app.py

import os
import uuid
from pathlib import Path

import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
import soundfile as sf
import spaces
import torch
import torchaudio

from esp_research.logging import logger
from hub_logger import upload_data

# from NatureLM.infer import Pipeline
# from NatureLM.models.NatureLM import NatureLM
from naturelm_audio import NatureLM  # noqa: F401

APP_DIR = Path(__file__).resolve().parent
STATIC_DIR = APP_DIR / "static"
ASSETS_DIR = APP_DIR / "assets"

SAMPLE_RATE = 16000  # Default sample rate for NatureLM-audio
MIN_AUDIO_DURATION: float = 0.5  # seconds
MAX_HISTORY_TURNS = 3  # Maximum number of conversation turns to include in context (user + assistant pairs)

DEVICE: str = "cuda" if torch.cuda.is_available() else "cpu"

# TODO: derive model version from model metadata or config instead of hardcoding
MODEL_VERSION = "1.5"


class _MockModel:
    """Placeholder model that returns dummy predictions."""

    def __call__(
        self,
        audios: list[str],
        queries: list[str],
        **kwargs: object,
    ) -> list[list[dict]]:
        return [[{"prediction": "(mock) I don't know yet!"}] for _ in audios]


# TODO: replace with real model loading

# model = NatureLM.from_pretrained("EarthSpeciesProject/NatureLM-audio")
# model = model.eval().to(DEVICE)
# model = Pipeline(model)
logger.info("Device: %s", DEVICE)
model = _MockModel()


def validate_audio_duration(audio_path: str) -> None:
    """Validate that the audio file meets the minimum duration requirement.

    Parameters
    ----------
    audio_path : str
        Path to the audio file.

    Raises
    ------
    Error
        If the audio duration is less than `MIN_AUDIO_DURATION`.
    """
    info = sf.info(audio_path)
    duration = info.duration  # info.num_frames / info.sample_rate
    if duration < MIN_AUDIO_DURATION:
        raise gr.Error(f"Audio duration must be at least {MIN_AUDIO_DURATION} seconds.")


@spaces.GPU
def prompt_lm(
    audios: list[str],
    queries: list[str] | str,
    window_length_seconds: float = 10.0,
    hop_length_seconds: float = 10.0,
) -> list[str]:
    """Generate response using the model.

    Parameters
    ----------
    audios : list[str]
        List of audio file paths.
    queries : list[str] | str
        Query or list of queries to process.
    window_length_seconds : float
        Length of the window for processing audio.
    hop_length_seconds : float
        Hop length for processing audio.

    Returns
    -------
    list[list[dict]]
        Nested list of prediction dictionaries for each audio-query pair.
    """
    if model is None:
        return "❌ Model not loaded. Please check the model configuration."

    with torch.amp.autocast(device_type="cuda", dtype=torch.float16):
        results: list[list[dict]] = model(
            audios,
            queries,
            window_length_seconds=window_length_seconds,
            hop_length_seconds=hop_length_seconds,
            input_sample_rate=None,
        )
    return results


def get_response(chatbot_history: list[dict], audio_input: str) -> list[dict]:
    """Generate response from the model based on user input and audio file.

    Parameters
    ----------
    chatbot_history : list[dict]
        Current chat history with conversation context.
    audio_input : str
        Path to the audio file.

    Returns
    -------
    list[dict]
        Updated chat history with model response appended.
    """
    try:
        # Warn if conversation is getting long
        num_turns = len(chatbot_history)
        if num_turns > MAX_HISTORY_TURNS * 2:  # Each turn = user + assistant message
            gr.Warning(
                "⚠️ Long conversations may affect response quality."
                " Consider starting a new conversation with the Clear button."
            )

        # Build conversation context from history
        conversation_context = []
        for message in chatbot_history:
            if message["role"] == "user":
                conversation_context.append(f"User: {message['content']}")
            elif message["role"] == "assistant":
                conversation_context.append(f"Assistant: {message['content']}")

        # Get the last user message
        last_user_message = ""
        for message in reversed(chatbot_history):
            if message["role"] == "user":
                last_user_message = message["content"]
                break

        # Format the full prompt with conversation history
        if len(conversation_context) > 2:  # More than just the current query
            # Include previous turns (limit to last MAX_HISTORY_TURNS exchanges)
            # recent_context = conversation_context[
            #    -(MAX_HISTORY_TURNS + 1) : -1
            # ]  # Exclude current message
            recent_context = conversation_context

            full_prompt = (
                "Previous conversation:\n" + "\n".join(recent_context) + "\n\nCurrent question: " + last_user_message
            )
        else:
            full_prompt = last_user_message

        logger.debug("Full prompt with history: %s", full_prompt)

        response = prompt_lm(
            audios=[audio_input],
            queries=[full_prompt.strip()],
            window_length_seconds=100_000,
            hop_length_seconds=100_000,
        )
        # get first item
        if isinstance(response, list) and len(response) > 0:
            response = response[0][0]["prediction"]
            logger.info("Model response: %s", response)
        else:
            response = "No response generated."
    except Exception as e:
        logger.exception("Error generating response: %s", e)
        response = "Error generating response. Please try again."

    # Add model response to chat history
    chatbot_history.append({"role": "assistant", "content": response})

    return chatbot_history


def plot_spectrogram(audio: torch.Tensor) -> plt.Figure:
    """Generate a spectrogram from the audio tensor.

    Parameters
    ----------
    audio : torch.Tensor
        Audio tensor.

    Returns
    -------
    plt.Figure
        Matplotlib figure with the spectrogram.
    """
    spectrogram = torchaudio.transforms.Spectrogram(n_fft=1024)(audio)
    spectrogram = spectrogram.numpy()[0].squeeze()

    fig, ax = plt.subplots(figsize=(13, 5))

    ax.imshow(np.log(spectrogram + 1e-4), aspect="auto", origin="lower", cmap="viridis")
    ax.set_title("Spectrogram")

    # Set x ticks to reflect 0 to audio duration seconds
    if audio.dim() > 1:
        duration = audio.size(1) / SAMPLE_RATE
    else:
        duration = audio.size(0) / SAMPLE_RATE
    ax.set_xlabel("Time")
    ax.set_xticks([0, spectrogram.shape[1]])
    ax.set_xticklabels(["0s", f"{duration:.2f}s"])

    ax.set_ylabel("Frequency")
    ax.set_yticks(
        [
            0,
            spectrogram.shape[0] // 4,
            spectrogram.shape[0] // 2,
            3 * spectrogram.shape[0] // 4,
            spectrogram.shape[0] - 1,
        ]
    )
    # Set y ticks to reflect 0 to nyquist frequency (sample_rate/2)
    nyquist_freq = SAMPLE_RATE / 2
    ax.set_yticklabels(
        [
            "0 Hz",
            f"{nyquist_freq / 4:.0f} Hz",
            f"{nyquist_freq / 2:.0f} Hz",
            f"{3 * nyquist_freq / 4:.0f} Hz",
            f"{nyquist_freq:.0f} Hz",
        ]
    )

    fig.tight_layout()

    return fig


def make_spectrogram_figure(audio_input: str) -> plt.Figure:
    audio = torch.zeros(1, SAMPLE_RATE)
    if audio_input:
        try:
            audio, _ = torchaudio.load(audio_input)
        except Exception:
            logger.exception("Error loading audio file %s", audio_input)
    return plot_spectrogram(audio)


def add_user_query(chatbot_history: list[dict], chat_input: str) -> list[dict]:
    """Add user message to chat history.

    Parameters
    ----------
    chatbot_history : list[dict]
        Current chat history.
    chat_input : str
        User's input text.

    Returns
    -------
    list[dict]
        Updated chat history with the user message appended.
    """
    # Validate input
    if not chat_input.strip():
        return chatbot_history

    chatbot_history.append({"role": "user", "content": chat_input.strip()})
    return chatbot_history


def log_to_hub(chatbot_history: list[dict], audio: str, session_id: str) -> None:
    """Upload data to hub."""
    if not chatbot_history or len(chatbot_history) < 2:
        return
    user_text = chatbot_history[-2]["content"]
    model_response = chatbot_history[-1]["content"]
    upload_data(audio, user_text, model_response, session_id, model_version=MODEL_VERSION)


def main() -> gr.Blocks:
    # Create placeholder audio files if they don't exist
    laz_audio = ASSETS_DIR / "Lazuli_Bunting_yell-YELLLAZB20160625SM303143.mp3"
    frog_audio = ASSETS_DIR / "nri-GreenTreeFrogEvergladesNP.mp3"
    robin_audio = ASSETS_DIR / "yell-YELLAMRO20160506SM3.mp3"
    whale_audio = ASSETS_DIR / "Humpback Whale - Megaptera novaeangliae.wav"
    crow_audio = ASSETS_DIR / "American Crow - Corvus brachyrhynchos.mp3"

    examples = {
        "Identifying Focal Species (Lazuli Bunting)": [
            str(laz_audio),
            "What is the common name for the focal species in the audio?",
        ],
        "Caption the audio (Green Tree Frog)": [
            str(frog_audio),
            "Caption the audio, using the common name for any animal species.",
        ],
        "Caption the audio (American Robin)": [
            str(robin_audio),
            "Caption the audio, using the scientific name for any animal species.",
        ],
        "Identifying Focal Species (Megaptera novaeangliae)": [
            str(whale_audio),
            "What is the scientific name for the focal species in the audio?",
        ],
        "Speaker Count (American Crow)": [
            str(crow_audio),
            "How many individuals are vocalizing in this audio?",
        ],
        "Caption the audio (Humpback Whale)": [str(whale_audio), "Caption the audio."],
    }

    gr.set_static_paths(paths=[ASSETS_DIR])

    theme = gr.themes.Base(primary_hue="blue", font=[gr.themes.GoogleFont("Noto Sans")])

    with gr.Blocks(
        title="NatureLM-audio",
    ) as app:
        with gr.Row():
            gr.HTML((STATIC_DIR / "header.html").read_text())

        with gr.Tabs():
            with gr.Tab("Analyze Audio"):
                session_id = gr.State(str(uuid.uuid4()))
                # uploaded_audio = gr.State()
                # Status indicator
                # status_text = gr.Textbox(
                #     value=model_manager.get_status(),
                #     label="Model Status",
                #     interactive=False,
                #     visible=True,
                # )

                with gr.Column(visible=True) as onboarding_message:
                    gr.HTML(
                        (STATIC_DIR / "onboarding.html").read_text(),
                        padding=False,
                    )

                with gr.Column(visible=True) as upload_section:
                    audio_input = gr.Audio(
                        container=True,
                        interactive=True,
                        sources=["upload"],
                    )
                    # check that audio duration is greater than MIN_AUDIO_DURATION
                    # raise
                    audio_input.change(
                        fn=validate_audio_duration,
                        inputs=[audio_input],
                        outputs=[],
                    )

                with gr.Accordion(label="Toggle Spectrogram", open=False, visible=False) as spectrogram:
                    plotter = gr.Plot(
                        plot_spectrogram(torch.zeros(1, SAMPLE_RATE)),
                        label="Spectrogram",
                        visible=False,
                        elem_id="spectrogram-plot",
                    )
                with gr.Column(visible=False) as tasks:
                    task_dropdown = gr.Dropdown(
                        [
                            "What are the common names for the species in the audio, if any?",
                            "Caption the audio, using the scientific name for any animal species.",
                            "Caption the audio, using the common name for any animal species.",
                            "What is the scientific name for the focal species in the audio?",
                            "What is the common name for the focal species in the audio?",
                            "What is the family of the focal species in the audio?",
                            "What is the genus of the focal species in the audio?",
                            "What is the taxonomic name of the focal species in the audio?",
                            "What call types are heard from the focal species in the audio?",
                            "What is the life stage of the focal species in the audio?",
                        ],
                        label="Pre-Loaded Tasks",
                        info="Select a task, or write your own prompt below.",
                        allow_custom_value=False,
                        value=None,
                    )
                with gr.Group(visible=False) as chat:
                    chatbot = gr.Chatbot(
                        elem_id="chatbot",
                        height=250,
                        label="Chat",
                        render_markdown=False,
                        group_consecutive_messages=False,
                        feedback_options=[
                            "like",
                            "dislike",
                            "wrong species",
                            "incorrect response",
                            "other",
                        ],
                        resizable=True,
                    )
                    with gr.Column():
                        chat_input = gr.Textbox(
                            placeholder="Type your message and press Enter to send",
                            lines=1,
                            show_label=False,
                            submit_btn="Send",
                            container=True,
                            autofocus=False,
                            elem_id="chat-input",
                        )

                with gr.Column():
                    gr.Examples(
                        list(examples.values()),
                        [audio_input, chat_input],
                        [audio_input, chat_input],
                        example_labels=list(examples.keys()),
                        examples_per_page=20,
                    )

                def validate_and_submit(chatbot_history: list[dict], chat_input: str) -> tuple[list[dict], str]:
                    if not chat_input or not chat_input.strip():
                        gr.Warning("Please enter a question or message before sending.")
                        return chatbot_history, chat_input

                    updated_history = add_user_query(chatbot_history, chat_input)
                    return updated_history, ""

                clear_button = gr.ClearButton(
                    components=[chatbot, chat_input, audio_input, plotter],
                    visible=False,
                )

                # if task_dropdown is selected, set chat_input to that value
                def set_query(task: str | None) -> dict:
                    if task:
                        return gr.update(value=task)
                    return gr.update(value="")

                task_dropdown.select(
                    fn=set_query,
                    inputs=[task_dropdown],
                    outputs=[chat_input],
                )

                def start_chat_interface(audio_path: str) -> tuple:
                    return (
                        gr.update(visible=False),  # hide onboarding message
                        gr.update(visible=True),  # show upload section
                        gr.update(visible=True),  # show spectrogram
                        gr.update(visible=True),  # show tasks
                        gr.update(visible=True),  # show chat box
                        gr.update(visible=True),  # show plotter
                    )

                # When audio added, set spectrogram
                audio_input.change(
                    fn=start_chat_interface,
                    inputs=[audio_input],
                    outputs=[
                        onboarding_message,
                        upload_section,
                        spectrogram,
                        tasks,
                        chat,
                        plotter,
                    ],
                ).then(
                    fn=make_spectrogram_figure,
                    inputs=[audio_input],
                    outputs=[plotter],
                )

                # When submit clicked first:
                # 1. Validate and add user query to chat history
                # 2. Get response from model
                # 3. Clear the chat input box
                # 4. Show clear button
                chat_input.submit(
                    validate_and_submit,
                    inputs=[chatbot, chat_input],
                    outputs=[chatbot, chat_input],
                ).then(
                    get_response,
                    inputs=[chatbot, audio_input],
                    outputs=[chatbot],
                ).then(
                    lambda: gr.update(visible=True),  # Show clear button
                    None,
                    [clear_button],
                ).then(
                    log_to_hub,
                    [chatbot, audio_input, session_id],
                    None,
                )

                clear_button.click(lambda: gr.ClearButton(visible=False), None, [clear_button])

            with gr.Tab("Sample Library"):
                with gr.Row():
                    with gr.Column():
                        gr.Markdown("### Download Sample Audio")
                        gr.Markdown(
                            "Feel free to explore these sample audio files."
                            " To download, click the button in the"
                            " top-right corner of each audio file."
                            " You can also find a large collection of"
                            " publicly available animal sounds on"
                            " [Xenocanto](https://xeno-canto.org/explore/taxonomy)"
                            " and [Watkins Marine Mammal Sound Database]"
                            "(https://whoicf2.whoi.edu/science/B/whalesounds/index.cfm)."
                        )
                        samples = [
                            (
                                str(ASSETS_DIR / "Lazuli_Bunting_yell-YELLLAZB20160625SM303143.m4a"),
                                "Lazuli Bunting",
                            ),
                            (
                                str(ASSETS_DIR / "nri-GreenTreeFrogEvergladesNP.mp3"),
                                "Green Tree Frog",
                            ),
                            (
                                str(ASSETS_DIR / "American Crow - Corvus brachyrhynchos.mp3"),
                                "American Crow",
                            ),
                            (
                                str(ASSETS_DIR / "Gray Wolf - Canis lupus italicus.m4a"),
                                "Gray Wolf",
                            ),
                            (
                                str(ASSETS_DIR / "Humpback Whale - Megaptera novaeangliae.wav"),
                                "Humpback Whale",
                            ),
                            (str(ASSETS_DIR / "Walrus - Odobenus rosmarus.wav"), "Walrus"),
                        ]
                        for row_i in range(0, len(samples), 3):
                            with gr.Row():
                                for filepath, label in samples[row_i : row_i + 3]:
                                    with gr.Column():
                                        gr.Audio(
                                            filepath,
                                            label=label,
                                        )

            with gr.Tab("💡 Help"):
                gr.HTML((STATIC_DIR / "help.html").read_text())

            app.css = (STATIC_DIR / "style.css").read_text()

    return app, theme


# Create and launch the app
if __name__ == "__main__":
    app, theme = main()

    # Docker-based HF Spaces require root_path so Gradio generates correct
    # URLs behind the reverse proxy (the Gradio SDK sets this automatically).
    root_path = os.environ.get("GRADIO_ROOT_PATH", "")

    app.launch(
        server_name="0.0.0.0",
        server_port=7860,
        theme=theme,
        root_path=root_path,
        allowed_paths=[str(ASSETS_DIR)],
    )