Minor UI tweaks

examples/deploy-on-spaces.py

@@ -0,0 +1,57 @@
+import random
+
+from tqdm import tqdm
+
+import trackio as wandb
+
+project_id = random.randint(10000, 99999)
+
+wandb.init(
+    project=f"fake-training-{project_id}",
+    name="test-run",
+    config=dict(
+        epochs=5,
+        learning_rate=0.001,
+        batch_size=32,
+    ),
+    space_id=f"abidlabs/trackio-{project_id}",
+)
+
+EPOCHS = 5
+NUM_TRAIN_BATCHES = 100
+NUM_VAL_BATCHES = 20
+
+for epoch in range(EPOCHS):
+    train_loss = 0
+    train_accuracy = 0
+    val_loss = 0
+    val_accuracy = 0
+
+    for _ in tqdm(range(NUM_TRAIN_BATCHES), desc=f"Epoch {epoch + 1} - Training"):
+        loss = random.uniform(0.2, 1.0)
+        accuracy = random.uniform(0.6, 0.95)
+        train_loss += loss
+        train_accuracy += accuracy
+
+    for _ in tqdm(range(NUM_VAL_BATCHES), desc=f"Epoch {epoch + 1} - Validation"):
+        loss = random.uniform(0.2, 0.9)
+        accuracy = random.uniform(0.65, 0.98)
+        val_loss += loss
+        val_accuracy += accuracy
+
+    train_loss /= NUM_TRAIN_BATCHES
+    train_accuracy /= NUM_TRAIN_BATCHES
+    val_loss /= NUM_VAL_BATCHES
+    val_accuracy /= NUM_VAL_BATCHES
+
+    wandb.log(
+        {
+            "epoch": epoch + 1,
+            "train_loss": train_loss,
+            "train_accuracy": train_accuracy,
+            "val_loss": val_loss,
+            "val_accuracy": val_accuracy,
+        }
+    )
+
+wandb.finish()

examples/fake-training.py

@@ -1,11 +1,12 @@
 import random
+import time
 
 from tqdm import tqdm
 
 import trackio as wandb
 
 wandb.init(
-    project="fake-training",
+    project=f"fake-training-{random.randint(10000, 99999)}",
     name="test-run",
     config=dict(
         epochs=5,
@@ -50,5 +51,6 @@
             "val_accuracy": val_accuracy,
         }
     )
+    time.sleep(1)
 
 wandb.finish()

trackio/ui.py

@@ -197,11 +197,9 @@ def configure(request: gr.Request):
         run_cb = gr.CheckboxGroup(
             label="Runs", choices=[], interactive=True, elem_id="run-cb"
         )
-
-    with gr.Sidebar(position="right", open=False) as settings_sidebar:
-        gr.Markdown("### ⚙️ Settings")
-        realtime_cb = gr.Checkbox(label="Refresh realtime", value=True)
-        smoothing_cb = gr.Checkbox(label="Smoothing", value=True)
+        gr.HTML("<hr>")
+        realtime_cb = gr.Checkbox(label="Refresh metrics realtime", value=True)
+        smoothing_cb = gr.Checkbox(label="Smooth metrics", value=True)
 
     timer = gr.Timer(value=1)
     metrics_subset = gr.State([])
@@ -255,19 +253,45 @@ def configure(request: gr.Request):
     )
 
     x_lim = gr.State(None)
+    last_step = gr.State(None)
 
     def update_x_lim(select_data: gr.SelectData):
         return select_data.index
 
+    def update_last_step(project, runs):
+        """Update the last step from all runs to detect when new data is available."""
+        if not project or not runs:
+            return None
+
+        max_step = 0
+        for run in runs:
+            metrics = SQLiteStorage.get_metrics(project, run)
+            if metrics:
+                df = pd.DataFrame(metrics)
+                if "step" not in df.columns:
+                    df["step"] = range(len(df))
+                if not df.empty:
+                    max_step = max(max_step, df["step"].max())
+
+        return max_step if max_step > 0 else None
+
+    timer.tick(
+        fn=update_last_step,
+        inputs=[project_dd, run_cb],
+        outputs=last_step,
+        show_progress="hidden",
+    )
+
     @gr.render(
         triggers=[
             demo.load,
             run_cb.change,
-            timer.tick,
+            last_step.change,
             smoothing_cb.change,
             x_lim.change,
         ],
         inputs=[project_dd, run_cb, smoothing_cb, metrics_subset, x_lim],
+        show_progress="hidden",
     )
     def update_dashboard(project, runs, smoothing, metrics_subset, x_lim_value):
         dfs = []