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"""The main page for the Trackio UI."""
import os
import re
import shutil
from dataclasses import dataclass
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, TrackioVideo
from trackio.sqlite_storage import SQLiteStorage
from trackio.table import Table
from trackio.typehints import LogEntry, UploadEntry
from trackio.ui import fns
from trackio.ui.run_detail import run_detail_page
from trackio.ui.runs import run_page
except ImportError:
import utils
from file_storage import FileStorage
from media import TrackioImage, TrackioVideo
from sqlite_storage import SQLiteStorage
from table import Table
from typehints import LogEntry, UploadEntry
from ui import fns
from ui.run_detail import run_detail_page
from ui.runs import run_page
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
@dataclass
class MediaData:
caption: str | None
file_path: str
def extract_media(logs: list[dict]) -> dict[str, list[MediaData]]:
media_by_key: dict[str, list[MediaData]] = {}
logs = sorted(logs, key=lambda x: x.get("step", 0))
for log in logs:
for key, value in log.items():
if isinstance(value, dict):
type = value.get("_type")
if type == TrackioImage.TYPE or type == TrackioVideo.TYPE:
if key not in media_by_key:
media_by_key[key] = []
try:
media_data = MediaData(
file_path=utils.MEDIA_DIR / value.get("file_path"),
caption=value.get("caption"),
)
media_by_key[key].append(media_data)
except Exception as e:
print(f"Media currently unavailable: {key}: {e}")
return media_by_key
def load_run_data(
project: str | None,
run: str | None,
smoothing_granularity: int,
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
media = extract_media(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_granularity > 0:
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"] = run
df_original["data_type"] = "original"
df_smoothed = df.copy()
window_size = max(3, min(smoothing_granularity, len(df)))
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, media
else:
df["run"] = run
df["data_type"] = "original"
df["x_axis"] = x_column
return df, media
def update_runs(
project, filter_text, user_interacted_with_runs=False, selected_runs_from_url=None
):
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:
if selected_runs_from_url:
value = [r for r in runs if r in selected_runs_from_url]
else:
value = runs
return gr.CheckboxGroup(choices=runs, value=value), 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:
"""
Note: this method is not used in the latest versions of Trackio (replaced by bulk_log) but
is kept for backwards compatibility for users who are connecting to a newer version of
a Trackio Spaces dashboard with an older version of Trackio installed locally.
"""
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": [], "config": None}
logs_by_run[key]["metrics"].append(log_entry["metrics"])
logs_by_run[key]["steps"].append(log_entry.get("step"))
if log_entry.get("config") and logs_by_run[key]["config"] is None:
logs_by_run[key]["config"] = log_entry["config"]
for (project, run), data in logs_by_run.items():
SQLiteStorage.bulk_log(
project=project,
run=run,
metrics_list=data["metrics"],
steps=data["steps"],
config=data["config"],
)
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)
metrics_param = request.query_params.get("metrics", "")
runs_param = request.query_params.get("runs", "")
selected_runs = runs_param.split(",") if runs_param else []
navbar_param = request.query_params.get("navbar")
match navbar_param:
case "hidden":
navbar = gr.Navbar(visible=False)
case _:
navbar = gr.Navbar(visible=True)
return [], sidebar, metrics_param, selected_runs, navbar
def create_media_section(media_by_run: dict[str, dict[str, list[MediaData]]]):
with gr.Accordion(label="media"):
with gr.Group(elem_classes=("media-group")):
for run, media_by_key in media_by_run.items():
with gr.Tab(label=run, elem_classes=("media-tab")):
for key, media_item in media_by_key.items():
gr.Gallery(
[(item.file_path, item.caption) for item in media_item],
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 .fixed-height { min-height: 275px; }
.media-group, .media-group > div { background: none; }
.media-group .tabs { padding: 0.5em; }
.media-tab { max-height: 500px; overflow-y: scroll; }
"""
javascript = """
<script>
function setCookie(name, value, days) {
var expires = "";
if (days) {
var date = new Date();
date.setTime(date.getTime() + (days * 24 * 60 * 60 * 1000));
expires = "; expires=" + date.toUTCString();
}
document.cookie = name + "=" + (value || "") + expires + "; path=/; SameSite=Lax";
}
function getCookie(name) {
var nameEQ = name + "=";
var ca = document.cookie.split(';');
for(var i=0;i < ca.length;i++) {
var c = ca[i];
while (c.charAt(0)==' ') c = c.substring(1,c.length);
if (c.indexOf(nameEQ) == 0) return c.substring(nameEQ.length,c.length);
}
return null;
}
(function() {
const urlParams = new URLSearchParams(window.location.search);
const writeToken = urlParams.get('write_token');
if (writeToken) {
setCookie('trackio_write_token', writeToken, 7);
urlParams.delete('write_token');
const newUrl = window.location.pathname +
(urlParams.toString() ? '?' + urlParams.toString() : '') +
window.location.hash;
window.history.replaceState({}, document.title, newUrl);
}
})();
</script>
"""
gr.set_static_paths(paths=[utils.MEDIA_DIR])
with gr.Blocks(title="Trackio Dashboard", css=css, head=javascript) 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)
embed_code = gr.Code(
label="Embed this view",
max_lines=2,
lines=2,
language="html",
visible=bool(os.environ.get("SPACE_HOST")),
)
run_tb = gr.Textbox(label="Runs", placeholder="Type to filter...")
run_cb = gr.CheckboxGroup(
label="Runs",
choices=[],
interactive=True,
elem_id="run-cb",
show_select_all=True,
)
gr.HTML("<hr>")
realtime_cb = gr.Checkbox(label="Refresh metrics realtime", value=True)
smoothing_slider = gr.Slider(
label="Smoothing Factor",
minimum=0,
maximum=20,
value=10,
step=1,
info="0 = no smoothing",
)
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.",
)
navbar = gr.Navbar(value=[("Metrics", ""), ("Runs", "/runs")], main_page_name=False)
timer = gr.Timer(value=1)
metrics_subset = gr.State([])
user_interacted_with_run_cb = gr.State(False)
selected_runs_from_url = gr.State([])
gr.on(
[demo.load],
fn=configure,
outputs=[
metrics_subset,
sidebar,
metric_filter_tb,
selected_runs_from_url,
navbar,
],
queue=False,
api_name=False,
)
gr.on(
[demo.load],
fn=fns.get_projects,
outputs=project_dd,
show_progress="hidden",
queue=False,
api_name=False,
)
gr.on(
[timer.tick],
fn=update_runs,
inputs=[
project_dd,
run_tb,
user_interacted_with_run_cb,
selected_runs_from_url,
],
outputs=[run_cb, run_tb],
show_progress="hidden",
api_name=False,
)
gr.on(
[timer.tick],
fn=lambda: gr.Dropdown(info=fns.get_project_info()),
outputs=[project_dd],
show_progress="hidden",
api_name=False,
)
gr.on(
[demo.load, project_dd.change],
fn=update_runs,
inputs=[project_dd, run_tb, gr.State(False), selected_runs_from_url],
outputs=[run_cb, run_tb],
show_progress="hidden",
queue=False,
api_name=False,
).then(
fn=update_x_axis_choices,
inputs=[project_dd, run_cb],
outputs=x_axis_dd,
show_progress="hidden",
queue=False,
api_name=False,
).then(
fn=utils.generate_embed_code,
inputs=[project_dd, metric_filter_tb, run_cb],
outputs=[embed_code],
show_progress="hidden",
api_name=False,
queue=False,
).then(
fns.update_navbar_value,
inputs=[project_dd],
outputs=[navbar],
show_progress="hidden",
api_name=False,
queue=False,
)
gr.on(
[run_cb.input],
fn=update_x_axis_choices,
inputs=[project_dd, run_cb],
outputs=x_axis_dd,
show_progress="hidden",
queue=False,
api_name=False,
)
gr.on(
[metric_filter_tb.change, run_cb.change],
fn=utils.generate_embed_code,
inputs=[project_dd, metric_filter_tb, run_cb],
outputs=embed_code,
show_progress="hidden",
api_name=False,
queue=False,
)
realtime_cb.change(
fn=toggle_timer,
inputs=realtime_cb,
outputs=timer,
api_name=False,
queue=False,
)
run_cb.input(
fn=lambda: True,
outputs=user_interacted_with_run_cb,
api_name=False,
queue=False,
)
run_tb.input(
fn=filter_runs,
inputs=[project_dd, run_tb],
outputs=run_cb,
api_name=False,
queue=False,
)
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):
"""Check the last step for each run to detect when new data is available."""
if not project:
return {}
return SQLiteStorage.get_max_steps_for_runs(project)
timer.tick(
fn=update_last_steps,
inputs=[project_dd],
outputs=last_steps,
show_progress="hidden",
api_name=False,
)
@gr.render(
triggers=[
demo.load,
run_cb.change,
last_steps.change,
smoothing_slider.change,
x_lim.change,
x_axis_dd.change,
log_scale_cb.change,
metric_filter_tb.change,
],
inputs=[
project_dd,
run_cb,
smoothing_slider,
metrics_subset,
x_lim,
x_axis_dd,
log_scale_cb,
metric_filter_tb,
],
show_progress="hidden",
queue=False,
)
def update_dashboard(
project,
runs,
smoothing_granularity,
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_granularity, x_axis, log_scale
)
if df is not None:
dfs.append(df)
images_by_run[run] = images_by_key
if dfs:
if smoothing_granularity > 0:
original_dfs = []
smoothed_dfs = []
for df in dfs:
original_data = df[df["data_type"] == "original"]
smoothed_data = df[df["data_type"] == "smoothed"]
if not original_data.empty:
original_dfs.append(original_data)
if not smoothed_data.empty:
smoothed_dfs.append(smoothed_data)
all_dfs = original_dfs + smoothed_dfs
master_df = (
pd.concat(all_dfs, ignore_index=True) if all_dfs else pd.DataFrame()
)
else:
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 x_column and x_column in numeric_cols:
numeric_cols.remove(x_column)
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_granularity > 0)
metric_idx = 0
for group_name in sorted(nested_metric_groups.keys()):
group_data = nested_metric_groups[group_name]
total_plot_count = sum(
1
for m in group_data["direct_metrics"]
if not master_df.dropna(subset=[m]).empty
) + sum(
sum(1 for m in metrics if not master_df.dropna(subset=[m]).empty)
for metrics in group_data["subgroups"].values()
)
group_label = (
f"{group_name} ({total_plot_count})"
if total_plot_count > 0
else group_name
)
with gr.Accordion(
label=group_label,
open=True,
key=f"accordion-{group_name}",
preserved_by_key=["value", "open"],
):
if group_data["direct_metrics"]:
with gr.Draggable(
key=f"row-{group_name}-direct", orientation="row"
):
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 group_data["subgroups"]:
for subgroup_name in sorted(group_data["subgroups"].keys()):
subgroup_metrics = group_data["subgroups"][subgroup_name]
subgroup_plot_count = sum(
1
for m in subgroup_metrics
if not master_df.dropna(subset=[m]).empty
)
subgroup_label = (
f"{subgroup_name} ({subgroup_plot_count})"
if subgroup_plot_count > 0
else subgroup_name
)
with gr.Accordion(
label=subgroup_label,
open=True,
key=f"accordion-{group_name}-{subgroup_name}",
preserved_by_key=["value", "open"],
):
with gr.Draggable(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_media_section(images_by_run)
table_cols = master_df.select_dtypes(include="object").columns
table_cols = [c for c in table_cols if c not in utils.RESERVED_KEYS]
if metrics_subset:
table_cols = [c for c in table_cols if c in metrics_subset]
if metric_filter and metric_filter.strip():
table_cols = filter_metrics_by_regex(list(table_cols), metric_filter)
actual_table_count = sum(
1
for metric_name in table_cols
if not (metric_df := master_df.dropna(subset=[metric_name])).empty
and isinstance(value := metric_df[metric_name].iloc[-1], dict)
and value.get("_type") == Table.TYPE
)
if actual_table_count > 0:
with gr.Accordion(f"tables ({actual_table_count})", open=True):
with gr.Row(key="row"):
for metric_idx, metric_name in enumerate(table_cols):
metric_df = master_df.dropna(subset=[metric_name])
if not metric_df.empty:
value = metric_df[metric_name].iloc[-1]
if (
isinstance(value, dict)
and "_type" in value
and value["_type"] == Table.TYPE
):
try:
df = pd.DataFrame(value["_value"])
gr.DataFrame(
df,
label=f"{metric_name} (latest)",
key=f"table-{metric_idx}",
wrap=True,
)
except Exception as e:
gr.Warning(
f"Column {metric_name} failed to render as a table: {e}"
)
with demo.route("Runs", show_in_navbar=False):
run_page.render()
with demo.route("Run", show_in_navbar=False):
run_detail_page.render()
if __name__ == "__main__":
demo.launch(allowed_paths=[utils.TRACKIO_LOGO_DIR], show_api=False, show_error=True)
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