import os
from datetime import datetime, timezone
from pathlib import Path

os.environ["KERAS_BACKEND"] = "tensorflow"
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"

import keras
import numpy as np


keras.utils.set_random_seed(31)

run_name = datetime.now(timezone.utc).strftime("run-%Y%m%d-%H%M%S")
log_dir = Path("logs/tensorboard") / run_name

x_train = np.linspace(0.0, 1.0, 80, dtype="float32").reshape(20, 4)
y_train = ((x_train[:, 0] + x_train[:, 1]) > 0.8).astype("float32")

model = keras.Sequential(
    [
        keras.Input(shape=(4,), name="features"),
        keras.layers.Dense(8, activation="relu"),
        keras.layers.Dense(1, activation="sigmoid"),
    ]
)
model.compile(
    optimizer=keras.optimizers.Adam(learning_rate=0.03),
    loss=keras.losses.BinaryCrossentropy(),
    metrics=[keras.metrics.BinaryAccuracy(name="accuracy")],
)

tensorboard_callback = keras.callbacks.TensorBoard(
    log_dir=log_dir,
    histogram_freq=0,
    update_freq="epoch",
)

model.fit(
    x_train,
    y_train,
    epochs=3,
    batch_size=5,
    callbacks=[tensorboard_callback],
    verbose=0,
)

event_files = list(log_dir.rglob("events.out.tfevents.*"))
print(f"Run directory: {log_dir}")
print(f"TensorBoard event files: {len(event_files)}")