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import json
import os
import pandas as pd
from src.display.formatting import has_no_nan_values, make_clickable_model
from src.display.utils import AutoEvalColumn, EvalQueueColumn
from src.leaderboard.read_evals import get_raw_eval_results
def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
"""Creates a dataframe from all the individual experiment results"""
raw_data = get_raw_eval_results(results_path, requests_path)
all_data_json = [v.to_dict() for v in raw_data]
df = pd.DataFrame.from_records(all_data_json)
# ------------------------------------------------------------------
# Fallback: if no evaluation results are found we populate the
# leaderboard with a single example model. This guarantees that a
# freshly deployed Space shows a non-empty leaderboard and it serves
# as a template for the expected columns/values.
# ------------------------------------------------------------------
if df.empty:
example_row = {}
# Populate benchmark metrics with the default value 0.5 using internal column names
for metric in benchmark_cols:
example_row[metric] = 0.5
# Minimal metadata so that the row displays nicely
example_row[AutoEvalColumn.model.name] = make_clickable_model("example/model")
example_row[AutoEvalColumn.average.name] = 0.5
example_row[AutoEvalColumn.model_type_symbol.name] = "🟢"
example_row[AutoEvalColumn.model_type.name] = "pretrained"
example_row[AutoEvalColumn.precision.name] = "float16"
example_row[AutoEvalColumn.weight_type.name] = "Original"
example_row[AutoEvalColumn.still_on_hub.name] = True
example_row[AutoEvalColumn.architecture.name] = "Transformer"
example_row[AutoEvalColumn.revision.name] = "main"
example_row[AutoEvalColumn.license.name] = "apache-2.0"
# Any missing columns will be created later in the function
df = pd.DataFrame([example_row])
# Sort primarily by LLM exact-match Pass@1 metric; if not present, fall back to average
preferred_cols = []
if hasattr(AutoEvalColumn, "pass_at_1"):
preferred_cols.append(AutoEvalColumn.pass_at_1.name)
preferred_cols.append(AutoEvalColumn.average.name)
for col in preferred_cols:
if col in df.columns:
df = df.sort_values(by=[col], ascending=False)
break
# Ensure all expected columns exist, add missing ones with NaN so selection does not fail
for expected in cols:
if expected not in df.columns:
df[expected] = pd.NA
df = df[cols].round(decimals=2)
# filter out if any of the benchmarks have not been produced
df = df[has_no_nan_values(df, benchmark_cols)]
return df
def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
"""Creates the different dataframes for the evaluation queues requestes"""
entries = [entry for entry in os.listdir(save_path) if not entry.startswith(".")]
all_evals = []
for entry in entries:
if ".json" in entry:
file_path = os.path.join(save_path, entry)
with open(file_path) as fp:
data = json.load(fp)
data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
data[EvalQueueColumn.revision.name] = data.get("revision", "main")
all_evals.append(data)
elif ".md" not in entry:
# this is a folder
sub_entries = [e for e in os.listdir(f"{save_path}/{entry}") if os.path.isfile(e) and not e.startswith(".")]
for sub_entry in sub_entries:
file_path = os.path.join(save_path, entry, sub_entry)
with open(file_path) as fp:
data = json.load(fp)
data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
data[EvalQueueColumn.revision.name] = data.get("revision", "main")
all_evals.append(data)
pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN"]]
running_list = [e for e in all_evals if e["status"] == "RUNNING"]
finished_list = [e for e in all_evals if e["status"].startswith("FINISHED") or e["status"] == "PENDING_NEW_EVAL"]
df_pending = pd.DataFrame.from_records(pending_list, columns=cols)
df_running = pd.DataFrame.from_records(running_list, columns=cols)
df_finished = pd.DataFrame.from_records(finished_list, columns=cols)
return df_finished[cols], df_running[cols], df_pending[cols]
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