ui.py

import os
import re
import shutil
from typing import Any

import gradio as gr
import huggingface_hub as hf
import numpy as np
import pandas as pd

HfApi = hf.HfApi()

try:
    import trackio.utils as utils
    from trackio.file_storage import FileStorage
    from trackio.media import TrackioImage
    from trackio.sqlite_storage import SQLiteStorage
    from trackio.typehints import LogEntry, UploadEntry
except:  # noqa: E722
    import utils
    from file_storage import FileStorage
    from media import TrackioImage
    from sqlite_storage import SQLiteStorage
    from typehints import LogEntry, UploadEntry


def get_project_info() -> str | None:
    dataset_id = os.environ.get("TRACKIO_DATASET_ID")
    space_id = os.environ.get("SPACE_ID")
    persistent_storage_enabled = os.environ.get(
        "PERSISTANT_STORAGE_ENABLED"
    )  # Space env name has a typo
    if persistent_storage_enabled:
        return "✨ Persistent Storage is enabled, logs are stored directly in this Space."
    if dataset_id:
        sync_status = utils.get_sync_status(SQLiteStorage.get_scheduler())
        upgrade_message = f"New changes are synced every 5 min <span class='info-container'><input type='checkbox' class='info-checkbox' id='upgrade-info'><label for='upgrade-info' class='info-icon'>&#9432;</label><span class='info-expandable'> To avoid losing data between syncs, <a href='https://huggingface.co/spaces/{space_id}/settings' class='accent-link'>click here</a> to open this Space's settings and add Persistent Storage.</span></span>"
        if sync_status is not None:
            info = f"&#x21bb; Backed up {sync_status} min ago to <a href='https://huggingface.co/datasets/{dataset_id}' target='_blank' class='accent-link'>{dataset_id}</a> | {upgrade_message}"
        else:
            info = f"&#x21bb; Not backed up yet to <a href='https://huggingface.co/datasets/{dataset_id}' target='_blank' class='accent-link'>{dataset_id}</a> | {upgrade_message}"
        return info
    return None


def get_projects(request: gr.Request):
    projects = SQLiteStorage.get_projects()
    if project := request.query_params.get("project"):
        interactive = False
    else:
        interactive = True
        project = projects[0] if projects else None

    return gr.Dropdown(
        label="Project",
        choices=projects,
        value=project,
        allow_custom_value=True,
        interactive=interactive,
        info=get_project_info(),
    )


def get_runs(project) -> list[str]:
    if not project:
        return []
    return SQLiteStorage.get_runs(project)


def get_available_metrics(project: str, runs: list[str]) -> list[str]:
    """Get all available metrics across all runs for x-axis selection."""
    if not project or not runs:
        return ["step", "time"]

    all_metrics = set()
    for run in runs:
        metrics = SQLiteStorage.get_logs(project, run)
        if metrics:
            df = pd.DataFrame(metrics)
            numeric_cols = df.select_dtypes(include="number").columns
            numeric_cols = [c for c in numeric_cols if c not in utils.RESERVED_KEYS]
            all_metrics.update(numeric_cols)

    all_metrics.add("step")
    all_metrics.add("time")

    sorted_metrics = utils.sort_metrics_by_prefix(list(all_metrics))

    result = ["step", "time"]
    for metric in sorted_metrics:
        if metric not in result:
            result.append(metric)

    return result


def extract_images(logs: list[dict]) -> dict[str, list[TrackioImage]]:
    image_data = {}
    logs = sorted(logs, key=lambda x: x.get("step", 0))
    for log in logs:
        for key, value in log.items():
            if isinstance(value, dict) and value.get("_type") == TrackioImage.TYPE:
                if key not in image_data:
                    image_data[key] = []
                try:
                    image_data[key].append(TrackioImage._from_dict(value))
                except Exception as e:
                    print(f"Image not currently available: {key}: {e}")
    return image_data


def load_run_data(
    project: str | None,
    run: str | None,
    smoothing: bool,
    x_axis: str,
    log_scale: bool = False,
) -> tuple[pd.DataFrame, dict]:
    if not project or not run:
        return None, None

    logs = SQLiteStorage.get_logs(project, run)
    if not logs:
        return None, None

    images = extract_images(logs)
    df = pd.DataFrame(logs)

    if "step" not in df.columns:
        df["step"] = range(len(df))

    if x_axis == "time" and "timestamp" in df.columns:
        df["timestamp"] = pd.to_datetime(df["timestamp"])
        first_timestamp = df["timestamp"].min()
        df["time"] = (df["timestamp"] - first_timestamp).dt.total_seconds()
        x_column = "time"
    elif x_axis == "step":
        x_column = "step"
    else:
        x_column = x_axis

    if log_scale and x_column in df.columns:
        x_vals = df[x_column]
        if (x_vals <= 0).any():
            df[x_column] = np.log10(np.maximum(x_vals, 0) + 1)
        else:
            df[x_column] = np.log10(x_vals)

    if smoothing:
        numeric_cols = df.select_dtypes(include="number").columns
        numeric_cols = [c for c in numeric_cols if c not in utils.RESERVED_KEYS]

        df_original = df.copy()
        df_original["run"] = f"{run}_original"
        df_original["data_type"] = "original"

        df_smoothed = df.copy()
        window_size = max(3, min(10, len(df) // 10))  # Adaptive window size
        df_smoothed[numeric_cols] = (
            df_smoothed[numeric_cols]
            .rolling(window=window_size, center=True, min_periods=1)
            .mean()
        )
        df_smoothed["run"] = f"{run}_smoothed"
        df_smoothed["data_type"] = "smoothed"

        combined_df = pd.concat([df_original, df_smoothed], ignore_index=True)
        combined_df["x_axis"] = x_column
        return combined_df, images
    else:
        df["run"] = run
        df["data_type"] = "original"
        df["x_axis"] = x_column
        return df, images


def update_runs(project, filter_text, user_interacted_with_runs=False):
    if project is None:
        runs = []
        num_runs = 0
    else:
        runs = get_runs(project)
        num_runs = len(runs)
        if filter_text:
            runs = [r for r in runs if filter_text in r]
    if not user_interacted_with_runs:
        return gr.CheckboxGroup(choices=runs, value=runs), gr.Textbox(
            label=f"Runs ({num_runs})"
        )
    else:
        return gr.CheckboxGroup(choices=runs), gr.Textbox(label=f"Runs ({num_runs})")


def filter_runs(project, filter_text):
    runs = get_runs(project)
    runs = [r for r in runs if filter_text in r]
    return gr.CheckboxGroup(choices=runs, value=runs)


def update_x_axis_choices(project, runs):
    """Update x-axis dropdown choices based on available metrics."""
    available_metrics = get_available_metrics(project, runs)
    return gr.Dropdown(
        label="X-axis",
        choices=available_metrics,
        value="step",
    )


def toggle_timer(cb_value):
    if cb_value:
        return gr.Timer(active=True)
    else:
        return gr.Timer(active=False)


def check_auth(hf_token: str | None) -> None:
    if os.getenv("SYSTEM") == "spaces":  # if we are running in Spaces
        # check auth token passed in
        if hf_token is None:
            raise PermissionError(
                "Expected a HF_TOKEN to be provided when logging to a Space"
            )
        who = HfApi.whoami(hf_token)
        access_token = who["auth"]["accessToken"]
        owner_name = os.getenv("SPACE_AUTHOR_NAME")
        repo_name = os.getenv("SPACE_REPO_NAME")
        # make sure the token user is either the author of the space,
        # or is a member of an org that is the author.
        orgs = [o["name"] for o in who["orgs"]]
        if owner_name != who["name"] and owner_name not in orgs:
            raise PermissionError(
                "Expected the provided hf_token to be the user owner of the space, or be a member of the org owner of the space"
            )
        # reject fine-grained tokens without specific repo access
        if access_token["role"] == "fineGrained":
            matched = False
            for item in access_token["fineGrained"]["scoped"]:
                if (
                    item["entity"]["type"] == "space"
                    and item["entity"]["name"] == f"{owner_name}/{repo_name}"
                    and "repo.write" in item["permissions"]
                ):
                    matched = True
                    break
                if (
                    (
                        item["entity"]["type"] == "user"
                        or item["entity"]["type"] == "org"
                    )
                    and item["entity"]["name"] == owner_name
                    and "repo.write" in item["permissions"]
                ):
                    matched = True
                    break
            if not matched:
                raise PermissionError(
                    "Expected the provided hf_token with fine grained permissions to provide write access to the space"
                )
        # reject read-only tokens
        elif access_token["role"] != "write":
            raise PermissionError(
                "Expected the provided hf_token to provide write permissions"
            )


def upload_db_to_space(
    project: str, uploaded_db: gr.FileData, hf_token: str | None
) -> None:
    check_auth(hf_token)
    db_project_path = SQLiteStorage.get_project_db_path(project)
    if os.path.exists(db_project_path):
        raise gr.Error(
            f"Trackio database file already exists for project {project}, cannot overwrite."
        )
    os.makedirs(os.path.dirname(db_project_path), exist_ok=True)
    shutil.copy(uploaded_db["path"], db_project_path)


def bulk_upload_media(uploads: list[UploadEntry], hf_token: str | None) -> None:
    check_auth(hf_token)
    for upload in uploads:
        media_path = FileStorage.init_project_media_path(
            upload["project"], upload["run"], upload["step"]
        )
        shutil.copy(upload["uploaded_file"]["path"], media_path)


def log(
    project: str,
    run: str,
    metrics: dict[str, Any],
    step: int | None,
    hf_token: str | None,
) -> None:
    check_auth(hf_token)
    SQLiteStorage.log(project=project, run=run, metrics=metrics, step=step)


def bulk_log(
    logs: list[LogEntry],
    hf_token: str | None,
) -> None:
    check_auth(hf_token)

    logs_by_run = {}
    for log_entry in logs:
        key = (log_entry["project"], log_entry["run"])
        if key not in logs_by_run:
            logs_by_run[key] = {"metrics": [], "steps": []}
        logs_by_run[key]["metrics"].append(log_entry["metrics"])
        logs_by_run[key]["steps"].append(log_entry.get("step"))

    for (project, run), data in logs_by_run.items():
        SQLiteStorage.bulk_log(
            project=project,
            run=run,
            metrics_list=data["metrics"],
            steps=data["steps"],
        )


def filter_metrics_by_regex(metrics: list[str], filter_pattern: str) -> list[str]:
    """
    Filter metrics using regex pattern.

    Args:
        metrics: List of metric names to filter
        filter_pattern: Regex pattern to match against metric names

    Returns:
        List of metric names that match the pattern
    """
    if not filter_pattern.strip():
        return metrics

    try:
        pattern = re.compile(filter_pattern, re.IGNORECASE)
        return [metric for metric in metrics if pattern.search(metric)]
    except re.error:
        return [
            metric for metric in metrics if filter_pattern.lower() in metric.lower()
        ]


def configure(request: gr.Request):
    sidebar_param = request.query_params.get("sidebar")
    match sidebar_param:
        case "collapsed":
            sidebar = gr.Sidebar(open=False, visible=True)
        case "hidden":
            sidebar = gr.Sidebar(open=False, visible=False)
        case _:
            sidebar = gr.Sidebar(open=True, visible=True)

    if metrics := request.query_params.get("metrics"):
        return metrics.split(","), sidebar
    else:
        return [], sidebar


def create_image_section(images_by_run: dict[str, dict[str, list[TrackioImage]]]):
    with gr.Accordion(label="media"):
        with gr.Group(elem_classes=("media-group")):
            for run, images_by_key in images_by_run.items():
                with gr.Tab(label=run, elem_classes=("media-tab")):
                    for key, images in images_by_key.items():
                        gr.Gallery(
                            [(image._pil, image.caption) for image in images],
                            label=key,
                            columns=6,
                            elem_classes=("media-gallery"),
                        )


css = """
#run-cb .wrap { gap: 2px; }
#run-cb .wrap label {
    line-height: 1;
    padding: 6px;
}
.logo-light { display: block; } 
.logo-dark { display: none; }
.dark .logo-light { display: none; }
.dark .logo-dark { display: block; }
.dark .caption-label { color: white; }

.info-container {
    position: relative;
    display: inline;
}
.info-checkbox {
    position: absolute;
    opacity: 0;
    pointer-events: none;
}
.info-icon {
    border-bottom: 1px dotted;
    cursor: pointer;
    user-select: none;
    color: var(--color-accent);
}
.info-expandable {
    display: none;
    opacity: 0;
    transition: opacity 0.2s ease-in-out;
}
.info-checkbox:checked ~ .info-expandable {
    display: inline;
    opacity: 1;
}
.info-icon:hover { opacity: 0.8; }
.accent-link { font-weight: bold; }

.media-gallery { max-height: 325px; }
.media-group, .media-group > div { background: none; }
.media-group .tabs { padding: 0.5em; }
"""

with gr.Blocks(theme="citrus", title="Trackio Dashboard", css=css) as demo:
    with gr.Sidebar(open=False) as sidebar:
        logo = gr.Markdown(
            f"""
                <img src='/gradio_api/file={utils.TRACKIO_LOGO_DIR}/trackio_logo_type_light_transparent.png' width='80%' class='logo-light'>
                <img src='/gradio_api/file={utils.TRACKIO_LOGO_DIR}/trackio_logo_type_dark_transparent.png' width='80%' class='logo-dark'>            
            """
        )
        project_dd = gr.Dropdown(label="Project", allow_custom_value=True)
        run_tb = gr.Textbox(label="Runs", placeholder="Type to filter...")
        run_cb = gr.CheckboxGroup(
            label="Runs", choices=[], interactive=True, elem_id="run-cb"
        )
        gr.HTML("<hr>")
        realtime_cb = gr.Checkbox(label="Refresh metrics realtime", value=True)
        smoothing_cb = gr.Checkbox(label="Smooth metrics", value=True)
        x_axis_dd = gr.Dropdown(
            label="X-axis",
            choices=["step", "time"],
            value="step",
        )
        log_scale_cb = gr.Checkbox(label="Log scale X-axis", value=False)
        metric_filter_tb = gr.Textbox(
            label="Metric Filter (regex)",
            placeholder="e.g., loss|ndcg@10|gpu",
            value="",
            info="Filter metrics using regex patterns. Leave empty to show all metrics.",
        )

    timer = gr.Timer(value=1)
    metrics_subset = gr.State([])
    user_interacted_with_run_cb = gr.State(False)

    gr.on([demo.load], fn=configure, outputs=[metrics_subset, sidebar])
    gr.on(
        [demo.load],
        fn=get_projects,
        outputs=project_dd,
        show_progress="hidden",
    )
    gr.on(
        [timer.tick],
        fn=update_runs,
        inputs=[project_dd, run_tb, user_interacted_with_run_cb],
        outputs=[run_cb, run_tb],
        show_progress="hidden",
    )
    gr.on(
        [timer.tick],
        fn=lambda: gr.Dropdown(info=get_project_info()),
        outputs=[project_dd],
        show_progress="hidden",
    )
    gr.on(
        [demo.load, project_dd.change],
        fn=update_runs,
        inputs=[project_dd, run_tb],
        outputs=[run_cb, run_tb],
        show_progress="hidden",
    )
    gr.on(
        [demo.load, project_dd.change, run_cb.change],
        fn=update_x_axis_choices,
        inputs=[project_dd, run_cb],
        outputs=x_axis_dd,
        show_progress="hidden",
    )

    realtime_cb.change(
        fn=toggle_timer,
        inputs=realtime_cb,
        outputs=timer,
        api_name="toggle_timer",
    )
    run_cb.input(
        fn=lambda: True,
        outputs=user_interacted_with_run_cb,
    )
    run_tb.input(
        fn=filter_runs,
        inputs=[project_dd, run_tb],
        outputs=run_cb,
    )

    gr.api(
        fn=upload_db_to_space,
        api_name="upload_db_to_space",
    )
    gr.api(
        fn=bulk_upload_media,
        api_name="bulk_upload_media",
    )
    gr.api(
        fn=log,
        api_name="log",
    )
    gr.api(
        fn=bulk_log,
        api_name="bulk_log",
    )

    x_lim = gr.State(None)
    last_steps = gr.State({})

    def update_x_lim(select_data: gr.SelectData):
        return select_data.index

    def update_last_steps(project, runs):
        """Update the last step from all runs to detect when new data is available."""
        if not project or not runs:
            return {}

        return SQLiteStorage.get_max_steps_for_runs(project, runs)

    timer.tick(
        fn=update_last_steps,
        inputs=[project_dd, run_cb],
        outputs=last_steps,
        show_progress="hidden",
    )

    @gr.render(
        triggers=[
            demo.load,
            run_cb.change,
            last_steps.change,
            smoothing_cb.change,
            x_lim.change,
            x_axis_dd.change,
            log_scale_cb.change,
            metric_filter_tb.change,
        ],
        inputs=[
            project_dd,
            run_cb,
            smoothing_cb,
            metrics_subset,
            x_lim,
            x_axis_dd,
            log_scale_cb,
            metric_filter_tb,
        ],
        show_progress="hidden",
    )
    def update_dashboard(
        project,
        runs,
        smoothing,
        metrics_subset,
        x_lim_value,
        x_axis,
        log_scale,
        metric_filter,
    ):
        dfs = []
        images_by_run = {}
        original_runs = runs.copy()

        for run in runs:
            df, images_by_key = load_run_data(
                project, run, smoothing, x_axis, log_scale
            )
            if df is not None:
                dfs.append(df)
                images_by_run[run] = images_by_key
        if dfs:
            master_df = pd.concat(dfs, ignore_index=True)
        else:
            master_df = pd.DataFrame()

        if master_df.empty:
            return

        x_column = "step"
        if dfs and not dfs[0].empty and "x_axis" in dfs[0].columns:
            x_column = dfs[0]["x_axis"].iloc[0]

        numeric_cols = master_df.select_dtypes(include="number").columns
        numeric_cols = [c for c in numeric_cols if c not in utils.RESERVED_KEYS]
        if metrics_subset:
            numeric_cols = [c for c in numeric_cols if c in metrics_subset]

        if metric_filter and metric_filter.strip():
            numeric_cols = filter_metrics_by_regex(list(numeric_cols), metric_filter)

        nested_metric_groups = utils.group_metrics_with_subprefixes(list(numeric_cols))
        color_map = utils.get_color_mapping(original_runs, smoothing)

        metric_idx = 0
        for group_name in sorted(nested_metric_groups.keys()):
            group_data = nested_metric_groups[group_name]

            with gr.Accordion(
                label=group_name,
                open=True,
                key=f"accordion-{group_name}",
                preserved_by_key=["value", "open"],
            ):
                # Render direct metrics at this level
                if group_data["direct_metrics"]:
                    with gr.Row(key=f"row-{group_name}-direct"):
                        for metric_name in group_data["direct_metrics"]:
                            metric_df = master_df.dropna(subset=[metric_name])
                            color = "run" if "run" in metric_df.columns else None
                            if not metric_df.empty:
                                plot = gr.LinePlot(
                                    utils.downsample(
                                        metric_df,
                                        x_column,
                                        metric_name,
                                        color,
                                        x_lim_value,
                                    ),
                                    x=x_column,
                                    y=metric_name,
                                    y_title=metric_name.split("/")[-1],
                                    color=color,
                                    color_map=color_map,
                                    title=metric_name,
                                    key=f"plot-{metric_idx}",
                                    preserved_by_key=None,
                                    x_lim=x_lim_value,
                                    show_fullscreen_button=True,
                                    min_width=400,
                                )
                                plot.select(
                                    update_x_lim,
                                    outputs=x_lim,
                                    key=f"select-{metric_idx}",
                                )
                                plot.double_click(
                                    lambda: None,
                                    outputs=x_lim,
                                    key=f"double-{metric_idx}",
                                )
                            metric_idx += 1

                # If there are subgroups, create nested accordions
                if group_data["subgroups"]:
                    for subgroup_name in sorted(group_data["subgroups"].keys()):
                        subgroup_metrics = group_data["subgroups"][subgroup_name]

                        with gr.Accordion(
                            label=subgroup_name,
                            open=True,
                            key=f"accordion-{group_name}-{subgroup_name}",
                            preserved_by_key=["value", "open"],
                        ):
                            with gr.Row(key=f"row-{group_name}-{subgroup_name}"):
                                for metric_name in subgroup_metrics:
                                    metric_df = master_df.dropna(subset=[metric_name])
                                    color = (
                                        "run" if "run" in metric_df.columns else None
                                    )
                                    if not metric_df.empty:
                                        plot = gr.LinePlot(
                                            utils.downsample(
                                                metric_df,
                                                x_column,
                                                metric_name,
                                                color,
                                                x_lim_value,
                                            ),
                                            x=x_column,
                                            y=metric_name,
                                            y_title=metric_name.split("/")[-1],
                                            color=color,
                                            color_map=color_map,
                                            title=metric_name,
                                            key=f"plot-{metric_idx}",
                                            preserved_by_key=None,
                                            x_lim=x_lim_value,
                                            show_fullscreen_button=True,
                                            min_width=400,
                                        )
                                        plot.select(
                                            update_x_lim,
                                            outputs=x_lim,
                                            key=f"select-{metric_idx}",
                                        )
                                        plot.double_click(
                                            lambda: None,
                                            outputs=x_lim,
                                            key=f"double-{metric_idx}",
                                        )
                                    metric_idx += 1
        if images_by_run and any(any(images) for images in images_by_run.values()):
            create_image_section(images_by_run)


if __name__ == "__main__":
    demo.launch(allowed_paths=[utils.TRACKIO_LOGO_DIR], show_api=False, show_error=True)