src/leaderboard/read_evals.py

import glob
import json
import os
from dataclasses import dataclass
import dateutil

from src.display.formatting import make_clickable_model
from src.display.utils import AutoEvalColumn, EvalDimensions
from src.submission.check_validity import is_model_on_hub


@dataclass
class EvalResult:
    """Represents one full evaluation. Built from a combination of the result and request file for a given run.
    """
    eval_name: str # org_model_precision (uid)
    full_model: str # org/model (path on hub)
    org: str 
    model: str
    results: dict
    model_source: str = "" # HF, API, ...
    model_category: str = "" #Nano, Small, Medium, Large
    date: str = "" # submission date of request file
    still_on_hub: bool = False

    @classmethod
    def init_from_json_file(self, json_filepath):
        """Inits the result from the specific model result file"""
        with open(json_filepath) as fp:
            data = json.load(fp)

        config = data.get("config")

        # Get model and org
        org_and_model = config.get("model", config.get("model_args", None))
  
        org_and_model = org_and_model.split("/", 1)

        if len(org_and_model) == 1:
            org = None
            model = org_and_model[0]
            result_key = f"{model}"
        else:
            org = org_and_model[0]
            model = org_and_model[1]
            result_key = f"{org}_{model}"
        full_model = "/".join(org_and_model)

        still_on_hub, _, _ = is_model_on_hub(
            full_model, config.get("model_sha", "main"), trust_remote_code=True, test_tokenizer=False
        )
        

        # Extract results available in this file (some results are split in several files)
        results = {}

        results_obj = data.get("results")
        
        results["average_score"] = results_obj.get("average_score")
        results["speed"] = results_obj.get("speed")
        results["contamination_score"] = results_obj.get("contamination_score")

        scores_by_category = results_obj.get("scores_by_category")

        for category_obj in scores_by_category:
            category = category_obj["category"]
            average_score = category_obj["average_score"]
            results[category.lower()] = average_score

            
        

        return self(
            eval_name=result_key,
            full_model=full_model,
            org=org,
            model=model,
            model_source=config.get("model_source", ""),
            model_category=config.get("model_category", ""),
            results=results,
            still_on_hub=still_on_hub,
        )

    def update_with_request_file(self, requests_path):
        """Finds the relevant request file for the current model and updates info with it"""
        request_file = get_request_file_for_model(requests_path, self.full_model)
        try:
            with open(request_file, "r") as f:
                request = json.load(f)
            
            self.date = request.get("submitted_time", "")
        except Exception:
            print(f"Could not find request file for {self.org}/{self.model}")

    def to_dict(self):
        """Converts the Eval Result to a dict compatible with our dataframe display"""
        average_score = self.results["average_score"]
        data_dict = {
            "eval_name": self.eval_name,  # not a column, just a save name,
            AutoEvalColumn.model_source.name: self.model_source,
            AutoEvalColumn.model_category.name: self.model_category,
            AutoEvalColumn.model.name: make_clickable_model(self.full_model),
            AutoEvalColumn.average_score.name: average_score,
        }

        for eval_dim in EvalDimensions:
            dimension_name = eval_dim.value.col_name
            try:
                dimension_value = self.results[eval_dim.value.metric]
            except KeyError:
                dimension_value = 0

            if dimension_name == "Contamination Score":
                dimension_value = 0 if dimension_value < 0 else round(dimension_value,2)
            
            data_dict[dimension_name] = dimension_value

        return data_dict


def get_request_file_for_model(requests_path, model_name):
    """Selects the correct request file for a given model. Only keeps runs tagged as FINISHED"""
    request_files = os.path.join(
        requests_path,
        f"{model_name}_eval_request.json",
    )
    
    request_files = glob.glob(request_files)

    # Select correct request file (precision)
    request_file = ""
    request_files = sorted(request_files, reverse=True)
    for tmp_request_file in request_files:
        with open(tmp_request_file, "r") as f:
            req_content = json.load(f)
            if (
                req_content["status"] in ["FINISHED"]
            ):
                request_file = tmp_request_file
    return request_file


def get_raw_eval_results(results_path: str, requests_path: str) -> list[EvalResult]:
    """From the path of the results folder root, extract all needed info for results"""
    model_result_filepaths = []

    for root, _, files in os.walk(results_path):
        
        ## we allow HTML files now
        #if len(files) == 0 or any([not f.endswith(".json") for f in files]):
        #    continue
        files = [f for f in files if f.endswith(".json")]

        # Sort the files by date
        try:
            files.sort(key=lambda x: x.removesuffix(".json").removeprefix("results_")[:-7])
        except dateutil.parser._parser.ParserError as e:
            print("Error",e)
            files = [files[-1]]
        
        for file in files:
            model_result_filepaths.append(os.path.join(root, file))

    eval_results = {}
    for model_result_filepath in model_result_filepaths:
        # Creation of result
        eval_result = EvalResult.init_from_json_file(model_result_filepath)
        #eval_result.update_with_request_file(requests_path) ##not needed, save processing time

        # Store results of same eval together
        eval_name = eval_result.eval_name
        if eval_name in eval_results.keys():
            eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None})
        else:
            eval_results[eval_name] = eval_result

    results = []

    for v in eval_results.values():
        try:

            v.to_dict() # we test if the dict version is complete
            results.append(v)
        except KeyError:  # not all eval values present
            print("Key error in eval result, skipping")
            
            continue

    return results


def get_model_answers_html_file(results_path, model_name):

    model_org,model_name_only = model_name.split("/")
    model_answers_prefix = f"{results_path}/{model_org}/"

    html_file_content = "EMPTY"
    download_file_path = "https://huggingface.co/spaces/silma-ai/Arabic-LLM-Broad-Leaderboard/raw/main/"

    for root, _, files in os.walk(model_answers_prefix):

        for file_name in files:

            if file_name.startswith(f"{model_name_only}_abb_benchmark_answers_"):

                file_path = os.path.join(root, file_name)

                with open(file_path, "r") as f:
                    
                    html_file_content = f.read()
                    download_file_path = download_file_path + file_path.replace("./", "")
                    break

    return html_file_content,download_file_path