app.py

import gradio as gr
from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
import os

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,
    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)

# Create directories first
os.makedirs(EVAL_REQUESTS_PATH, exist_ok=True)
os.makedirs(EVAL_RESULTS_PATH, exist_ok=True)

### Space initialisation
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 as e:
    print(f"Error downloading requests: {e}")
    # Initialize with empty directory if download fails
    pass

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 as e:
    print(f"Error downloading results: {e}")
    # Initialize with empty directory if download fails
    pass

# Initialize the leaderboard DataFrame
try:
    LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
except Exception:
    LEADERBOARD_DF = pd.DataFrame(columns=COLS)

# Get evaluation queue status
(
    finished_eval_queue_df,
    running_eval_queue_df,
    pending_eval_queue_df,
) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)

def init_leaderboard(dataframe):
    return Leaderboard(
        value=dataframe,
        datatype=[c.type for c in fields(AutoEvalColumn)],
        select_columns=SelectColumns(
            default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
            cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
            label="Seleccionar columnas:",
        ),
        search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
        filter_columns=[
            ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
            ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
            ColumnFilter(
                AutoEvalColumn.params.name,
                type="slider",
                min=0.01,
                max=150,
                label="Select the number of parameters (B)",
            ),
            ColumnFilter(
                AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True
            ),
        ],
        bool_checkboxgroup_label="Hide models",
        interactive=False,
    )

def submit_handler(model, base_model, revision, precision, weight_type, model_type, submit_type, openrouter_key):
    """Manejador unificado para ambos tipos de submit"""
    return add_new_eval(
        model=model,
        base_model=base_model,
        revision=revision,
        precision=precision,
        weight_type=weight_type,
        model_type=model_type,
        submit_type=submit_type,
        openrouter_key=openrouter_key if submit_type == "openrouter" else None
    )

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):
            leaderboard = init_leaderboard(LEADERBOARD_DF)

        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")

            # Replace Radio with Tabs
            with gr.Tabs() as submit_tabs:
                # Huggingface Tab
                with gr.TabItem("Huggingface") as huggingface_tab:
                    with gr.Row():
                        with gr.Column():
                            hf_model_name_textbox = gr.Textbox(label="Model name")
                            hf_revision_name_textbox = gr.Textbox(
                                label="Revision commit", 
                                placeholder="main"
                            )
                            hf_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():
                            hf_precision = gr.Dropdown(
                                choices=[i.value.name for i in Precision if i != Precision.Unknown],
                                label="Precision",
                                multiselect=False,
                                value="float16",
                                interactive=True,
                            )
                            hf_weight_type = gr.Dropdown(
                                choices=[i.value.name for i in WeightType],
                                label="Weights type",
                                multiselect=False,
                                value="Original",
                                interactive=True,
                            )
                            hf_base_model_name_textbox = gr.Textbox(
                                label="Base model (for delta or adapter weights)"
                            )
                    
                    hf_submit_button = gr.Button("Submit Huggingface Model")
                    hf_submission_result = gr.Markdown()
                
                # OpenRouter Tab  
                with gr.TabItem("OpenRouter") as openrouter_tab:
                    with gr.Row():
                        with gr.Column():
                            or_model_name_textbox = gr.Textbox(
                                label="OpenRouter Model ID"
                            )
                            # Get available themes from EXAM_QUESTIONS
                            from src.evaluation.questions import EXAM_QUESTIONS
                            
                            # Solución para mostrar solo los labels
                            # Creamos un diccionario auxiliar para mapear los nombres visibles a los valores internos
                            theme_label_to_value = {}
                            theme_labels = ["Todos los temas"]  # Lista de solo labels para mostrar
                            theme_values = [None]  # Lista de valores correspondientes (misma posición)
                            
                            # Rellenamos las listas de labels y values en el mismo orden
                            for theme, questions in EXAM_QUESTIONS.items():
                                if questions:
                                    original_theme = questions[0]["theme"]
                                    theme_labels.append(original_theme)  # Solo nombre legible
                                    theme_values.append(theme)  # Valor interno
                                    theme_label_to_value[original_theme] = theme  # Para mapeo
                            
                            # Función para convertir de label a value cuando se selecciona
                            def convert_theme_selection(label):
                                if label == "Todos los temas" or label is None:
                                    return None
                                return theme_label_to_value.get(label)
                            
                            or_theme = gr.Dropdown(
                                choices=theme_labels,  # Solo mostramos los labels
                                label="Tema del examen (opcional, por defecto todos)",
                                multiselect=False,
                                interactive=True,
                                value=None
                            )
                            or_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():
                            or_api_key = gr.Textbox(
                                label="OpenRouter API Key", 
                                type="password"
                            )
                    
                    or_submit_button = gr.Button("Submit OpenRouter Model")
                    or_submission_result = gr.Markdown()
            
            # Replace old submit handler with individual handlers for each tab
            def hf_submit_handler(model, base_model, revision, precision, weight_type, model_type):
                return add_new_eval(
                    model=model,
                    base_model=base_model,
                    revision=revision,
                    precision=precision,
                    weight_type=weight_type,
                    model_type=model_type,
                    submit_type="huggingface",
                    openrouter_key=None
                )
                
            def or_submit_handler(model, model_type, openrouter_key, theme_label=None, progress=gr.Progress()):
                """OpenRouter submission handler with progress indicator"""
                # Convertir el label seleccionado al value correspondiente
                theme_value = convert_theme_selection(theme_label)
                
                # Pass theme as parameter to run_exam function via do_exam.py
                return add_new_eval(
                    model=model,
                    base_model="",
                    revision="openrouter",
                    precision="float16",  # Default for API models
                    weight_type="Original",
                    model_type=model_type,
                    submit_type="openrouter",
                    openrouter_key=openrouter_key,
                    exam_theme=theme_value,  # Pasar el valor interno, no el label
                    progress=progress,  # Añadir el indicador de progreso
                    leaderboard_component=leaderboard  # Pasar la referencia al componente leaderboard
                )
                
            # Connect handlers to buttons
            hf_submit_button.click(
                hf_submit_handler,
                inputs=[
                    hf_model_name_textbox,
                    hf_base_model_name_textbox,
                    hf_revision_name_textbox,
                    hf_precision,
                    hf_weight_type,
                    hf_model_type,
                ],
                outputs=hf_submission_result,
            )
            
            or_submit_button.click(
                or_submit_handler,
                inputs=[
                    or_model_name_textbox,
                    or_model_type,
                    or_api_key,
                    or_theme,
                ],
                outputs=or_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()