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import glob
import json
import os
from dataclasses import dataclass
from functools import lru_cache
import numpy as np
from app.display.formatting import make_clickable_model
from app.display.utils import AutoEvalColumn, ModelType, Precision, Tasks
from app.submission.check_validity import is_model_on_hub
# Add caching for hub checks to avoid repeated network calls
@lru_cache(maxsize=256)
def cached_is_model_on_hub(full_model, revision):
"""Cached version of is_model_on_hub to avoid repeated network calls"""
return is_model_on_hub(full_model, revision, trust_remote_code=True, test_tokenizer=False)
@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
revision: str # commit hash, "" if main
results: dict
precision: Precision = Precision.Unknown
model_type: ModelType = ModelType.Unknown # Pretrained, fine tuned, ...
architecture: str = "Unknown"
license: str = "?"
likes: int = 0
num_params: int = 0
date: str = "" # submission date of request file
still_on_hub: bool = False
reasoning: bool = False # Whether reasoning is enabled for this model
note: str = "" # Extra information about the model (e.g., thinking budget, warnings)
@classmethod
def init_from_new_format_json_file(self, json_filepath):
"""Inits the result from the new format model result file"""
with open(json_filepath) as fp:
data = json.load(fp)
results = data.get("results")
full_model = data.get("config_general", {}).get("model_name", "").strip()
result_key = full_model.replace("/", "_")
org, model = full_model.split("/", 1) if "/" in full_model else ("", full_model)
still_on_hub, _, model_config = cached_is_model_on_hub(full_model, "main")
architecture = "?"
if model_config is not None:
architectures = getattr(model_config, "architectures", None)
if architectures:
architecture = ";".join(architectures)
# Extract results available in this file
score_results = {}
for task in Tasks:
task = task.value
benchmark_id = task.benchmark
metric = task.metric
scores = [
results[key][metric]
for key in results
if "|" in key and benchmark_id.startswith(key.split("|")[1].removeprefix("icelandic_evals:"))
]
if len(scores) == 0:
continue
mean_acc = np.mean(scores) * 100.0
score_results[benchmark_id] = mean_acc
return self(
eval_name=result_key,
full_model=full_model,
org=org,
model=model,
results=score_results,
revision="",
still_on_hub=still_on_hub,
architecture=architecture,
)
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, self.precision.value.name)
try:
with open(request_file, "r") as f:
request = json.load(f)
self.model_type = ModelType.from_str(request.get("model_type", ""))
self.license = request.get("license", "?")
self.likes = request.get("likes", 0)
self.num_params = request.get("params", 0)
self.date = request.get("submitted_time", "")
self.reasoning = request.get("reasoning", False) or request.get("gen_kwargs", {}).get(
"reasoning_effort", None
)
self.note = request.get("note", "") # Default to empty string if missing
except FileNotFoundError:
print(
f"Could not find request file for {self.org}/{self.model} with precision {self.precision.value.name}"
)
def to_dict(self):
"""Converts the Eval Result to a dict compatible with our dataframe display"""
average = sum([v for v in self.results.values() if v is not None]) / len(Tasks)
data_dict = {
"eval_name": self.eval_name, # not a column, just a save name,
AutoEvalColumn.precision.name: self.precision.value.name,
AutoEvalColumn.model_type.name: self.model_type.value.name,
AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol,
AutoEvalColumn.architecture.name: self.architecture,
AutoEvalColumn.model.name: make_clickable_model(self.full_model),
AutoEvalColumn.revision.name: self.revision,
AutoEvalColumn.average.name: average,
AutoEvalColumn.license.name: self.license,
AutoEvalColumn.likes.name: self.likes,
AutoEvalColumn.params.name: self.num_params,
AutoEvalColumn.still_on_hub.name: self.still_on_hub,
AutoEvalColumn.reasoning.name: self.reasoning,
AutoEvalColumn.note.name: self.note,
}
for task in Tasks:
if task.value.benchmark in self.results.keys():
data_dict[task.value.col_name] = self.results[task.value.benchmark]
else:
data_dict[task.value.col_name] = None
return data_dict
def get_request_file_for_model(requests_path, model_name, precision):
"""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)
if len(request_files) == 1:
return request_files[0]
# 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["precision"] == precision.split(".")[-1] or req_content["precision"] is None:
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 = []
# Collect all JSON files first
for root, _, files in os.walk(results_path):
# We should only have json files in model results
json_files = [f for f in files if f.endswith(".json")]
if len(json_files) == 0:
continue
# Sort JSON files by date (newer later)
try:
json_files.sort(key=lambda x: x.removesuffix(".json").removeprefix("results_")[:-7])
except (ValueError, IndexError):
# If sorting fails, just use the files as-is or take the last one
json_files = [json_files[-1]] if json_files else []
for file in json_files:
model_result_filepaths.append(os.path.join(root, file))
eval_results = {}
for model_result_filepath in model_result_filepaths:
try:
# Creation of result
eval_result = EvalResult.init_from_new_format_json_file(model_result_filepath)
eval_result.update_with_request_file(requests_path)
# Store results of same eval together
eval_name = eval_result.eval_name
if eval_name in eval_results:
# Update with newer scores
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
except Exception as e:
# Log error but continue processing other files
print(f"Error processing {model_result_filepath}: {e}")
continue
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
continue
return results
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