app.py

import os
from pathlib import Path

import gradio as gr
import requests
from fastapi import BackgroundTasks, Response, status
from huggingface_hub import WebhookPayload, WebhooksServer
from huggingface_hub.utils import build_hf_headers, get_session

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

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']
# HF_TOKEN = os.environ["HF_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 [reddit-tools-HF/dataset-creator-reddit-bestofredditorupdates](https://huggingface.co/datasets/reddit-tools-HF/dataset-creator-reddit-bestofredditorupdates).
2. It then takes the updates from that dataset and get embeddings by making leveraging [reddit-tools-HF/nomic-embeddings](https://huggingface.co/spaces/reddit-tools-HF/nomic-embeddings)
    - [reddit-tools-HF/nomic-embeddings](https://huggingface.co/spaces/reddit-tools-HF/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 [reddit-tools-HF/reddit-bestofredditorupdates-processed](https://huggingface.co/datasets/reddit-tools-HF/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/reddit-tools-HF/processing-bestofredditorupdates/blob/main/src/build_nomic.py)
"""

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 handle_repository_changes(payload: WebhookPayload, task_queue: BackgroundTasks):
    if not payload.event.scope.startswith("repo"):
        return Response("No task scheduled", status_code=status.HTTP_200_OK)
    # Only run if change is on main branch
    try:
        if payload.updatedRefs[0].ref != 'refs/heads/main':
            response_content = "No task scheduled: Change not on main branch"
            logger.info(response_content)
            return Response(response_content, status_code=status.HTTP_200_OK)
    except:
        response_content = "No task scheduled"
        logger.info(response_content)
        return Response(response_content, status_code=status.HTTP_200_OK)

    # No need to run for README only updates
    try:
        commit_files_url = f"""{payload.repo.url.api}/compare/{payload.updatedRefs[0].oldSha}..{payload.updatedRefs[0].newSha}?raw=true"""
        response_text = get_session().get(commit_files_url, headers=build_hf_headers()).text
        logger.info(f"Git Compare URl: {commit_files_url}")

        # Splitting the output into lines
        file_lines = response_text.split('\n')

        # Filtering the lines to find file changes
        changed_files = [line.split('\t')[-1] for line in file_lines if line.strip()]
        logger.info(f"Changed files: {changed_files}")

        # Checking if only README.md has been changed
        if all('README.md' in file for file in changed_files):
            response_content = "No task scheduled: its a README only update."
            logger.info(response_content)
            return Response(response_content, status_code=status.HTTP_200_OK)
    except Exception as e:
        logger.error(f"{str(e)}")
        response_content = "Unexpected issue :'("
        logger.info(response_content)
        return Response(response_content, status_code=status.HTTP_501_NOT_IMPLEMENTED)

    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)
    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)