yourbench_space/app.py

import asyncio
import os
import sys
import time
import gradio as gr
import uuid

from datasets import load_dataset
from huggingface_hub import whoami
from loguru import logger
from pathlib import Path

from yourbench_space.config import generate_and_save_config
from yourbench_space.utils import (
    SubprocessManagerGroup,
    save_files,
    update_dataset,
    STAGES,
)
from yourbench_space.evaluation import create_eval_file, run_evaluations
from yourbench_space.leaderboard_space.env import HF_TOKEN

project_description = """
# YourBench 🚀  
**Dynamic Benchmark Generation for Language Models**

Quickly create zero-shot benchmarks from your documents – keeping models accurate and adaptable
- 📖 [FAQ](#) 
- 💻 [GitHub](https://github.com/huggingface/yourbench/tree/v0.2-alpha-space)
"""

logger.remove()
logger.add(sys.stderr, level="INFO")

# Global to store all managers per session
MANAGERS = SubprocessManagerGroup()

docs_path = Path(__file__).parent / "docs.md"
citation_content = (
    docs_path.read_text().split("# Citation")[-1].strip()
    if docs_path.exists()
    else "# Citation\n\nDocumentation file not found."
)

def generate_and_return(hf_org, hf_dataset_name, session_state: gr.State):
    manager = MANAGERS.get(session_state.value)
    config_path = generate_and_save_config(hf_org, hf_dataset_name, session_state.value, manager.config_path)
    for _ in range(5):
        time.sleep(0.5)
        if config_path.exists():
            return (
                "✅ Config saved!",
                gr.update(value=str(config_path), visible=True, interactive=True),
            )
    return (
        "❌ Config generation failed.",
        gr.update(visible=False, interactive=False),
    )

final_dataset = None

def update_process_status(session_state: gr.State):
    """Update process status and include exit details if process has terminated"""
    manager = MANAGERS.get(session_state.value)

    is_running = manager.is_running()
    
    if not is_running:
        exit_code, exit_reason = manager.get_exit_details()
        status_text = f"Process Status: Stopped - {exit_reason}, exit code - {exit_code}" if exit_reason else "Process Status: Stopped"
        return gr.update(value=False, label=status_text)
    
    return gr.update(value=True, label="Process Status: Running")

def prepare_task(session_state: gr.State, oauth_token: gr.OAuthToken | None, hf_dataset_name: str, _=None):
    manager = MANAGERS.get(session_state.value)
    new_env = os.environ.copy()
    if oauth_token:
        new_env["HF_TOKEN"] = oauth_token.token
    new_env["DATASET_PREFIX"] = hf_dataset_name
    manager.start_process(custom_env=new_env)


def update_hf_org_dropdown(oauth_token: gr.OAuthToken | None):
    if oauth_token is None:
        return gr.Dropdown([], label="Organization")

    try:
        user_info = whoami(oauth_token.token)
        org_names = [org["name"] for org in user_info.get("orgs", [])]
        user_name = user_info.get("name", "Unknown User")
        org_names.insert(0, user_name)
        return gr.Dropdown(org_names, value=user_name, label="Organization")

    except Exception as e:
        return gr.Dropdown([], label="Organization")


def switch_to_run_generation_tab():
    return gr.Tabs(selected=1)


def enable_button(files):
    return gr.update(interactive=bool(files))

def run_evaluation_pipeline(oauth_token: gr.OAuthToken | None, org_name, eval_name):
    # Test dataset existence
    eval_ds_name = f"{org_name}/{eval_name}"
    # Test dataset existence
    try:
        load_dataset(eval_ds_name, streaming=True)
    except Exception as e:
        print(f"Error while loading the dataset: {e}")
        return
    # Run evaluations
    create_eval_file(eval_ds_name)
    status = asyncio.run(run_evaluations(eval_ds_name=eval_ds_name, org=org_name))
    # Create space
    from huggingface_hub import HfApi
    repo_id = f"{org_name}/leaderboard_yourbench_{eval_ds_name.replace('/', '_')}"
    api = HfApi()

    try:
        api.create_repo(repo_id=repo_id, repo_type="space", space_sdk="gradio", token=oauth_token.token)
        api.upload_folder(repo_id=repo_id, repo_type="space", folder_path="src/", token=oauth_token.token)
        api.add_space_secret(repo_id=repo_id, key="HF_TOKEN", value=oauth_token.token, token=oauth_token.token)
        api.add_space_variable(repo_id=repo_id, key="TASK", value=eval_ds_name, token=oauth_token.token)
        api.add_space_variable(repo_id=repo_id, key="ORG_NAME", value=org_name, token=oauth_token.token)
    except Exception as e:
        status = "Evaluation" + status + "\nLeaderboard creation:" + e
    return status


with gr.Blocks(theme=gr.themes.Default()) as app:
    # We initialize the session state with the user randomly generated uuid
    # Using uuid4 makes collision cases extremely unlikely even for concurrent users
    session_state = gr.State(uuid.uuid4(), delete_callback=lambda uid: MANAGERS.remove(uid))
    MANAGERS.create(session_state.value)

    gr.Markdown(project_description)

    with gr.Tabs() as tabs:
        with gr.Tab("Setup", id=0):
            with gr.Row():
                with gr.Accordion("Hugging Face Settings"):
                    login_btn = gr.LoginButton()
                    hf_org_dropdown = gr.Dropdown(
                        choices=[], label="Organization", allow_custom_value=True
                    )
                    app.load(
                        update_hf_org_dropdown, inputs=None, outputs=hf_org_dropdown
                    )

                    hf_dataset_name = gr.Textbox(
                        label="Dataset name",
                        value="yourbench",
                        info="Name of your new evaluation dataset",
                    )

                with gr.Accordion("Upload documents"):
                    file_input = gr.File(
                        label="Upload text files",
                        file_count="multiple",
                        file_types=[".txt", ".md", ".html", ".pdf"],
                    )
                    output = gr.Textbox(label="Log")
                    file_input.upload(
                        lambda files: save_files(session_state, [file.name for file in files]),
                        file_input,
                        output,
                    )

            preview_button = gr.Button("Generate New Config", interactive=False)
            log_message = gr.Textbox(label="Log Message", visible=True)
            download_button = gr.File(
                label="Download Config", visible=False, interactive=False
            )

            file_input.change(enable_button, inputs=file_input, outputs=preview_button)

            preview_button.click(
                generate_and_return,
                inputs=[hf_org_dropdown, hf_dataset_name, session_state],
                outputs=[log_message, download_button],
            )
            preview_button.click(
                switch_to_run_generation_tab,
                inputs=None,
                outputs=tabs,
            )
            
        with gr.Tab("Run Generation", id=1):
            with gr.Row():
                start_button = gr.Button("Start Task")
                start_button.click(prepare_task, inputs=[session_state, login_btn, hf_dataset_name])

                stop_button = gr.Button("Stop Task")
                stop_button.click(MANAGERS.stop_process, inputs=session_state)

                kill_button = gr.Button("Kill Task")
                kill_button.click(MANAGERS.kill_process, inputs=session_state)


            with gr.Row():
                with gr.Column():
                    with gr.Accordion("Log Output", open=True):
                        log_output = gr.Code(language=None, lines=20, interactive=False)

                    process_status = gr.Checkbox(label="Process Status", interactive=False)
                    status_timer = gr.Timer(1.0, active=True)
                    status_timer.tick(update_process_status, inputs=session_state, outputs=process_status)

                with gr.Column():
                    with gr.Accordion("Stages", open=True):
                        stages_table = gr.CheckboxGroup(
                            choices=STAGES,
                            value=[],
                            label="Pipeline Stages Completed",
                            interactive=False,
                        )

                    with gr.Accordion("Ingestion"):
                        ingestion_df = gr.DataFrame()
                        
                    with gr.Accordion("Summarization"):
                        summarization_df = gr.DataFrame()
                        
                    with gr.Accordion("Single-Hop"):
                        single_hop = gr.DataFrame()

                    with gr.Accordion("Answer Generation"):
                        answers_df = gr.DataFrame()
                
                    stages_table.change(
                        update_dataset, inputs=[stages_table, hf_org_dropdown, hf_dataset_name], outputs=[ingestion_df, summarization_df, single_hop, answers_df]
                    )

            log_timer = gr.Timer(1.0, active=True)
            log_timer.tick(
                MANAGERS.read_and_get_output, inputs=session_state, outputs=[log_output, stages_table]
            )
        with gr.Tab("Evaluate", id=2):
            with gr.Row():
                btn_launch_evals = gr.Button("Launch evaluations")
                status = gr.Textbox(label="Status")

            btn_launch_evals.click(run_evaluation_pipeline, [hf_org_dropdown, hf_dataset_name], status)


app.launch(allowed_paths=["/app"])