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




# blz -------------------------

import time
app_start_time = time.time()

"""
import builtins

# Global counter for file openings
file_open_count = 0

def log_file_access(file_name):
    global file_open_count
    file_open_count += 1
    print(f"File '{file_name}' opened for reading. Total count: {file_open_count}")

# Patch built-in open
original_open = builtins.open

def custom_open(*args, **kwargs):
    file_name = args[0] if args else kwargs.get('file', 'Unknown')
    log_file_access(file_name)
    return original_open(*args, **kwargs)

builtins.open = custom_open
"""

import json
def print_first_json_content(directory):
    for root, dirs, files in os.walk(directory):
        for file in files:
            if file.endswith(".json"):
                json_file_path = os.path.join(root, file)
                try:
                    with open(json_file_path, 'r') as json_file:
                        data = json.load(json_file)
                        print(f"Contents of {json_file_path}:")
                        print(json.dumps(data, indent=4))
                        return True
                except Exception as e:
                    print(f"Error reading {json_file_path}: {e}")
                    return False
    print("No JSON file found.")
    return False


import os
def count_files_in_directory_tree(directory):
    file_count = 0
    for root, dirs, files in os.walk(directory):
        file_count += len(files)
    return file_count


from huggingface_hub import hf_hub_download, Repository
def download_dataset(repo_id, local_dir):
    # Clone the repository
    repo_id_full = f"https://huggingface.co/datasets/{repo_id}"
    repo = Repository(local_dir=local_dir, clone_from=repo_id_full)
    files_cnt = count_files_in_directory_tree(local_dir)
    print(f"{local_dir}: {files_cnt}")
    # print_first_json_content(local_dir)
    # Alternatively, you can download specific files using hf_hub_download
    # file_path = hf_hub_download(repo_id, filename="your_file_name")

# Usage
#download_dataset("https://huggingface.co/datasets/open-llm-leaderboard/requests", "new/requests")
#download_dataset("datasets/open-llm-leaderboard/results", "new/results")

# blz end -------------------

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


try:
    print(EVAL_REQUESTS_PATH)
    download_dataset(repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH)
    #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)
    download_dataset(repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH)
    #snapshot_download(
    #    repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
    #)
except Exception:
    restart_space()



print("after downloading datasets") # BLZ

raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
leaderboard_df = original_df.copy()

print("after get_leaderboard_df()") # BLZ

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


print("after get_evaluation_queue_df()") # BLZ


# 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,
    recent7: bool,
    recent14: bool,
    recent21: bool
):
    filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted, recent7, recent14, recent21)
    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, recent7: bool, recent14: bool, recent21: 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]

    # blz
    if recent7:
        filtered_df = df[df[AutoEvalColumn.recent7.name] == True]
    if recent14:
        filtered_df = df[df[AutoEvalColumn.recent14.name] == True]
    if recent21:
        filtered_df = df[df[AutoEvalColumn.recent21.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


demo = gr.Blocks(css=custom_css)
with demo:
    gr.HTML(TITLE)
    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        search_bar = gr.Textbox(
                            placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
                            show_label=False,
                            elem_id="search-bar",
                        )
                    with gr.Row():
                        shown_columns = gr.CheckboxGroup(
                            choices=[
                                c.name
                                for c in fields(AutoEvalColumn)
                                if not c.hidden and not c.never_hidden and not c.dummy
                            ],
                            value=[
                                c.name
                                for c in fields(AutoEvalColumn)
                                if c.displayed_by_default and not c.hidden and not c.never_hidden
                            ],
                            label="Select columns to show",
                            elem_id="column-select",
                            interactive=True,
                        )
                    with gr.Row():
                        deleted_models_visibility = gr.Checkbox(
                            value=False, label="Show gated/private/deleted models", interactive=True
                        )

                    with gr.Row():
                        filter_recent7 = gr.Checkbox(
                            value=False, label="Recent (7 days)", interactive=True
                        )
                        filter_recent14 = gr.Checkbox(
                            value=False, label="Recent (14 days)", interactive=True
                        )
                        filter_recent21 = gr.Checkbox(
                            value=False, label="Recent (21 days)", interactive=True
                        )
                with gr.Column(min_width=320):
                    #with gr.Box(elem_id="box-filter"):
                    filter_columns_type = gr.CheckboxGroup(
                        label="Model types",
                        choices=[t.to_str() for t in ModelType],
                        value=[t.to_str() for t in ModelType],
                        interactive=True,
                        elem_id="filter-columns-type",
                    )
                    filter_columns_precision = gr.CheckboxGroup(
                        label="Precision",
                        choices=[i.value.name for i in Precision],
                        value=[i.value.name for i in Precision],
                        interactive=True,
                        elem_id="filter-columns-precision",
                    )
                    filter_columns_size = gr.CheckboxGroup(
                        label="Model sizes (in billions of parameters)",
                        choices=list(NUMERIC_INTERVALS.keys()),
                        value=list(NUMERIC_INTERVALS.keys()),
                        interactive=True,
                        elem_id="filter-columns-size",
                    )

            leaderboard_table = gr.components.Dataframe(
                value=leaderboard_df[
                    [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
                    + shown_columns.value
                    + [AutoEvalColumn.dummy.name]
                ],
                headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
                datatype=TYPES,
                elem_id="leaderboard-table",
                interactive=False,
                visible=True,
                column_widths=["2%", "33%"] 
            )

            # Dummy leaderboard for handling the case when the user uses backspace key
            hidden_leaderboard_table_for_search = gr.components.Dataframe(
                value=original_df[COLS],
                headers=COLS,
                datatype=TYPES,
                visible=False,
            )
            search_bar.submit(
                update_table,
                [
                    hidden_leaderboard_table_for_search,
                    shown_columns,
                    filter_columns_type,
                    filter_columns_precision,
                    filter_columns_size,
                    deleted_models_visibility,
                    search_bar,
                    filter_recent7,
                    filter_recent14,
                    filter_recent21,
                ],
                leaderboard_table,
            )
            for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility, filter_recent7, filter_recent14, filter_recent21]:
                selector.change(
                    update_table,
                    [
                        hidden_leaderboard_table_for_search,
                        shown_columns,
                        filter_columns_type,
                        filter_columns_precision,
                        filter_columns_size,
                        deleted_models_visibility,
                        search_bar,
                        filter_recent7,
                        filter_recent14,
                        filter_recent21,
                    ],
                    leaderboard_table,
                    queue=True,
                )

        with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")

        with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
            with gr.Column():
                with gr.Row():
                    gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")

                with gr.Column():
                    with gr.Accordion(
                        f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
                        open=False,
                    ):
                        with gr.Row():
                            finished_eval_table = gr.components.Dataframe(
                                value=finished_eval_queue_df,
                                headers=EVAL_COLS,
                                datatype=EVAL_TYPES,
                                row_count=5,
                            )
                    with gr.Accordion(
                        f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})",
                        open=False,
                    ):
                        with gr.Row():
                            running_eval_table = gr.components.Dataframe(
                                value=running_eval_queue_df,
                                headers=EVAL_COLS,
                                datatype=EVAL_TYPES,
                                row_count=5,
                            )

                    with gr.Accordion(
                        f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
                        open=False,
                    ):
                        with gr.Row():
                            pending_eval_table = gr.components.Dataframe(
                                value=pending_eval_queue_df,
                                headers=EVAL_COLS,
                                datatype=EVAL_TYPES,
                                row_count=5,
                            )
            with gr.Row():
                gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")

            with gr.Row():
                with gr.Column():
                    model_name_textbox = gr.Textbox(label="Model name")
                    revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
                    model_type = gr.Dropdown(
                        choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
                        label="Model type",
                        multiselect=False,
                        value=None,
                        interactive=True,
                    )

                with gr.Column():
                    precision = gr.Dropdown(
                        choices=[i.value.name for i in Precision if i != Precision.Unknown],
                        label="Precision",
                        multiselect=False,
                        value="float16",
                        interactive=True,
                    )
                    weight_type = gr.Dropdown(
                        choices=[i.value.name for i in WeightType],
                        label="Weights type",
                        multiselect=False,
                        value="Original",
                        interactive=True,
                    )
                    base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")

            submit_button = gr.Button("Submit Eval")
            submission_result = gr.Markdown()
            submit_button.click(
                add_new_eval,
                [
                    model_name_textbox,
                    base_model_name_textbox,
                    revision_name_textbox,
                    precision,
                    weight_type,
                    model_type,
                ],
                submission_result,
            )

            
    # blz --------------------        
    with gr.Row():
        gr.Button("Download", link="/file=output/results.csv")
    # end blz -------------------
            
    with gr.Row():
        with gr.Accordion("📙 Citation", open=False):
            citation_button = gr.Textbox(
                value=CITATION_BUTTON_TEXT,
                label=CITATION_BUTTON_LABEL,
                lines=20,
                elem_id="citation-button",
                show_copy_button=True,
            )


# blz ---
#import os
#os.makedirs('output', exist_ok=True)
#with open('output/results.csv', 'w') as f: 
#    f.write('hello world')

print(f"app start time {time.time() - app_start_time:.2f} seconds")
# blz end ----


scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch(allowed_paths=["output/"]) # blz allowed paths

print(f"app start after demo.queue {time.time() - app_start_time:.2f} seconds")