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"""Recipe-local Gradio launcher for ESPnet3 demos."""

from __future__ import annotations

import argparse
import logging
from pathlib import Path

import gradio as gr
import librosa
import numpy as np

from espnet3.publication.demo.session import load_demo_session
from espnet3.utils.logging_utils import configure_logging

logger = logging.getLogger(__name__)

# The classifier was trained on 16 kHz audio; browsers record at 44.1/48 kHz.
MODEL_SAMPLING_RATE = 16000


def _as_model_audio(value):
    """Convert one Gradio audio value into a mono 16 kHz float32 waveform.
    ``gr.Audio`` yields ``(sample_rate, samples)`` and the samples are int16
    for a browser recording, but ``espnet2.bin.cls_inference.Classification``
    accepts only a float waveform at the model's own rate. Values that are not
    audio tuples are passed through untouched.
    """
    if not (isinstance(value, tuple) and len(value) == 2):
        return value

    sampling_rate, samples = value
    samples = np.asarray(samples)
    if samples.ndim > 1:
        samples = samples.mean(axis=1)
    if np.issubdtype(samples.dtype, np.integer):
        samples = samples.astype(np.float32) / np.iinfo(samples.dtype).max
    else:
        samples = samples.astype(np.float32)
    if sampling_rate != MODEL_SAMPLING_RATE:
        samples = librosa.resample(
            samples, orig_sr=sampling_rate, target_sr=MODEL_SAMPLING_RATE
        )
    return samples


def build_demo(
    demo_dir: Path,
    demo_config_path: Path | None = None,
):
    """Build the default Gradio Blocks app for one packed demo."""
    if demo_config_path is None:
        demo_config_path = demo_dir / "demo.yaml"
    logger.info(
        "Building recipe demo UI | demo_dir=%s demo_config_path=%s",
        demo_dir,
        demo_config_path,
    )
    session = load_demo_session(demo_dir, demo_config_path)
    logger.info(
        "Resolved demo specs | inputs=%s outputs=%s",
        session.input_specs,
        session.output_specs,
    )
    session_inference_fn = session.create_inference_fn(
        session.input_specs,
        session.output_specs,
    )

    def inference_fn(*values):
        """Normalize Gradio audio values before handing them to the session."""
        return session_inference_fn(*(_as_model_audio(v) for v in values))

    with gr.Blocks(title=session.title) as app:
        if session.title:
            gr.Markdown(f"# {session.title}")

        input_components = []
        with gr.Column():
            # Gradio click handlers bind positional values, not a dict keyed by
            # spec name. Keep this list in the same order as
            # session.input_specs so create_inference_fn(*values) can zip each
            # incoming value back to the matching spec/key.
            for spec in session.input_specs:
                logger.info("Building input component | spec=%s", spec)
                # build_input_component() returns one Gradio input component
                # instance (for example gr.Audio or gr.Textbox). That component
                # object is what Gradio expects in click(..., inputs=[...]).
                input_components.append(session.build_input_component(spec))

        submit_button = gr.Button("Run")

        output_components = []
        with gr.Column():
            # Outputs also stay positional. inference_fn returns one value per
            # spec in this exact order, and Gradio routes each returned value
            # to the component at the same list index.
            for spec in session.output_specs:
                logger.info("Building output component | spec=%s", spec)
                # build_output_component() returns one Gradio output component
                # instance that Gradio can target from click(..., outputs=[...]).
                output_components.append(session.build_output_component(spec))

        if session.description:
            gr.Markdown(session.description)

        logger.info("Binding Run button click handler")
        submit_button.click(
            fn=inference_fn,
            inputs=input_components,
            outputs=output_components,
        )

    logger.info("Recipe demo UI ready")
    return app


def main() -> None:
    parser = argparse.ArgumentParser(description="Launch an ESPnet3 demo.")
    parser.add_argument(
        "--demo-dir",
        type=Path,
        default=Path(__file__).resolve().parent,
        help="Path to the demo directory. Defaults to this script's directory.",
    )
    parser.add_argument(
        "--demo-config",
        type=Path,
        default=None,
        help="Optional packed demo config path. Relative paths use --demo-dir.",
    )
    args = parser.parse_args()
    configure_logging(log_dir=args.demo_dir, filename="demo.log")
    logger.info("Starting recipe demo CLI | args=%s", args)
    demo_config_path = args.demo_config or (args.demo_dir / "demo.yaml")
    app = build_demo(
        args.demo_dir,
        demo_config_path=demo_config_path,
    )
    logger.info("Launching Gradio app")
    app.launch()


if __name__ == "__main__":
    main()

	"""Recipe-local Gradio launcher for ESPnet3 demos."""

	from __future__ import annotations

	import argparse
	import logging
	from pathlib import Path

	import gradio as gr
	import librosa
	import numpy as np

	from espnet3.publication.demo.session import load_demo_session
	from espnet3.utils.logging_utils import configure_logging

	logger = logging.getLogger(__name__)

	# The classifier was trained on 16 kHz audio; browsers record at 44.1/48 kHz.
	MODEL_SAMPLING_RATE = 16000


	def _as_model_audio(value):
	"""Convert one Gradio audio value into a mono 16 kHz float32 waveform.

	``gr.Audio`` yields ``(sample_rate, samples)`` and the samples are int16
	for a browser recording, but ``espnet2.bin.cls_inference.Classification``
	accepts only a float waveform at the model's own rate. Values that are not
	audio tuples are passed through untouched.
	"""
	if not (isinstance(value, tuple) and len(value) == 2):
	return value

	sampling_rate, samples = value
	samples = np.asarray(samples)
	if samples.ndim > 1:
	samples = samples.mean(axis=1)
	if np.issubdtype(samples.dtype, np.integer):
	samples = samples.astype(np.float32) / np.iinfo(samples.dtype).max
	else:
	samples = samples.astype(np.float32)
	if sampling_rate != MODEL_SAMPLING_RATE:
	samples = librosa.resample(
	samples, orig_sr=sampling_rate, target_sr=MODEL_SAMPLING_RATE
	)
	return samples


	def build_demo(
	demo_dir: Path,
	demo_config_path: Path \| None = None,
	):
	"""Build the default Gradio Blocks app for one packed demo."""
	if demo_config_path is None:
	demo_config_path = demo_dir / "demo.yaml"
	logger.info(
	"Building recipe demo UI \| demo_dir=%s demo_config_path=%s",
	demo_dir,
	demo_config_path,
	)
	session = load_demo_session(demo_dir, demo_config_path)
	logger.info(
	"Resolved demo specs \| inputs=%s outputs=%s",
	session.input_specs,
	session.output_specs,
	)
	session_inference_fn = session.create_inference_fn(
	session.input_specs,
	session.output_specs,
	)

	def inference_fn(*values):
	"""Normalize Gradio audio values before handing them to the session."""
	return session_inference_fn(*(_as_model_audio(v) for v in values))

	with gr.Blocks(title=session.title) as app:
	if session.title:
	gr.Markdown(f"# {session.title}")

	input_components = []
	with gr.Column():
	# Gradio click handlers bind positional values, not a dict keyed by
	# spec name. Keep this list in the same order as
	# session.input_specs so create_inference_fn(*values) can zip each
	# incoming value back to the matching spec/key.
	for spec in session.input_specs:
	logger.info("Building input component \| spec=%s", spec)
	# build_input_component() returns one Gradio input component
	# instance (for example gr.Audio or gr.Textbox). That component
	# object is what Gradio expects in click(..., inputs=[...]).
	input_components.append(session.build_input_component(spec))

	submit_button = gr.Button("Run")

	output_components = []
	with gr.Column():
	# Outputs also stay positional. inference_fn returns one value per
	# spec in this exact order, and Gradio routes each returned value
	# to the component at the same list index.
	for spec in session.output_specs:
	logger.info("Building output component \| spec=%s", spec)
	# build_output_component() returns one Gradio output component
	# instance that Gradio can target from click(..., outputs=[...]).
	output_components.append(session.build_output_component(spec))

	if session.description:
	gr.Markdown(session.description)

	logger.info("Binding Run button click handler")
	submit_button.click(
	fn=inference_fn,
	inputs=input_components,
	outputs=output_components,
	)

	logger.info("Recipe demo UI ready")
	return app


	def main() -> None:
	parser = argparse.ArgumentParser(description="Launch an ESPnet3 demo.")
	parser.add_argument(
	"--demo-dir",
	type=Path,
	default=Path(__file__).resolve().parent,
	help="Path to the demo directory. Defaults to this script's directory.",
	)
	parser.add_argument(
	"--demo-config",
	type=Path,
	default=None,
	help="Optional packed demo config path. Relative paths use --demo-dir.",
	)
	args = parser.parse_args()
	configure_logging(log_dir=args.demo_dir, filename="demo.log")
	logger.info("Starting recipe demo CLI \| args=%s", args)
	demo_config_path = args.demo_config or (args.demo_dir / "demo.yaml")
	app = build_demo(
	args.demo_dir,
	demo_config_path=demo_config_path,
	)
	logger.info("Launching Gradio app")
	app.launch()


	if __name__ == "__main__":
	main()