app.py

import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download

from src.display.about import (
    CITATION_BUTTON_LABEL,
    CITATION_BUTTON_TEXT,
    EVALUATION_QUEUE_TEXT,
    INTRODUCTION_TEXT,
    LLM_BENCHMARKS_TEXT,
    TITLE,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
    BENCHMARK_COLS,
    COLS,
    EVAL_COLS,
    EVAL_TYPES,
    NUMERIC_INTERVALS,
    TYPES,
    AutoEvalColumn,
    ModelType,
    fields,
    WeightType,
    Precision
)
from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, TOKEN, QUEUE_REPO, REPO_ID, RESULTS_REPO
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_eval


def restart_space():
    API.restart_space(repo_id=REPO_ID, token=TOKEN)

try:
    print(EVAL_REQUESTS_PATH)
    print("Downloading eval requests")
    snapshot_download(
        repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
    )
except Exception:
    restart_space()
try:
    print(EVAL_RESULTS_PATH)
    print("Downloading results into local cache")
    snapshot_download(
        repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
    )
except Exception:
    restart_space()


raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
print("og df: ", original_df)
leaderboard_df = original_df.copy()

(
    finished_eval_queue_df,
    running_eval_queue_df,
    pending_eval_queue_df,
) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)


# Searching and filtering
def update_table(
    hidden_df: pd.DataFrame,
    columns: list,
    type_query: list,
    precision_query: str,
    size_query: list,
    show_deleted: bool,
    query: str,
):
    filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
    filtered_df = filter_queries(query, filtered_df)
    df = select_columns(filtered_df, columns)
    return df


def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
    return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))]


def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
    always_here_cols = [
        AutoEvalColumn.model_type_symbol.name,
        AutoEvalColumn.model.name,
    ]
    # We use COLS to maintain sorting
    filtered_df = df[
        always_here_cols + [c for c in COLS if c in df.columns and c in columns] + [AutoEvalColumn.dummy.name]
    ]
    return filtered_df


def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:
    final_df = []
    if query != "":
        queries = [q.strip() for q in query.split(";")]
        for _q in queries:
            _q = _q.strip()
            if _q != "":
                temp_filtered_df = search_table(filtered_df, _q)
                if len(temp_filtered_df) > 0:
                    final_df.append(temp_filtered_df)
        if len(final_df) > 0:
            filtered_df = pd.concat(final_df)
            filtered_df = filtered_df.drop_duplicates(
                subset=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name]
            )

    return filtered_df


def filter_models(
    df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
) -> pd.DataFrame:
    # Show all models
    if show_deleted:
        filtered_df = df
    else:  # Show only still on the hub models
        filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]

    type_emoji = [t[0] for t in type_query]
    filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
    filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]

    numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
    params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
    mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
    filtered_df = filtered_df.loc[mask]

    return filtered_df


leaderboard_df = filter_models(
    df=leaderboard_df,
    type_query=[t.to_str(" : ") for t in ModelType],
    size_query=list(NUMERIC_INTERVALS.keys()),
    precision_query=[i.value.name for i in Precision],
    show_deleted=False,
)

import unicodedata

def is_valid_unicode(char):
    try:
        unicodedata.name(char)
        return True  # Valid Unicode character
    except ValueError:
        return False  # Invalid Unicode character

def remove_invalid_unicode(input_string):
    if isinstance(input_string, str):
        valid_chars = [char for char in input_string if is_valid_unicode(char)]
        return ''.join(valid_chars)
    else:
        return input_string  # Return non-string values as is

dummy1 = gr.Textbox(visible=False)

hidden_leaderboard_table_for_search = gr.components.Dataframe(
    headers=COLS,
    datatype=TYPES,
    visible=False,
    line_breaks=False,
    interactive=False
)

def display(x, y):
    # Assuming df is your DataFrame
    for column in leaderboard_df.columns:
        if leaderboard_df[column].dtype == 'object':
            leaderboard_df[column] = leaderboard_df[column].apply(remove_invalid_unicode)

    subset_df = leaderboard_df[COLS]
    # Ensure the output directory exists
    #output_dir = 'output'
    #if not os.path.exists(output_dir):
    #    os.makedirs(output_dir)
#
    ## Save JSON to a file in the output directory
    #output_file_path = os.path.join(output_dir, 'output.json')
    #with open(output_file_path, 'w') as file:
    #    file.write(subset_df.to_json(orient='records'))

    #first_50_rows = subset_df.head(50)
    #print(first_50_rows.to_string())
    #json_data = first_50_rows.to_json(orient='records')
    #print(json_data)  # Print JSON representation
    return subset_df

INTRODUCTION_TEXT = """
This is a copied space from Open Source LLM leaderboard. Instead of displaying
the results as table the space simply provides a gradio API interface to access
the full leaderboard data easily.
Example python on how to access the data:
```python
from gradio_client import Client
import json
client = Client("https://felixz-open-llm-leaderboard.hf.space/")
json_data = client.predict("","", api_name='/predict')
with open(json_data, 'r') as file:
    file_data = file.read()
# Load the JSON data
data = json.loads(file_data)
# Get the headers and the data
headers = data['headers']
data = data['data']
```
"""

interface = gr.Interface(
    fn=display,
    inputs=[gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text"), dummy1],
    outputs=[hidden_leaderboard_table_for_search]
)

scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()
scheduler = BackgroundScheduler()

interface.launch()