app.py

import gradio as gr
import os
from huggingface_hub import HfApi, snapshot_download
from apscheduler.schedulers.background import BackgroundScheduler
from datasets import load_dataset
from src.utils import load_all_data
from src.md import ABOUT_TEXT, TOP_TEXT
from src.plt import plot_avg_correlation
from src.constants import subset_mapping, length_categories, example_counts
import numpy as np

api = HfApi()

COLLAB_TOKEN = os.environ.get("COLLAB_TOKEN")
evals_repo = "ai2-adapt-dev/HERM-Results"

eval_set_repo = "ai2-adapt-dev/rm-benchmark-dev"
repo_dir_herm = "./evals/herm/"

def restart_space():
    api.restart_space(repo_id="ai2-adapt-dev/rm-benchmark-viewer", token=COLLAB_TOKEN)

print("Pulling evaluation results")
repo = snapshot_download(
    local_dir=repo_dir_herm,
    ignore_patterns=["pref-sets-scores/*", "eval-set-scores/*"],
    repo_id=evals_repo,
    use_auth_token=COLLAB_TOKEN,
    tqdm_class=None, 
    etag_timeout=30,
    repo_type="dataset",
)


def avg_over_herm(dataframe_core, dataframe_prefs):
    """
    Averages over the subsets alpacaeval, mt-bench, llmbar, refusals, hep and returns dataframe with only these columns.

    We average over 4 core sections (per prompt weighting):
    1. Chat: Includes the easy chat subsets (alpacaeval-easy, alpacaeval-length, alpacaeval-hard, mt-bench-easy, mt-bench-medium)
    2. Chat Hard: Includes the hard chat subsets (mt-bench-hard, llmbar-natural, llmbar-adver-neighbor, llmbar-adver-GPTInst, llmbar-adver-GPTOut, llmbar-adver-manual)
    3. Safety: Includes the safety subsets (refusals-dangerous, refusals-offensive, xstest-should-refuse, xstest-should-respond, do not answer)
    4. Code: Includes the code subsets (hep-cpp, hep-go, hep-java, hep-js, hep-python, hep-rust)

    """
    new_df = dataframe_core.copy()
    dataframe_prefs = dataframe_prefs.copy()

    # for main subsets, keys in subset_mapping, take the weighted avg by example_counts and store for the models
    for subset, sub_subsets in subset_mapping.items():
        subset_cols = [col for col in new_df.columns if col in sub_subsets]
        sub_data = new_df[subset_cols].values # take the relevant column values
        sub_counts = [example_counts[s] for s in sub_subsets] # take the example counts
        new_df[subset] = np.round(np.average(sub_data, axis=1, weights=sub_counts), 2) # take the weighted average
        # new_df[subset] = np.round(np.nanmean(new_df[subset_cols].values, axis=1), 2)

    data_cols = list(subset_mapping.keys())
    keep_columns = ["model",] + ["model_type"] + data_cols
    # keep_columns = ["model", "average"] + subsets
    new_df = new_df[keep_columns]

    # selected average from pref_sets
    pref_columns = ["anthropic_helpful", "mtbench_gpt4", "shp", "summarize"]
    pref_data = dataframe_prefs[pref_columns].values

    # add column test sets knowing the rows are not identical, take superset
    dataframe_prefs["Test Sets"] = np.round(np.nanmean(pref_data, axis=1), 2)

    # add column Test Sets empty to new_df
    new_df["Test Sets"] = np.nan
    # per row in new_df if model is in dataframe_prefs, add the value to new_df["Test Sets"]
    values = []
    for i, row in new_df.iterrows():
        model = row["model"]
        if model in dataframe_prefs["model"].values:
            values.append(dataframe_prefs[dataframe_prefs["model"] == model]["Test Sets"].values[0])
            # new_df.at[i, "Test Sets"] = dataframe_prefs[dataframe_prefs["model"] == model]["Test Sets"].values[0]
        else:
            values.append(np.nan)
    
    new_df["Test Sets"] = values

    # add total average
    data_cols += ["Test Sets"]
    new_df["average"] = np.round(np.nanmean(new_df[data_cols].values, axis=1), 2)

    # make average third column
    keep_columns = ["model", "model_type", "average"] + data_cols
    new_df = new_df[keep_columns]
    return new_df

def expand_subsets(dataframe):
    # TODO need to modify data/ script to do this
    pass


def length_bias_check(dataframe):
    """
    Takes the raw herm dataframe and splits the data into new buckets according to length_categories.
    Then, take the average of the three buckets as "average"
    """
    new_df = dataframe.copy()
    existing_subsets = new_df.columns[3:] # model, model_type, average
    final_subsets = ["Length Bias", "Neutral", "Terse Bias"]
    # new data is empty list dict for each final subset
    new_data = {s: [] for s in final_subsets}

    # now, subsets correspond to those with True, Nuetral, and False length bias
    # check if length_categories[subset] == "True" or "False" or "Neutral"
    for subset in existing_subsets:
        subset_data = new_df[subset].values
        subset_length = length_categories[subset]
        # route to the correct bucket
        if subset_length == "True":
            new_data["Length Bias"].append(subset_data)
        elif subset_length == "Neutral":
            new_data["Neutral"].append(subset_data)
        elif subset_length == "False":
            new_data["Terse Bias"].append(subset_data)

    # take average of new_data and add to new_df (removing other columns than model)
    for subset in final_subsets:
        new_df[subset] = np.round(np.nanmean(new_data[subset], axis=0), 2)
    keep_columns = ["model"] + final_subsets
    new_df = new_df[keep_columns]
    # recompute average
    # new_df["average"] = np.round(np.nanmean(new_df[final_subsets].values, axis=1), 2)

    return new_df


    
herm_data = load_all_data(repo_dir_herm, subdir="eval-set").sort_values(by='average', ascending=False)
herm_data_length = length_bias_check(herm_data).sort_values(by='Terse Bias', ascending=False)
prefs_data = load_all_data(repo_dir_herm, subdir="pref-sets").sort_values(by='average', ascending=False)
# prefs_data_sub = expand_subsets(prefs_data).sort_values(by='average', ascending=False)

herm_data_avg = avg_over_herm(herm_data, prefs_data).sort_values(by='average', ascending=False)

col_types_herm = ["markdown"] + ["str"] + ["number"] * (len(herm_data.columns) - 1)
col_types_herm_avg = ["markdown"]+ ["str"] + ["number"] * (len(herm_data_avg.columns) - 1)
cols_herm_data_length = ["markdown"] + ["number"] * (len(herm_data_length.columns) - 1)
col_types_prefs = ["markdown"] + ["number"] * (len(prefs_data.columns) - 1)
# col_types_prefs_sub = ["markdown"] + ["number"] * (len(prefs_data_sub.columns) - 1)

# for showing random samples
eval_set = load_dataset(eval_set_repo, use_auth_token=COLLAB_TOKEN, split="filtered")
def random_sample(r: gr.Request, subset):
    if subset is None or subset == []:
        sample_index = np.random.randint(0, len(eval_set) - 1)
        sample = eval_set[sample_index]
    else: # filter by subsets (can be list)
        if isinstance(subset, str):
            subset = [subset]
        # filter down dataset to only include the subset(s)
        eval_set_filtered = eval_set.filter(lambda x: x["subset"] in subset)
        sample_index = np.random.randint(0, len(eval_set_filtered) - 1)
        sample = eval_set_filtered[sample_index]

    markdown_text = '\n\n'.join([f"**{key}**:\n\n{value}" for key, value in sample.items()])
    return markdown_text

subsets = eval_set.unique("subset")

def regex_table(dataframe, regex, filter_button):
    """
    Takes a model name as a regex, then returns only the rows that has that in it.
    """
    # Split regex statement by comma and trim whitespace around regexes
    regex_list = [x.strip() for x in regex.split(",")]
    # Join the list into a single regex pattern with '|' acting as OR
    combined_regex = '|'.join(regex_list)
    # if filter_button, remove all rows with "ai2" in the model name
    if (not filter_button) and ("ai2" not in regex):
        dataframe = dataframe[~dataframe["model"].str.contains("ai2", case=False, na=False)]
    # Filter the dataframe such that 'model' contains any of the regex patterns
    return dataframe[dataframe["model"].str.contains(combined_regex, case=False, na=False)]


with gr.Blocks() as app:
    # create tabs for the app, moving the current table to one titled "HERM" and the benchmark_text to a tab called "About"
    with gr.Row():
        with gr.Column(scale=3):
            gr.Markdown(TOP_TEXT)
        with gr.Column(scale=2):
            search = gr.Textbox(label="Model Search (delimit with , )", placeholder="Regex search for a model")
            filter_button = gr.Checkbox(label="Include AI2 training runs (or type ai2 above).", interactive=True)
    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("HERM Eval Set - Overview"):
            with gr.Row():
                # reference data
                herm_table_hidden = gr.Dataframe(
                    herm_data_avg.values,
                    datatype=col_types_herm_avg,
                    headers=herm_data_avg.columns.tolist(),
                    visible=False,
                )
                herm_table = gr.Dataframe(
                    regex_table(herm_data_avg.copy(), "", False).values,
                    datatype=col_types_herm_avg,
                    headers=herm_data_avg.columns.tolist(),
                    elem_id="herm_dataframe_avg",
                    height=1000,
                )
                
        with gr.TabItem("HERM Eval Set - Detailed"):
            with gr.Row():
                # ref data
                herm_table_detailed_hidden = gr.Dataframe(
                    herm_data.values,
                    datatype=col_types_herm,
                    headers=herm_data.columns.tolist(),
                    visible=False,
                )
                herm_table_detailed = gr.Dataframe(
                    regex_table(herm_data.copy(), "", False).values,
                    datatype=col_types_herm,
                    headers=herm_data.columns.tolist(),
                    elem_id="herm_dataframe",
                    height=1000,
                )
        with gr.TabItem("HERM Eval Set - Length Bias"):
            with gr.Row():
                # backup
                herm_table_len_hidden = gr.Dataframe(
                    herm_data_length.values,
                    datatype=cols_herm_data_length,
                    headers=herm_data_length.columns.tolist(),
                    visible=False,
                )
                herm_table_len = gr.Dataframe(
                    regex_table(herm_data_length.copy(), "", False).values,
                    datatype=cols_herm_data_length,
                    headers=herm_data_length.columns.tolist(),
                    elem_id="herm_dataframe_length",
                    height=1000,
                )
        with gr.TabItem("Known Pref. Sets"):
            with gr.Row():
                PREF_SET_TEXT = """
                For more information, see the [dataset](https://huggingface.co/datasets/allenai/pref-test-sets).
                """
                gr.Markdown(PREF_SET_TEXT)
            with gr.Row():
                # backup
                pref_sets_table_hidden = gr.Dataframe(
                    prefs_data.values,
                    datatype=col_types_prefs,
                    headers=prefs_data.columns.tolist(),
                    visible=False,
                )
                pref_sets_table = gr.Dataframe(
                    regex_table(prefs_data.copy(), "", False).values,
                    datatype=col_types_prefs,
                    headers=prefs_data.columns.tolist(),
                    elem_id="prefs_dataframe",
                    height=1000,
                )


        with gr.TabItem("About"):
            with gr.Row():
                gr.Markdown(ABOUT_TEXT)

        with gr.TabItem("Dataset Viewer"):
            with gr.Row():
                # loads one sample
                gr.Markdown("## Random Dataset Sample Viewer")
                subset_selector = gr.Dropdown(subsets, label="Subset", value=None, multiselect=True)
                button = gr.Button("Show Random Sample")

            with gr.Row():
                sample_display = gr.Markdown("{sampled data loads here}")

            button.click(fn=random_sample, inputs=[subset_selector], outputs=[sample_display])
        # removed plot because not pretty enough
        # with gr.TabItem("Model Correlation"):
        #     with gr.Row():
        #         plot = plot_avg_correlation(herm_data_avg, prefs_data)
        #         gr.Plot(plot)

    search.change(regex_table, inputs=[herm_table_hidden, search, filter_button], outputs=herm_table)
    search.change(regex_table, inputs=[herm_table_detailed_hidden, search, filter_button], outputs=herm_table_detailed)
    search.change(regex_table, inputs=[herm_table_len_hidden, search, filter_button], outputs=herm_table_len)
    search.change(regex_table, inputs=[pref_sets_table_hidden, search, filter_button], outputs=pref_sets_table)    
                
# Load data when app starts, TODO make this used somewhere...
# def load_data_on_start():
#     data_herm = load_all_data(repo_dir_herm)
#     herm_table.update(data_herm)

#     data_herm_avg = avg_over_herm(repo_dir_herm)
#     herm_table.update(data_herm_avg)

#     data_prefs = load_all_data(repo_dir_prefs)
#     pref_sets_table.update(data_prefs)

scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=10800) # restarted every 3h
scheduler.start()
app.launch() # had .queue() before launch before... not sure if that's necessary