%S UTC')} | |
| </div> | |
| """ | |
| def create_interface(): | |
| viewer = DatasetViewer() | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# PhotoshopRequest Dataset Viewer") | |
| gr.Markdown(""" | |
| This is a viewer for the PhotoshopRequest dataset. Each sample shows a Photoshop editing request post. | |
| Click the 'Show New Samples' button to see **5 random samples** from the dataset. | |
| **Layout**: For each sample, you'll see: | |
| 1. The post title and description | |
| 2. The source image (left) and edited result (right) | |
| """) | |
| # Create 5 sets of outputs | |
| outputs = [] | |
| for i in range(5): | |
| post_info = gr.Markdown() | |
| outputs.append(post_info) | |
| with gr.Row(): | |
| source = gr.Image(label=f"Source Image {i+1}") | |
| edited = gr.Image(label=f"Edited Image {i+1}") | |
| outputs.extend([source, edited]) | |
| sample_button = gr.Button("Show New Samples") | |
| info_md = gr.Markdown() | |
| # Set up event handlers | |
| sample_button.click( | |
| viewer.get_next_samples, | |
| outputs=outputs | |
| ).then( | |
| viewer.get_info, | |
| outputs=[info_md] | |
| ) | |
| return demo | |
| if __name__ == "__main__": | |
| demo = create_interface() | |
| demo.launch() |