yourbench_space/leaderboard_space/utils.py

import json
from typing import Tuple

from env import TASK, MODELS, ORG_NAME

import gradio as gr
from datasets import Dataset, load_dataset


def aggregate_results() -> list:
    """From the path of outputs and model list, extracts the current scores and stores them in a list of dicts with model, score, time as keys"""
    all_results = []
    for org_model in MODELS:
        try:
            path = f"{ORG_NAME}/details_{org_model.replace('/', '__')}_private"
            ds = load_dataset(path, "results", split="latest")
            config = json.loads(ds["config_general"][0])
            results = json.loads(ds["results"][0])

            # Model data
            org, model = org_model.split("/")

            cur_result = {"Org": org, "Model": model, "Duration (s)": config["end_time"] - config["start_time"]}

            # Extract the task from the JSON data
            for k_metric, v_dict in results.items():
                if k_metric != "all":
                    for k, v in v_dict.items():
                        cur_result[f"{k}({k_metric})"] = v
            all_results.append(cur_result)
        except Exception as e:
            print(f"Error processing {org_model} {ORG_NAME}: {e}")
    return all_results


def extract_dataviz() -> Tuple[list, list]:
    """From the path of outputs and model list, extracts from the details the worst samples, best samples"""
    all_samples = {}
    for org_model in MODELS:
        try:
            path = f"{ORG_NAME}/details_{org_model.replace('/', '__')}_private"
            ds = load_dataset(path, f"custom_{TASK.replace('/', '_')}_0", split="latest")

            for ix, row in enumerate(ds):
                prompt = row["full_prompt"]
                gold = row["gold"]
                score = list(row["metrics"].values())[0]
                prediction = row["predictions"][0]

                # We store flattened samples in a dict
                # ix -> ix, prompt, gold, model_score for each model, model_prediction for each model
                # then 2 lists: model_scores and models, to aggreg more easily
                if ix not in all_samples:
                    all_samples[ix] = {
                        "ix": ix,
                        "prompt": prompt,
                        "gold": gold[0] if isinstance(gold, list) else gold,
                        # A bit redundant, but put in their own boxes for simplicity of access later
                        "model_scores": [],
                        "models": [],
                    }
                if org_model not in all_samples[ix]["models"]:
                    all_samples[ix][f"{org_model}_score"] = row["metrics"]
                    all_samples[ix][f"{org_model}_prediction"] = prediction
                    all_samples[ix]["model_scores"].append(score)
                    all_samples[ix]["models"].append(org_model)

        except Exception as e:
            print(f"Error processing {org_model}: {e}")

    full_samples = sorted(all_samples.values(), key=lambda r: r["ix"])

    hard_samples = sorted(
        [sample for sample in all_samples.values() if sum(sample["model_scores"]) == 0], key=lambda r: r["ix"]
    )
    easy_samples = sorted(
        [sample for sample in all_samples.values() if sum(sample["model_scores"]) == len(sample["model_scores"])],
        key=lambda r: r["ix"],
    )
    return easy_samples, hard_samples, full_samples


def samples_to_box_display(samples: list, example_index: int = 0):
    """Adapted from Nathan's code in https://huggingface.co/spaces/SaylorTwift/OpenEvalsModelDetails/"""
    if len(samples) == 0:
        return "No samples in this category!"
    outputs = []
    sample = samples[example_index]
    for model in sample["models"]:
        try:
            outputs.append({
                "Model": model,
                "Prediction": sample[f"{model}_prediction"],
                "Prompt": sample["prompt"],
                "Metrics": sample[f"{model}_score"],
                "Gold": sample["gold"],
            })
        except (KeyError, IndexError):
            continue

    if not outputs:
        return "No results found for the selected combination."

    # Create HTML output with all models
    html_output = "<div style='max-width: 800px; margin: 0 auto;'>\n\n"

    # Show gold answer at the top with distinct styling
    if outputs:
        html_output += "<div style='background: #e6f3e6; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>\n"
        html_output += "<h3 style='margin-top: 0;'>Ground Truth</h3>\n"
        html_output += "<div style='overflow-x: auto; max-width: 100%;'>\n"
        html_output += f"<pre style='white-space: pre-wrap; word-wrap: break-word; margin: 0;'><code>{outputs[0]['Gold']}</code></pre>\n"
        html_output += "</div>\n"
        html_output += "</div>\n"

    for output in outputs:
        html_output += "<div style='background: #f5f5f5; padding: 20px; margin-bottom: 20px; border-radius: 10px;'>\n"
        html_output += f"<h2 style='margin-top: 0;'>{output['Model']}</h2>\n"

        # Format metrics as a clean table
        html_output += "<details open style='margin-bottom: 15px;'>\n"
        html_output += "<summary><h3 style='display: inline; margin: 0;'>Metrics</h3></summary>\n"
        metrics = output["Metrics"]
        if isinstance(metrics, str):
            metrics = eval(metrics)
        html_output += "<div style='overflow-x: auto;'>\n"
        html_output += "<table style='width: 100%; margin: 10px 0; border-collapse: collapse;'>\n"
        for key, value in metrics.items():
            if isinstance(value, float):
                value = f"{value:.3f}"
            html_output += f"<tr><td style='padding: 5px; border-bottom: 1px solid #ddd;'><strong>{key}</strong></td><td style='padding: 5px; border-bottom: 1px solid #ddd;'>{value}</td></tr>\n"
        html_output += "</table>\n"
        html_output += "</div>\n"
        html_output += "</details>\n\n"

        # Handle prompt formatting with better styling
        html_output += "<details style='margin-bottom: 15px;'>\n"
        html_output += "<summary><h3 style='display: inline; margin: 0;'>Prompt</h3></summary>\n"
        html_output += "<div style='background: #ffffff; padding: 15px; border-radius: 5px; margin-top: 10px;'>\n"

        prompt_text = output["Prompt"]
        if isinstance(prompt_text, list):
            for i, msg in enumerate(prompt_text):
                if isinstance(msg, dict) and "content" in msg:
                    role = msg.get("role", "message").title()
                    html_output += "<div style='margin-bottom: 10px;'>\n"
                    html_output += f"<strong>{role}:</strong>\n"
                    html_output += "<div style='overflow-x: auto;'>\n"
                    html_output += f"<pre style='white-space: pre-wrap; word-wrap: break-word; margin: 5px 0;'><code>{msg['content']}</code></pre>\n"
                    html_output += "</div>\n"
                    html_output += "</div>\n"
                else:
                    html_output += "<div style='margin-bottom: 10px;'>\n"
                    html_output += "<div style='overflow-x: auto;'>\n"
                    html_output += f"<pre style='white-space: pre-wrap; word-wrap: break-word; margin: 5px 0;'><code>{json.dumps(msg, indent=2)}</code></pre>\n"
                    html_output += "</div>\n"
                    html_output += "</div>\n"
        else:
            html_output += "<div style='overflow-x: auto;'>\n"
            if isinstance(prompt_text, dict) and "content" in prompt_text:
                html_output += f"<pre style='white-space: pre-wrap; word-wrap: break-word; margin: 5px 0;'><code>{prompt_text['content']}</code></pre>\n"
            else:
                html_output += f"<pre style='white-space: pre-wrap; word-wrap: break-word; margin: 5px 0;'><code>{prompt_text}</code></pre>\n"
            html_output += "</div>\n"

        html_output += "</div>\n"
        html_output += "</details>\n\n"

        # Style prediction output - now in a collapsible section
        html_output += "<details open style='margin-bottom: 15px;'>\n"
        html_output += "<summary><h3 style='display: inline; margin: 0;'>Prediction</h3>"
        # Add word count in a muted style
        word_count = len(output["Prediction"].split())
        html_output += f"<span style='color: #666; font-size: 0.8em; margin-left: 10px;'>({word_count} words)</span>"
        html_output += "</summary>\n"
        html_output += "<div style='background: #ffffff; padding: 15px; border-radius: 5px; margin-top: 10px;'>\n"
        html_output += "<div style='overflow-x: auto;'>\n"
        html_output += f"<pre style='white-space: pre-wrap; word-wrap: break-word; margin: 0;'><code>{output['Prediction']}</code></pre>\n"
        html_output += "</div>\n"
        html_output += "</div>\n"
        html_output += "</details>\n"
        html_output += "</div>\n\n"

    html_output += "</div>"
    return html_output


def run_pipeline(samples_ix: int = 0):
    results = aggregate_results()
    best_samples, worst_samples, all_samples = extract_dataviz()
    return (
        gr.Dataframe(Dataset.from_list(results).to_pandas(), visible=True),
        gr.HTML(
            samples_to_box_display(best_samples, samples_ix), label="Easiest samples (always found)", visible=True
        ),
        gr.HTML(
            samples_to_box_display(worst_samples, samples_ix), label="Hardest samples (always failed)", visible=True
        ),
        gr.HTML(samples_to_box_display(all_samples, samples_ix), label="All samples", visible=True),
    )


def update_examples(samples_ix: int = 0):
    best_samples, worst_samples, all_samples = extract_dataviz()
    return (
        samples_to_box_display(best_samples, samples_ix),
        samples_to_box_display(worst_samples, samples_ix),
        samples_to_box_display(all_samples, samples_ix),
    )