server.py

import copy
import glob
import json
import os
import hashlib
import time
from collections import namedtuple

import gradio as gr
import pandas as pd
from huggingface_hub import HfApi, snapshot_download

from compare_significance import check_significance, SUPPORTED_METRICS

VISIBLE_METRICS = SUPPORTED_METRICS + ["macro_f1"]

api = HfApi()

ORG = "xdolez52"
REPO = f"{ORG}/LLM_benchmark_data"
HF_TOKEN = os.environ.get("HF_TOKEN")
TASKS_METADATA_PATH = "./tasks_metadata.json"

class LeaderboardServer:
    def __init__(self):
        self.server_address = REPO
        self.repo_type = "dataset"
        self.local_leaderboard = snapshot_download(
            self.server_address,
            repo_type=self.repo_type,
            token=HF_TOKEN,
            local_dir="./",
        )
        self.submission_id_to_file = {}  # Map submission ids to file paths
        self.tasks_metadata = json.load(open(TASKS_METADATA_PATH))
        self.tasks_categories = {self.tasks_metadata[task]["category"] for task in self.tasks_metadata}
        self.submission_ids = set()
        self.fetch_existing_models()
        self.tournament_results = self.load_tournament_results()
        self.pre_submit = None

    def update_leaderboard(self):
        self.local_leaderboard = snapshot_download(
            self.server_address,
            repo_type=self.repo_type,
            token=HF_TOKEN,
            local_dir="./",
        )
        self.fetch_existing_models()
        self.tournament_results = self.load_tournament_results()

    def load_tournament_results(self):
        metadata_rank_paths = os.path.join(self.local_leaderboard, "tournament.json")
        if not os.path.exists(metadata_rank_paths):
            return {}
        with open(metadata_rank_paths) as ranks_file:
            results = json.load(ranks_file)
        return results

    def fetch_existing_models(self):
        # Models data
        for submission_file in glob.glob(os.path.join(self.local_leaderboard, "data") + "/*.json"):
            data = json.load(open(submission_file))
            metadata = data.get('metadata')
            if metadata is None:
                continue
            submission_id = metadata["submission_id"]
            self.submission_ids.add(submission_id)

            self.submission_id_to_file[submission_id] = submission_file

    def get_leaderboard(self, tournament_results=None):
        tournament_results = tournament_results if tournament_results else self.tournament_results

        if len(tournament_results) == 0:
            return pd.DataFrame(columns=['No submissions yet'])
        else:
            processed_results = []
            for submission_id in tournament_results.keys():
                path = self.submission_id_to_file.get(submission_id)
                if path is None:
                    if self.pre_submit and submission_id == self.pre_submit.submission_id:
                        data = json.load(open(self.pre_submit.file))
                    else:
                        raise gr.Error(f"Internal error: Submission [{submission_id}] not found")
                elif path:
                    data = json.load(open(path))
                else:
                    raise gr.Error(f"Submission [{submission_id}] not found")
                
                if submission_id != data["metadata"]["submission_id"]:
                    raise gr.Error(f"Proper submission [{submission_id}] not found")

                local_results = {}
                for task in self.tasks_metadata.keys():
                    
                    # tournament_results
                    local_results[task] = 0
                    for competitor_id in tournament_results[submission_id].keys():
                        if tournament_results[submission_id][competitor_id][task]:
                            local_results[task] += 1
                    
                    for metric in VISIBLE_METRICS:
                        metric_value = data['results'][task].get(metric)
                        if metric_value is not None:
                            local_results[task + "_" + metric] = metric_value

                local_results["model"] = f'<a href="{data["metadata"]["link_to_model"]}">{submission_id}</a>'
                release = data["metadata"].get("submission_timestamp")
                release = time.strftime("%Y-%m-%d", time.gmtime(release)) if release else "N/A"
                local_results["release"] = release
                local_results["model_type"] = data["metadata"]["model_type"]
                local_results["parameters"] = data["metadata"]["parameters"]
                local_results["win_score"] = "TBD"  # TODO: Implementovat výpočet WinScore

                if self.pre_submit and submission_id == self.pre_submit.submission_id:
                    processed_results.insert(0, local_results)
                else:
                    processed_results.append(local_results)
            dataframe = pd.DataFrame.from_records(processed_results)
            
            extra_attributes_map_word_to_header = {
                "model": "Model",
                "release": "Release",
                "win_score": "Win score",
                "team_name": "Team name",
                "model_name": "Model name",
                "model_type": "Type",
                "parameters": "Parameters",
                "precision": "Precision",
                "description": "Description",
                "link_to_model": "Link to model"
            }
            first_attributes = [
                "model",
                "release",
                "model_type",
                "parameters",
                "win_score",
            ]
            df_order = [
                key
                for key in dict.fromkeys(
                    first_attributes
                    + list(self.tasks_metadata.keys())
                    + list(dataframe.columns)
                ).keys()
                if key in dataframe.columns
            ]
            dataframe = dataframe[df_order]
            attributes_map_word_to_header = {key: value["abbreviation"] for key, value in self.tasks_metadata.items()}
            attributes_map_word_to_header.update(extra_attributes_map_word_to_header)
            dataframe = dataframe.rename(
                columns=attributes_map_word_to_header
            )
            return dataframe

    def start_tournament(self, new_submission_id, new_model_file):
        new_tournament = copy.deepcopy(self.tournament_results)
        new_tournament[new_submission_id] = {}
        new_tournament[new_submission_id][new_submission_id] = {
            task: False for task in self.tasks_metadata.keys()
        }

        for competitor_id in self.submission_ids:
            res = check_significance(new_model_file, self.submission_id_to_file[competitor_id])
            res_inverse = check_significance(self.submission_id_to_file[competitor_id], new_model_file)
            new_tournament[new_submission_id][competitor_id] = {
                task: data["significant"] for task, data in res.items()
            }
            new_tournament[competitor_id][new_submission_id] = {
                task: data["significant"] for task, data in res_inverse.items()
            }
        return new_tournament

    @staticmethod
    def create_submission_id(metadata):
        # Délka ID musí být omezena, protože se používá v názvu souboru
        submission_id = "_".join([metadata[key][:7] for key in (
            "team_name",
            "model_name",
            "model_predictions_sha256",
            "model_results_sha256",
        )])
        return submission_id

    @staticmethod
    def get_sha256_hexdigest(obj):
        data = json.dumps(
            obj,
            separators=(',', ':'),
            sort_keys=True,
            ensure_ascii=True,
        ).encode()
        result = hashlib.sha256(data).hexdigest()
        return result
    
    PreSubmit = namedtuple('PreSubmit', 'tournament_results, submission_id, file')
    
    def prepare_model_for_submission(self, file, metadata) -> None:
        with open(file, "r") as f:
            data = json.load(f)
        
        data["metadata"] = metadata
        
        metadata["model_predictions_sha256"] = self.get_sha256_hexdigest(data["predictions"])
        metadata["model_results_sha256"] = self.get_sha256_hexdigest(data["results"])
        
        submission_id = self.create_submission_id(metadata)
        metadata["submission_id"] = submission_id
        
        metadata["submission_timestamp"] = time.time()  # timestamp
        
        with open(file, "w") as f:
            json.dump(data, f, separators=(',', ':'))  # compact JSON
        
        tournament_results = self.start_tournament(submission_id, file)
        self.pre_submit = self.PreSubmit(tournament_results, submission_id, file)

    def save_pre_submit(self):
        if self.pre_submit:
            tournament_results, submission_id, file = self.pre_submit
            api.upload_file(
                path_or_fileobj=file,
                path_in_repo=f"data/{submission_id}.json",
                repo_id=self.server_address,
                repo_type=self.repo_type,
                token=HF_TOKEN,
            )

            # Temporary save tournament results
            tournament_results_path = os.path.join(self.local_leaderboard, "tournament.json")
            with open(tournament_results_path, "w") as f:
                json.dump(tournament_results, f, sort_keys=True, indent=2)  # readable JSON

            api.upload_file(
                path_or_fileobj=tournament_results_path,
                path_in_repo="tournament.json",
                repo_id=self.server_address,
                repo_type=self.repo_type,
                token=HF_TOKEN,
            )

    def get_model_detail(self, submission_id):
        path = self.submission_id_to_file.get(submission_id)
        if path is None:
            raise gr.Error(f"Submission [{submission_id}] not found")
        data = json.load(open(path))
        return data["metadata"]