backup_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 shutil # For file operations
from pathlib import Path # For path manipulations


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)

### 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:
    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()


LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)

(
    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):
    if dataframe is None or dataframe.empty:
        raise ValueError("Leaderboard DataFrame is empty or None.")
    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="Select Columns to Display:",
        ),
        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,
    )




# --- Function to handle the uploaded directory ---
def save_uploaded_models(files):
    if files:
        saved_paths = []
        # 'files' will be a list of temporary file paths when file_count="directory"
        # The actual files are in a temporary directory.
        # We want to recreate the structure within UPLOAD_DIR.

        # Assuming 'files' contains full paths to files within a single uploaded directory
        # We need to determine the base name of the uploaded directory.
        # Gradio often provides a list of file objects. Each object has a .name attribute (path).
        # Example: if user uploads "my_run_1" containing "model.txt" and "config.json"
        # files might be like: ['/tmp/gradio/somerandomhash/my_run_1/model.txt', '/tmp/gradio/somerandomhash/my_run_1/config.json']
        # Or it might be a list of tempfile._TemporaryFileWrapper objects.

        if not isinstance(files, list):
            files = [files] # Ensure it's a list

        # Let's assume `files` is a list of `tempfile._TemporaryFileWrapper` or similar
        # where `file_obj.name` gives the temporary path to each file.

        # Get the common parent directory from the temporary paths if possible,
        # or derive the uploaded folder name from one of the paths.
        # This part can be tricky depending on exactly how Gradio passes directory uploads.
        # A robust way is to create a unique sub-directory for each upload.

        # Let's get the name of the directory the user uploaded.
        # With file_count="directory", `files` is a list of file paths.
        # We can infer the uploaded directory name from the first file path.
        if files:
            first_file_path = Path(files[0].name if hasattr(files[0], 'name') else files[0])
            # The uploaded directory name would be the parent of the files if Gradio flattens it,
            # or the parent of the temp directory housing the uploaded folder.
            # For simplicity, let's try to get the original uploaded folder name.
            # Gradio's `UploadButton` usually puts uploaded directories into a subdirectory
            # within the temp space that has the same name as the original uploaded directory.
            # e.g., if user uploads "my_models_run1", files might be in /tmp/somehash/my_models_run1/file1.txt

            # A common approach: find the common prefix of all file paths,
            # then determine the uploaded directory's name from that.
            # However, Gradio's behavior is that `files` is a list of file objects,
            # each with a `.name` attribute that is the full path to a temporary file.
            # These temporary files are often placed inside a directory that *itself*
            # represents the uploaded directory structure.

            # Let's assume the user uploaded a directory named "user_uploaded_dir"
            # And it contains "model1.txt" and "model2.txt"
            # `files` might be `[<temp_file_obj_for_model1>, <temp_file_obj_for_model2>]`
            # `files[0].name` might be `/tmp/gradio_guid/user_uploaded_dir/model1.txt`

            # We need to extract "user_uploaded_dir"
            # And then recreate this structure under UPLOAD_DIR.

            # Assuming the first file gives us a good representation of the path structure.
            temp_file_path = Path(files[0].name if hasattr(files[0], 'name') else files[0])
            # The uploaded directory's name is usually the second to last part of the temp path
            # e.g. /tmp/tmpxyz/uploaded_dir_name/file.txt -> "uploaded_dir_name"
            uploaded_dir_name = temp_file_path.parent.name

            destination_folder_path = Path(UPLOAD_DIR) / uploaded_dir_name
            os.makedirs(destination_folder_path, exist_ok=True)

            for uploaded_file_obj in files:
                # Get the path to the temporary file
                temp_path_str = uploaded_file_obj.name
                temp_path = Path(temp_path_str)

                # Get the original filename (relative to the uploaded directory)
                # This should be just the filename itself if Gradio preserves the structure
                # correctly inside the temp directory for the uploaded folder.
                original_filename = temp_path.name # e.g., "model1.txt"

                destination_file_path = destination_folder_path / original_filename

                try:
                    shutil.copy(temp_path_str, destination_file_path)
                    saved_paths.append(str(destination_file_path))
                except Exception as e:
                    print(f"Error copying {temp_path_str} to {destination_file_path}: {e}")
                    return f"Error saving files: {e}"

            if saved_paths:
                return f"Successfully uploaded and saved models to: {destination_folder_path}"
            else:
                return "No files were saved."
        return "No files uploaded."




# demo = gr.Blocks(css=custom_css)
demo = gr.Blocks()

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("🚀 Simple Submit here!", elem_id="llm-benchmark-tab-table", id=4):
            gr.Markdown(
                "## Submit your generated models here!",
                elem_classes="markdown-text",
            )
            upload_button = gr.UploadButton(
                label="Upload your generated models (only directories accepted)",
                size="lg",
                file_count="directory",
                elem_id="upload-button",
            )
            # Add an output component to display the result of the upload
            upload_status = gr.Textbox(label="Upload Status", interactive=False)

            # Connect the upload_button to the save_uploaded_models function
            upload_button.upload(save_uploaded_models, upload_button, upload_status)



        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()