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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, ModelType, Precision, WeightType
from src.leaderboard.read_evals import get_raw_eval_results
from src.about import Tasks
def load_csv_results():
"""Load results from main-results.csv file"""
csv_path = "main-results.csv"
if not os.path.exists(csv_path):
return []
df = pd.read_csv(csv_path)
results = []
for _, row in df.iterrows():
# Parse parameters - handle different formats
param_str = str(row['Param'])
if 'activated' in param_str:
# Extract the activated parameter count (e.g., "2.8B activated (16B total)")
param_value = float(param_str.split('B')[0])
elif 'B' in param_str:
# Simple format (e.g., "9B")
param_value = float(param_str.replace('B', ''))
else:
param_value = 0
# Convert CSV data to the format expected by the leaderboard
data_dict = {
AutoEvalColumn.model.name: make_clickable_model(row['Model']),
AutoEvalColumn.average.name: row['ACC'], # Using ACC as the average score
AutoEvalColumn.params.name: param_value,
AutoEvalColumn.license.name: "Open Source" if row['Open Source?'] == 'Yes' else "Proprietary",
AutoEvalColumn.model_type.name: ModelType.FT.value.name, # Default to fine-tuned
AutoEvalColumn.precision.name: Precision.float16.value.name, # Default precision
AutoEvalColumn.weight_type.name: WeightType.Original.value.name,
AutoEvalColumn.architecture.name: "Unknown",
AutoEvalColumn.still_on_hub.name: True,
AutoEvalColumn.revision.name: "",
AutoEvalColumn.likes.name: 0,
AutoEvalColumn.model_type_symbol.name: ModelType.FT.value.symbol,
}
# Add task-specific scores (required by the leaderboard)
for task in Tasks:
data_dict[task.name] = row['ACC'] # Use the same ACC score for all tasks
results.append(data_dict)
return 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]
# If no JSON data found, try loading from CSV
if not all_data_json:
all_data_json = load_csv_results()
if not all_data_json:
# Return empty dataframe if no data found
return pd.DataFrame(columns=cols)
df = pd.DataFrame.from_records(all_data_json)
# Only include columns that exist in the dataframe
existing_cols = [col for col in cols if col in df.columns]
df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
df = df[existing_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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