app.py

import os
from pathlib import Path

from fastapi import BackgroundTasks, Response, status
import gradio as gr
from huggingface_hub import WebhookPayload, WebhooksServer

from src.my_logger import setup_logger
from src.utilities import load_datasets, merge_and_update_datasets
from src.visualize_logs import log_file_to_html_string
from src.build_nomic import build_nomic
from src.readme_update import update_dataset_readme

proj_dir = Path(__name__).parent

logger = setup_logger(__name__)
logger.info("Starting Application...")

SUBREDDIT = os.environ["SUBREDDIT"]
USERNAME = os.environ["USERNAME"]
OG_DATASET = f"{USERNAME}/dataset-creator-reddit-{SUBREDDIT}"
PROCESSED_DATASET = os.environ['PROCESSED_DATASET']
HUGGINGFACE_AUTH_TOKEN = os.environ["HUGGINGFACE_AUTH_TOKEN"]
WEBHOOK_SECRET = os.getenv("HF_WEBHOOK_SECRET", 'secret')

intro_md = """
# Processing BORU
This is a space to visually search the subreddit [/r/bestofredditorupdates](https://www.reddit.com/r/BestofRedditorUpdates/). 
Have you ever been curious to search for stories that are similar to one of your favorites? This can help!

- Each dot represents a post (try clicking on one)
- Closer dots are similar in topic
- Use the filters on the left to help you narrow down what you are looking for
    - The lasso can help you search in a smaller range that you drag with your mouse
    - The filter can help you narrow by field, 
        - Filtering posts that are `CONCLUDED`
        - Filtering popular posts
        - Filtering by date
    - The search can help you look by keyword
    
Check out the original on [Nomic](https://atlas.nomic.ai/data/derek2/boru-subreddit-neural-search/map)
"""

details_md = """
# Details
## Creation Details
1. This space is triggered by a webhook for changes on [derek-thomas/dataset-creator-reddit-bestofredditorupdates](https://huggingface.co/datasets/derek-thomas/dataset-creator-reddit-bestofredditorupdates).
2. It then takes the updates from that dataset and get embeddings by making leveraging [derek-thomas/nomic-embeddings](https://huggingface.co/spaces/derek-thomas/nomic-embeddings)
    - [derek-thomas/nomic-embeddings](https://huggingface.co/spaces/derek-thomas/nomic-embeddings) is using [zero-spaces](https://huggingface.co/zero-gpu-explorers) a free GPU service to compute the model [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5)
    - Im calling this via [gradio_client](https://www.gradio.app/docs/client) which allows any space to be used as an API
3. The calculated embeddings are stored in this dataset [derek-thomas/reddit-bestofredditorupdates-processed](https://huggingface.co/datasets/derek-thomas/reddit-bestofredditorupdates-processed)
4. These get visualized by [nomic atlas](https://docs.nomic.ai/atlas/introduction/quick-start). You can see how I process it in [build_nomic.py](https://huggingface.co/spaces/derek-thomas/processing-bestofredditorupdates/blob/main/src/build_nomic.py)

## Todo
- Ignore the colors for now, I need to clean that up :) 
- I need to integrate with Nomic's semantic search
"""

url = "https://atlas.nomic.ai/data/derek2/boru-subreddit-neural-search/map"
html_str = f'<iframe src={url} style="border:none;height:1024px;width:100%" allow="clipboard-read; clipboard-write" title="Nomic Atlas">'

with gr.Blocks() as ui:
    with gr.Tab("Application"):
        gr.Markdown(intro_md)
        gr.HTML(html_str)
    with gr.Tab("Logs"):
        gr.Markdown("# Logs")
        output = gr.HTML(log_file_to_html_string, every=1)
    with gr.Tab("Details"):
        gr.Markdown(details_md)

app = WebhooksServer(ui=ui.queue(), webhook_secret=WEBHOOK_SECRET)


@app.add_webhook("/dataset_repo")
async def community(payload: WebhookPayload, task_queue: BackgroundTasks):
    if not payload.event.scope.startswith("repo"):
        return Response("No task scheduled", status_code=status.HTTP_200_OK)

    logger.info(f"Webhook received from {payload.repo.name} indicating a repo {payload.event.action}")
    task_queue.add_task(_process_webhook, payload=payload)
    return Response("Task scheduled.", status_code=status.HTTP_202_ACCEPTED)

def _process_webhook(payload: WebhookPayload):
    logger.info(f"Loading new dataset...")
    dataset, original_dataset = load_datasets()
    logger.info(f"Loaded new dataset")

    logger.info(f"Merging and Updating rows...")
    dataset, updated_row_count = merge_and_update_datasets(dataset, original_dataset)
    logger.info(f"Merged and Updated rows")

    # Push the augmented dataset to the Hugging Face hub
    logger.info(f"Pushing processed data to the Hugging Face Hub...")
    dataset.push_to_hub(PROCESSED_DATASET, token=HUGGINGFACE_AUTH_TOKEN)
    logger.info(f"Pushed processed data to the Hugging Face Hub")

    update_dataset_readme(dataset_name=PROCESSED_DATASET, subreddit=SUBREDDIT, new_rows=updated_row_count)
    logger.info(f"Updated README.")

    # Build Nomic
    logger.info(f"Building Nomic...")
    build_nomic(dataset=dataset)
    logger.info(f"Built Nomic")

    logger.info(f"Update from webhook completed!")

if __name__ == '__main__':
    app.launch(server_name="0.0.0.0", show_error=True, server_port=7860)
    # ui.queue().launch(server_name="0.0.0.0", show_error=True, server_port=7860)