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open_ita_llm_leaderboard

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

from src.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, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
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)

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


raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
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.model.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] 
    ]
    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

def get_data_totale():
    dataset = pd.read_csv("mmlu_pro_it.csv", sep=',')
    if 'model ' in dataset.columns:
        dataset.rename(columns={'model ': 'model'}, inplace=True)
    return dataset


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
                            ],
                            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.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
                ],
                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,
            )

            # 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,
                ],
                leaderboard_table,
            )
            for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility]:
                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,
                    ],
                    leaderboard_table,
                    queue=True,
                )
        
        # with gr.TabItem('Classifica RAG'):

        #         gr.Markdown('''# Classifica RAG degli LLM italiani''')
        #         gr.Markdown(f'''In questa sezione i modelli sono valutati su dei task di Q&A e ordinati per F1 Score e EM (Exact Match). La repo di riferimento è [questa](https://github.com/C080/open-llm-ita-leaderboard).
        #                     I modelli in cima alla classifica sono ritenuti preferibili per i task di Retrieval Augmented Generation.''')
        #         gr.Dataframe(pd.read_csv('leaderboard.csv', sep=';'))
        #         gr.Markdown(f"Si ringrazia il @galatolo per il codice dell'eval.")
                

        with gr.TabItem('Eval aggiuntive'):

                gr.Markdown('''# Altre  evaluation''')
                gr.Markdown('''* classifica [INVALSI](https://huggingface.co/spaces/Crisp-Unimib/INVALSIbenchmark) gestita dai nostri amici del [CRISP](https://crispresearch.it/)''') 
                gr.Markdown('''* analisi dei modelli fatti da ita su [mmlu pro it](https://huggingface.co/datasets/efederici/MMLU-Pro-ita)''')
                gr.Dataframe(get_data_totale) 
                

        
        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,
            )

    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,
            )

scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch()


# import gradio as gr
# import pandas as pd

# csv_filename = 'leaderboard.csv'
# # url = 'https://docs.google.com/spreadsheets/d/1Oh3nrbdWjKuh9twJsc9yJLppiJeD_BZyKgCTOxRkALM/export?format=csv'

# def get_data_classifica():
#     dataset = pd.read_csv("leaderboard_general.csv", sep=',')
#     if 'model ' in dataset.columns:
#         dataset.rename(columns={'model ': 'model'}, inplace=True)
#     df_classifica = dataset[['model', 'helloswag_it acc norm', 'arc_it acc norm', 'm_mmlu_it acc shot 5']]
#     df_classifica['media'] = df_classifica[['helloswag_it acc norm', 'arc_it acc norm', 'm_mmlu_it acc shot 5']].mean(axis=1)
#     df_classifica['media'] = df_classifica['media'].round(3) 
#     df_classifica = df_classifica.sort_values(by='media', ascending=False) 
#     df_classifica = df_classifica[['model', 'media', 'helloswag_it acc norm', 'arc_it acc norm', 'm_mmlu_it acc shot 5']]

#     return df_classifica

# def get_data_totale():
#     dataset = pd.read_csv("leaderboard_general.csv", sep=',')
#     if 'model ' in dataset.columns:
#         dataset.rename(columns={'model ': 'model'}, inplace=True)
#     return dataset

# with gr.Blocks() as demo:

#         with gr.Tab('Classifica Generale'):

#             gr.Markdown('''# Classifica generale degli LLM italiani''')
#             discord_link = 'https://discord.gg/m7sS3mduY2'
#             gr.Markdown('''
#             I modelli sottostanti sono stati testati con [lm_evaluation_harness](https://github.com/EleutherAI/lm-evaluation-harness) su task specifici per l'italiano introdotti con questa [PR](https://github.com/EleutherAI/lm-evaluation-harness/pull/1358).
#             L'intero progetto, i modelli e i dataset sono rigorosamente open source e tutti i risultati sono riproducibili lanciando i seguenti comandi:
            
#                 ```
#                    lm_eval --model hf --model_args pretrained=HUGGINGFACE_MODEL_ID  --tasks hellaswag_it,arc_it  --device cuda:0 --batch_size auto:2
#                 ```
    
#                 ```
#                    lm_eval --model hf --model_args pretrained=HUGGINGFACE_MODEL_ID  --tasks m_mmlu_it --num_fewshot 5  --device cuda:0 --batch_size auto:2 
#                 ```
#             ''')
#             gr.DataFrame(get_data_classifica, every=3600)            
#             gr.Markdown(f"Contributore principale: @giux78")
#             gr.Markdown('''
#             ### Risultati su modelli "internazionali" (instruct)

#             | Model | Arc-c  | HellaS | MMUL | AVG |
#             | --- | --- | --- | --- | --- |
#             | Mixtral 8x22b | 55.3 | 77.1 | 75.8 | 69.4 |
#             | LLama3 70b | 52.9 | 70.3 | 74.8 | 66.0 |
#             | command-r-plus | 49.5 | 74.9 | 67.6 | 64.0 |
#             | Mixtral 8x7b | 51.1 | 72.9 | 65.9 | 63.3 |
#             | LLama2 70b | 49.4 | 70.9 | 65.1 | 61.8 |
#             | command-r-v01 | 50.8 | 72.3 | 60.0 | 61.0 |
#             | Phi-3-mini | 43.46 | 61.44 | 56.55 | 53.8 |
#             | LLama3 8b | 44.3 | 59.9 | 55.7 | 53.3 |
#             | LLama1 34b | 42.9 | 65.4 | 49.0 | 52.4 |
#             | Mistral 7b | 41.49 | 61.22 | 52.53 | 51.7 |
#             | Gemma 1.1 7b | 41.75 | 54.07 | 49.45 | 48.4 |

#             ''')


#         with gr.Tab('Classifica RAG'):

#             gr.Markdown('''# Classifica RAG degli LLM italiani''')
#             gr.Markdown(f'''In questa sezione i modelli sono valutati su dei task di Q&A e ordinati per F1 Score e EM (Exact Match). La repo di riferimento è [questa](https://github.com/C080/open-llm-ita-leaderboard).
#                         I modelli in cima alla classifica sono ritenuti preferibili per i task di Retrieval Augmented Generation.''')
#             gr.Dataframe(pd.read_csv(csv_filename, sep=';'))
#             gr.Markdown(f"Si ringrazia il @galatolo per il codice dell'eval.")
            

#         with gr.Tab('Eval aggiuntive'):

#             gr.Markdown('''# Altre evaluation''')
#             gr.Markdown('''Qui ci sono altri test di altri modelli, che non sono ancora stati integrati nella classifica generale.''')
#             gr.DataFrame(get_data_totale, every=3600) 

#         with gr.Tab('Informazioni'):
            
#             form_link = "https://forms.gle/Gc9Dfu52xSBhQPpAA"
#             gr.Markdown('''# Community discord
#             Se vuoi contribuire al progetto o semplicemente unirti alla community di LLM italiani unisciti al nostro [discord!](https://discord.gg/m7sS3mduY2)
#             # Aggiungi il tuo modello
#             Se hai sviluppato un tuo modello che vuoi far valutare, compila il form [qui](https://forms.gle/Gc9Dfu52xSBhQPpAA) è tutto gratuito!         
#             ''') 
        
#         with gr.Tab('Sponsor'):

#             gr.Markdown('''
#             # Sponsor
#             Le evaluation della classifica generale sono state gentilmente offerte da un provider cloud italiano [seeweb.it](https://www.seeweb.it/) specializzato in servizi di GPU cloud e AI.
#             ''')
            
# demo.launch()