src/populate.py

# src/populate.py

import os
import pandas as pd
import json

from src.display.utils import COLUMNS, EVAL_COLS, Tasks
from src.envs import EVAL_RESULTS_PATH

def get_leaderboard_df(eval_results_path, eval_requests_path, cols, benchmark_cols):
    # Initialize an empty DataFrame
    df = pd.DataFrame(columns=cols)

    # Load evaluation results from JSON files
    if os.path.exists(eval_results_path):
        result_files = [
            os.path.join(eval_results_path, f) 
            for f in os.listdir(eval_results_path) 
            if f.endswith('.json')
        ]
        data_list = []
        for file in result_files:
            with open(file, 'r') as f:
                data = json.load(f)
                flattened_data = {}
                flattened_data.update(data.get('config', {}))
                flattened_data.update(data.get('results', {}))
                data_list.append(flattened_data)
        if data_list:
            df = pd.DataFrame(data_list)

    # Ensure DataFrame has all columns
    for col in cols:
        if col not in df.columns:
            df[col] = None

    # Convert 'average' column to float and handle errors
    if 'average' in df.columns:
        df['average'] = pd.to_numeric(df['average'], errors='coerce')

    # Sort by 'average' column if it exists
    if 'average' in df.columns:
        df = df.sort_values(by=['average'], ascending=False)

    # 🔹 Fix Column Data Types for Correct Display in Gradio
    # Ensure numeric fields are properly converted
    NUMERIC_COLUMNS = ["average", "likes", "params"] + [col for col in df.columns if col not in [
        "model_name", "submitted_time", "base_model", "revision", "precision", "weight_type", "model_type", "license"
    ]]
    
    for col in NUMERIC_COLUMNS:
        if col in df.columns:
            df[col] = pd.to_numeric(df[col], errors="coerce")  # Convert to float
    
    # Ensure categorical/text fields are properly converted to strings
    TEXT_COLUMNS = ["model_name", "submitted_time", "base_model", "revision", "precision", "weight_type", "model_type", "license"]
    
    for col in TEXT_COLUMNS:
        if col in df.columns:
            df[col] = df[col].astype(str)
    
    # Debug: Print column types
    print("✅ LEADERBOARD_DF Updated dtypes:\n", df.dtypes)
    
    
    return df

def get_evaluation_queue_df(eval_requests_path, eval_cols):
    # Initialize empty DataFrames
    finished_df = pd.DataFrame(columns=eval_cols)
    running_df = pd.DataFrame(columns=eval_cols)
    pending_df = pd.DataFrame(columns=eval_cols)

    # Load evaluation requests from JSON files
    if os.path.exists(eval_requests_path):
        request_files = [
            os.path.join(eval_requests_path, f) 
            for f in os.listdir(eval_requests_path) 
            if f.endswith('.json')
        ]
        data_list = []
        for file in request_files:
            with open(file, 'r') as f:
                data = json.load(f)
                data_list.append(data)
        if data_list:
            df = pd.DataFrame(data_list)
            # Split DataFrame based on status
            finished_df = df[df['status'] == 'finished']
            running_df = df[df['status'] == 'running']
            pending_df = df[df['status'] == 'pending']

    return finished_df, running_df, pending_df