app.py

# TODO
# Remove duplication in code used to generate markdown
# periodically update models to check all still valid and public

import os
import re
import sys
from functools import lru_cache
from pathlib import Path
from typing import Dict, List, Set, Union

import gradio as gr
from apscheduler.schedulers.background import BackgroundScheduler
from apscheduler.triggers.cron import CronTrigger
from cachetools import TTLCache, cached
from diskcache import Cache
from dotenv import load_dotenv
from huggingface_hub import (
    HfApi,
    comment_discussion,
    create_discussion,
    dataset_info,
    get_repo_discussions,
)
from huggingface_hub.utils import HFValidationError, RepositoryNotFoundError
from sqlitedict import SqliteDict
from toolz import concat, count, unique
from tqdm.auto import tqdm
from tqdm.contrib.concurrent import thread_map

local = bool(sys.platform.startswith("darwin"))
cache_location = "cache/" if local else "/data/cache"

save_dir = "test_data" if local else "/data/"
Path(save_dir).mkdir(parents=True, exist_ok=True)
cache = Cache(cache_location)
load_dotenv()
user_agent = os.getenv("USER_AGENT")
HF_TOKEN = os.getenv("HF_TOKEN")
REPO = "librarian-bots/dataset-to-model-monitor"  # where issues land
AUTHOR = "librarian-bot"  # who makes the issues
hf_api = HfApi(user_agent=user_agent)

ten_min_cache = TTLCache(maxsize=5_000, ttl=600)


@cached(cache=ten_min_cache)
def get_datasets_for_user(username: str) -> List[str]:
    datasets = hf_api.list_datasets(author=username)
    datasets = (dataset.id for dataset in datasets)
    return datasets


@cached(cache=ten_min_cache)
def get_models_for_dataset(dataset_id):
    results = list(iter(hf_api.list_models(filter=f"dataset:{dataset_id}")))
    if results:
        results = list({result.id for result in results})
    return {dataset_id: results}


def generate_dataset_model_map(
    dataset_ids: List[str],
) -> dict[str, dict[str, List[str]]]:
    results = thread_map(get_models_for_dataset, dataset_ids)
    results = {key: value for d in results for key, value in d.items()}
    return results


def maybe_update_datasets_to_model_map(dataset_id):
    with SqliteDict(f"{save_dir}/models_to_dataset.sqlite") as dataset_to_model_map_db:
        if dataset_id not in dataset_to_model_map_db:
            dataset_to_model_map_db[dataset_id] = list(
                get_models_for_dataset(dataset_id)[dataset_id]
            )
            dataset_to_model_map_db.commit()
            return len(dataset_to_model_map_db)
        return False


def datasets_tracked_by_user(username):
    with SqliteDict(
        f"{save_dir}/tracked_dataset_to_users.sqlite"
    ) as tracked_dataset_to_users_db:
        return [
            dataset
            for dataset, users in tracked_dataset_to_users_db.items()
            if username in users
        ]


def update_tracked_dataset_to_users(dataset_id: str, username: str):
    with SqliteDict(
        f"{save_dir}/tracked_dataset_to_users.sqlite",
    ) as tracked_dataset_to_users_db:
        if dataset_id in tracked_dataset_to_users_db:
            # check if user already tracking dataset
            if username not in tracked_dataset_to_users_db[dataset_id]:
                users_for_dataset = tracked_dataset_to_users_db[dataset_id]
                users_for_dataset.append(username)
                tracked_dataset_to_users_db[dataset_id] = list(set(users_for_dataset))
                tracked_dataset_to_users_db.commit()
        else:
            tracked_dataset_to_users_db[dataset_id] = [username]
            tracked_dataset_to_users_db.commit()
    return datasets_tracked_by_user(username)


HUB_ORG_OR_USERNAME_GLOB_PATTERN = re.compile(r"^([^/]+)(?=/)")


@lru_cache(maxsize=128)
def match_org_user_glob_pattern(hub_id):
    if match := re.match(HUB_ORG_OR_USERNAME_GLOB_PATTERN, hub_id):
        return match[1]
    else:
        return None


@cached(cache=TTLCache(maxsize=100, ttl=60))
def grab_dataset_ids_for_user_or_org(hub_id: str) -> List[str]:
    datasets_for_org = hf_api.list_datasets(author=hub_id)
    datasets_for_org = (
        dataset for dataset in datasets_for_org if dataset.private is False
    )
    return [dataset.id for dataset in datasets_for_org]


@cached(cache=TTLCache(maxsize=100, ttl=60))
def parse_hub_id_entry(hub_id: str) -> Union[str, List[str]]:
    if match := match_org_user_glob_pattern(hub_id):
        return grab_dataset_ids_for_user_or_org(match), match
    try:
        dataset_info(hub_id)
        return hub_id, match
    except HFValidationError as e:
        raise gr.Error(f"Invalid format for Hugging Face Hub dataset ID. {e}") from e
    except RepositoryNotFoundError as e:
        raise gr.Error("Invalid Hugging Face Hub dataset ID") from e


def remove_user_from_tracking_datasets(dataset_id, profile: gr.OAuthProfile | None):
    if not profile and not local:
        return "You must be logged in to remove a dataset"
    username = profile.preferred_username
    dataset_id, match = parse_hub_id_entry(dataset_id)
    if isinstance(dataset_id, str):
        return _remove_user_from_tracking_datasets(dataset_id, username)
    if isinstance(dataset_id, list):
        [
            _remove_user_from_tracking_datasets(dataset, username)
            for dataset in dataset_id
        ]
        return f"Stopped tracking datasets for username or org: {match}"


def _remove_user_from_tracking_datasets(dataset_id: str, username):
    with SqliteDict(
        f"{save_dir}/tracked_dataset_to_users.sqlite"
    ) as tracked_dataset_to_users_db:
        users = tracked_dataset_to_users_db.get(dataset_id)
        if users is None:
            return "Dataset not being tracked"
        try:
            users.remove(username)
        except ValueError:
            return "No longer tracking dataset"
        tracked_dataset_to_users_db[dataset_id] = users
        if len(users) < 1:
            del tracked_dataset_to_users_db[dataset_id]
            with SqliteDict(
                f"{save_dir}/models_to_dataset.sqlite"
            ) as dataset_to_models_db:
                del dataset_to_models_db[dataset_id]
                dataset_to_models_db.commit()
        tracked_dataset_to_users_db.commit()
        return "Dataset no longer being tracked"


def user_unsubscribe_all(username):
    datasets_tracked = datasets_tracked_by_user(username)
    for dataset_id in datasets_tracked:
        remove_user_from_tracking_datasets(username, dataset_id)
    assert len(datasets_tracked_by_user(username)) == 0
    return f"Unsubscribed from {len(datasets_tracked)} datasets"


def user_update(hub_id, profile: gr.OAuthProfile | None):
    if not profile and not local:
        return "Please login to track a dataset"
    username = profile.preferred_username
    hub_id, match = parse_hub_id_entry(hub_id)
    if isinstance(hub_id, str):
        return _user_update(hub_id, username)
    else:
        return glob_update_tracked_datasets(hub_id, username, match)


def glob_update_tracked_datasets(hub_ids, username, match):
    for id_ in tqdm(hub_ids):
        _user_update(id_, username)
    response = "## Dataset tracking summary \n\n"
    response += (
        f"All datasets under the user or organization: {match} are being tracked \n\n"
    )
    tracked_datasets = datasets_tracked_by_user(username)
    response += (
        "You are currently tracking whether new models have been trained on"
        f" {len(tracked_datasets)} datasets.\n\n"
    )
    if tracked_datasets:
        response += "### Datasets being tracked \n\n"
        response += (
            "You are currently monitoring whether new models have been trained on the"
            " following datasets:\n"
        )
        for dataset in tracked_datasets:
            response += f"- [{dataset}](https://huggingface.co/datasets/{dataset})\n"
    return response


def _user_update(hub_id: str, username: str) -> str:
    """Update the user's tracked datasets and return a response string."""
    response = ""
    if number_datasets_being_tracked := maybe_update_datasets_to_model_map(hub_id):
        response += (
            "New dataset being tracked! Now tracking"
            f" {number_datasets_being_tracked} datasets \n\n"
        )
    if not number_datasets_being_tracked:
        response += f"Dataset {hub_id} is already being tracked. \n\n"
    datasets_tracked_by_user = update_tracked_dataset_to_users(hub_id, username)
    response += (
        "You are currently whether new models have been trained on"
        f" {len(datasets_tracked_by_user)} datasets."
    )
    if datasets_tracked_by_user:
        response += (
            "\nYou are currently monitoring whether new models have been trained on the"
            " following datasets:\n"
        )
        for dataset in datasets_tracked_by_user:
            response += f"- [{dataset}](https://huggingface.co/datasets/{dataset})\n"
    else:
        response += "You are not currently tracking any datasets."
    return response


def check_for_new_models_for_dataset_and_update() -> Dict[str, Set[str]]:
    # if not Path(f"{save_dir}/models_to_dataset.json").is_file():
    with SqliteDict(f"{save_dir}/models_to_dataset.sqlite") as old_results_db:
        dataset_ids = list(old_results_db.keys())
        new_results = generate_dataset_model_map(dataset_ids)
        models_to_notify_about = {
            dataset_id: set(models).difference(set(old_results_db[dataset_id]))
            for dataset_id, models in new_results.items()
            if len(models) > len(old_results_db[dataset_id])
        }
        for dataset_id, models in new_results.items():
            old_results_db[dataset_id] = models
        old_results_db.commit()
        return models_to_notify_about


def get_repo_discussion_by_author_and_type(
    repo, author, token, repo_type="space", include_prs=False
):
    discussions = get_repo_discussions(repo, repo_type=repo_type, token=token)
    for discussion in discussions:
        if discussion.author == author:
            if not include_prs and discussion.is_pull_request:
                continue
            yield discussion


def create_discussion_text_body(dataset_id, new_models, users_to_notify):
    usernames = [f"@{username}" for username in users_to_notify]
    usernames_string = ", ".join(usernames)
    dataset_id_markdown_url = (
        f"[{dataset_id}](https://huggingface.co/datasets/{dataset_id})"
    )
    description = (
        f"Hey {usernames_string}! Librarian bot found new models trained on the"
        f" {dataset_id_markdown_url} dataset!\n\n"
    )
    description += f"New model trained on {dataset_id}:\n"
    markdown_items = [
        f"- {hub_id_to_huggingface_hub_url_markdown(model)}" for model in new_models
    ]
    markdown_list = "\n".join(markdown_items)
    description += markdown_list
    return description


def maybe_create_discussion(
    repo: str,
    dataset_id: str,
    new_models: Union[List, str],
    users_to_notify: List[str],
    author: str,
    token: str,
):
    title = f"Discussion tracking new models trained on {dataset_id}"
    discussions = get_repo_discussion_by_author_and_type(repo, author, HF_TOKEN)
    if discussions_for_dataset := next(
        (discussion for discussion in discussions if title == discussion.title),
        None,
    ):
        discussion_id = discussions_for_dataset.num
        description = create_discussion_text_body(
            dataset_id, new_models, users_to_notify
        )
        comment_discussion(
            repo, discussion_id, description, token=token, repo_type="space"
        )
    else:
        description = create_discussion_text_body(
            dataset_id, new_models, users_to_notify
        )
        create_discussion(
            repo,
            title,
            token=token,
            description=description,
            repo_type="space",
        )


def hub_id_to_huggingface_hub_url_markdown(hub_id: str) -> str:
    return f"[{hub_id}](https://huggingface.co/{hub_id})"


def notify_about_new_models():
    print("running notifications")
    if models_to_notify_about := check_for_new_models_for_dataset_and_update():
        for dataset_id, new_models in models_to_notify_about.items():
            with SqliteDict(
                f"{save_dir}/tracked_dataset_to_users.sqlite"
            ) as tracked_dataset_to_users_db:
                users_to_notify = tracked_dataset_to_users_db.get(dataset_id)
                maybe_create_discussion(
                    REPO, dataset_id, new_models, users_to_notify, AUTHOR, HF_TOKEN
                )
                print("notified about new models")


def number_of_users_tracking_datasets():
    with SqliteDict(
        f"{save_dir}/tracked_dataset_to_users.sqlite"
    ) as tracked_dataset_to_users_db:
        return count(unique(concat(iter(tracked_dataset_to_users_db.values()))))


def number_of_datasets_tracked():
    with SqliteDict(f"{save_dir}/models_to_dataset.sqlite") as datasets_to_models_db:
        return len(datasets_to_models_db)


@cached(cache=ten_min_cache)
def generate_summary_stats():
    return (
        f"Currently there are {number_of_users_tracking_datasets()} users tracking"
        f" datasets with a total of {number_of_datasets_tracked()} datasets being"
        " tracked"
    )


def _user_stats(username: str):
    if not (tracked_datasets := datasets_tracked_by_user(username)):
        return "You are not currently tracking any datasets"
    response = (
        "You are  currently tracking whether new models have been trained on"
        f" {len(tracked_datasets)} datasets.\n\n"
    )
    response += "### Datasets being tracked \n\n"
    response += (
        "You are currently monitoring whether new models have been trained on the"
        " following datasets:\n"
    )
    for dataset in tracked_datasets:
        response += f"- [{dataset}](https://huggingface.co/datasets/{dataset})\n"
    return response


def user_stats(profile: gr.OAuthProfile | None):
    if not profile and not local:
        return "You must be logged in to remove a dataset"
    username = profile.preferred_username
    return _user_stats(username)


markdown_text = """
The Hugging Face Hub allows users to specify the dataset used to train a model in the model metadata. 
This metadata allows you to find models trained on a particular dataset.
These links can be very powerful for finding models that might be suitable for a particular task.\n\n

This Gradio app allows you to track datasets hosted on the Hugging Face Hub and get a notification when new models are trained on the dataset you are tracking. 
1. Submit the Hugging Face Hub ID for the dataset you are interested in tracking. 
2. If a new model is listed as being trained on this dataset Librarian Bot will ping you in a discussion on the Hugging Face Hub to let you know. 
3. Librarian Bot will check for new models for a particular dataset once a day.

**Tip** *You can use a wildcard `*` to track all datasets for a user or organization on the hub. For example `biglam/*` will create alerts for all the datasets under the biglam Hugging Face Organization*


**You need to be logged in to your Hugging Face account to use this app.** If you don't have a Hugging Face Hub account you can get one <a href="https://huggingface.co/join">here</a>.        
"""

with gr.Blocks() as demo:
    gr.Markdown(
        '<div style="text-align: center;"><h1> &#129302; Librarian Bot Dataset-to-Model'
        ' Monitor &#129302; </h1><i><p style="font-size: 20px;">✨ Get alerts when a new'
        " model is created from a dataset you are interested in! ✨</p></i></div>"
    )

    with gr.Row():
        gr.Markdown(markdown_text)
    with gr.Row():
        hub_id = gr.Textbox(
            "i.e. biglam/brill_iconclass",
            label="Hugging Face Hub ID for dataset to track",
        )
        with gr.Column():
            track_button = gr.Button("Track new models for dataset")
            with gr.Row():
                remove_specific_datasets = gr.Button("Stop tracking dataset")
                remove_all = gr.Button("⛔️ Unsubscribe from all datasets ⛔️")
    with gr.Row(variant="compact"):
        gr.LoginButton(size="sm")
        gr.LogoutButton(size="sm")
        summary_stats_btn = gr.Button(
            "Summary stats for datasets being tracked by this app", size="sm"
        )
        user_stats_btn = gr.Button("List my tracked datasets", size="sm")
    with gr.Row():
        output = gr.Markdown()
        track_button.click(user_update, [hub_id], output)
        remove_specific_datasets.click(
            remove_user_from_tracking_datasets, [hub_id], output
        )
        summary_stats_btn.click(generate_summary_stats, [], output)
        user_stats_btn.click(user_stats, [], output)
scheduler = BackgroundScheduler()

if local:
    scheduler.add_job(notify_about_new_models, "interval", minutes=30)
else:
    scheduler.add_job(
        notify_about_new_models,
        CronTrigger.from_crontab("0 */12 * * *"),
    )
scheduler.start()
demo.queue(max_size=5)
demo.launch()