app.py

import gradio as gr
from transformers import pipeline
import numpy as np
from datetime import datetime

# Load the audio classification model
classifier = pipeline(
    "audio-classification",
    model="dima806/bird_sounds_classification",
    device=-1,
)

# Bird information database (15 species with descriptions)
BIRD_INFO = {
    "Great Tinamou": {
        "habitat": "Tropical and subtropical lowland forests from southern Mexico to northern South America.",
        "song": "A series of tremulous, haunting whistles that echo through the forest - one of the most recognizable sounds of the neotropical lowlands.",
        "range": "Southern Mexico through Central America to northern South America, including Brazil and Peru.",
        "fun_fact": "Despite being a ground-dwelling bird, the Great Tinamou roosts in trees at night. Its eggs are among the most beautiful in the bird world - glossy turquoise-blue.",
    },
    "Plain Chachalaca": {
        "habitat": "Brushy woodland edges, thickets, and riparian areas. The only chachalaca regularly found in the United States (southern Texas).",
        "song": "A loud, raucous CHA-cha-LAC repeated in chorus by groups - unmistakable once you've heard it. Often called at dawn.",
        "range": "Southern Texas through Mexico to Costa Rica.",
        "fun_fact": "Plain Chachalacas are one of the few species in this model's list that can actually be seen in the US - in the Rio Grande Valley of Texas.",
    },
    "Crested Guan": {
        "habitat": "Mountain forests and cloud forests, typically at elevations of 500-2,500 meters.",
        "song": "A variety of honking and trumpeting calls, especially loud during breeding season.",
        "range": "Southern Mexico through Central America to western South America.",
        "fun_fact": "Crested Guans are important seed dispersers for many tropical tree species. They swallow fruits whole and spread seeds through the forest.",
    },
    "Andean Guan": {
        "habitat": "Cloud forests and humid montane forests of the Andes, typically between 1,500 and 3,500 meters elevation.",
        "song": "Deep honking calls that carry through mountain valleys, often given in the early morning.",
        "range": "Andes from Venezuela south through Colombia, Ecuador, Peru, and Bolivia.",
        "fun_fact": "The Andean Guan is a canopy specialist that rarely descends to the ground, moving through the treetops to feed on fruit and leaves.",
    },
    "Little Tinamou": {
        "habitat": "Dense undergrowth of tropical forests. Extremely secretive and almost never seen despite being common.",
        "song": "A long, tremulous whistle that rises and falls - one of the most frequently heard but least seen birds in its range.",
        "range": "Southern Mexico through Central America to South America, as far south as Brazil.",
        "fun_fact": "Little Tinamous are heard far more often than seen. They freeze when threatened and rely on their camouflage, only flushing at the last moment.",
    },
    "Solitary Tinamou": {
        "habitat": "Interior of humid tropical forests, usually on the forest floor.",
        "song": "A mournful, descending whistle that sounds almost electronic - quite eerie when heard in the deep forest.",
        "range": "Central America through northern South America.",
        "fun_fact": "True to its name, the Solitary Tinamou is almost always found alone. Males incubate the eggs and raise the chicks by themselves.",
    },
    "Highland Tinamou": {
        "habitat": "Cloud forests and montane forests, from 1,200 to 3,000 meters elevation.",
        "song": "A clear, descending series of whistles. Sometimes described as sounding like someone playing a slow scale on a flute.",
        "range": "Mountains of Costa Rica and Panama through the Andes to Bolivia.",
        "fun_fact": "Highland Tinamous have been recorded at higher elevations than almost any other tinamou species.",
    },
    "Grey-headed Chachalaca": {
        "habitat": "Dry forests, forest edges, and agricultural areas with scattered trees.",
        "song": "Loud, harsh calls similar to other chachalacas, often given by groups in noisy choruses at dawn and dusk.",
        "range": "Honduras through Central America to northern Colombia.",
        "fun_fact": "Chachalacas get their name from the sound of their call - cha-cha-lac-a - repeated over and over.",
    },
    "Band-tailed Guan": {
        "habitat": "Humid mountain forests and cloud forests.",
        "song": "A series of deep, resonant honking sounds, especially vocal during the breeding season.",
        "range": "Andes from Colombia and Venezuela south to Bolivia.",
        "fun_fact": "Band-tailed Guans travel in small family groups and are surprisingly acrobatic for their size, leaping between branches to reach fruit.",
    },
    "Black-capped Tinamou": {
        "habitat": "Forests from lowlands to lower montane elevations.",
        "song": "A bubbling, accelerating series of whistles - sounds like a bouncing ball slowing to a stop, but in reverse.",
        "range": "Central Peru to Bolivia, in the eastern Andean slopes.",
        "fun_fact": "Like other tinamous, the Black-capped Tinamou can fly but strongly prefers to walk. When it does fly, it's in short, explosive bursts.",
    },
    "Spotted Nothura": {
        "habitat": "Grasslands and open areas, including agricultural fields and pastures.",
        "song": "A series of sharp, staccato whistles, often given from the ground in open grassland.",
        "range": "Central South America - Brazil, Paraguay, Argentina, Uruguay.",
        "fun_fact": "Nothuras are grassland tinamous - unlike their forest-dwelling relatives, they live in open habitats and look somewhat like partridges.",
    },
    "Red-winged Tinamou": {
        "habitat": "Grasslands, scrublands, and agricultural areas in southern South America.",
        "song": "A melodious, flute-like whistle that rises and falls, often heard at dawn and dusk across the pampas.",
        "range": "Southern Brazil, Paraguay, Uruguay, Argentina.",
        "fun_fact": "Named for the rufous-red color visible on its wings in flight - one of the few times you'll see this secretive bird in the open.",
    },
    "Australian Brushturkey": {
        "habitat": "Rainforests, scrublands, and suburban gardens in eastern Australia.",
        "song": "Deep booming calls during breeding. Otherwise relatively quiet compared to other species in this list.",
        "range": "Eastern Australia, from Cape York to southern New South Wales.",
        "fun_fact": "Males build enormous mound nests (up to 4 meters wide) out of decomposing vegetation. The heat from decomposition incubates the eggs - no body heat required.",
    },
    "Dusky Megapode": {
        "habitat": "Tropical forests on islands in the western Pacific, often near volcanic areas.",
        "song": "Loud wailing calls, especially at night. Some species in this family are among the noisiest birds in their habitat.",
        "range": "Islands of Indonesia and Papua New Guinea.",
        "fun_fact": "Some megapodes use volcanic heat to incubate their eggs, burying them in volcanically warmed soil - the only birds known to use geothermal energy for reproduction.",
    },
    "Tataupa Tinamou": {
        "habitat": "Forest edges, secondary growth, and dense undergrowth in tropical and subtropical regions.",
        "song": "A rich, mellow series of whistles. Has been described as one of the most beautiful bird songs in South America.",
        "range": "Eastern South America from Brazil to Argentina.",
        "fun_fact": "The Tataupa Tinamou is more tolerant of disturbed habitats than many of its relatives, which helps it survive in areas affected by deforestation.",
    },
}

ALL_SPECIES = sorted(set(classifier.model.config.id2label.values()))

# Sighting log (in-memory)
sighting_log = []


def classify_bird(audio):
    if audio is None:
        return "Please upload or record an audio file.", gr.update(), gr.update()

    sr, y = audio

    if y.dtype == np.int16:
        y = y.astype(np.float32) / 32768.0
    elif y.dtype == np.int32:
        y = y.astype(np.float32) / 2147483648.0
    elif y.dtype != np.float32:
        y = y.astype(np.float32)

    if len(y.shape) > 1:
        y = y[:, 0]

    if sr != 16000:
        duration = len(y) / sr
        new_length = int(duration * 16000)
        y = np.interp(
            np.linspace(0, len(y) - 1, new_length),
            np.arange(len(y)),
            y,
        )
        sr = 16000

    results = classifier({"sampling_rate": sr, "raw": y}, top_k=3)

    lines = []
    top_species = results[0]["label"]
    top_score = results[0]["score"]

    if top_score < 0.40:
        lines.append("Not confident - this may not be a recognizable bird song,")
        lines.append("or the species may not be in this model's training data.\n")

    for i, pred in enumerate(results, 1):
        score = pred["score"]
        label = pred["label"]

        # Color-coded confidence
        if score >= 0.70:
            indicator = "HIGH"
        elif score >= 0.40:
            indicator = "MED"
        else:
            indicator = "LOW"

        bar_length = int(score * 20)
        bar = "#" * bar_length + "." * (20 - bar_length)
        lines.append(f"[{indicator}] {i}. {label}")
        lines.append(f"   {bar}  {score:.1%}\n")

    return "\n".join(lines), gr.update(value=top_species), gr.update()


def get_bird_info(species):
    if species in BIRD_INFO:
        info = BIRD_INFO[species]
        text = f"## {species}\n\n"
        text += f"**Habitat:** {info['habitat']}\n\n"
        text += f"**Song:** {info['song']}\n\n"
        text += f"**Range:** {info['range']}\n\n"
        text += f"**Fun fact:** {info['fun_fact']}"
        return text
    else:
        return (
            f"## {species}\n\n"
            f"No detailed description available for this species yet. "
            f"I've written descriptions for 15 of the 50 species in this model. "
            f"Try searching [Xeno-Canto](https://xeno-canto.org/) or the "
            f"[Cornell Lab](https://www.allaboutbirds.org/) for more information."
        )


def add_sighting(species, location, notes):
    if not species:
        return format_log()

    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M")
    entry = {
        "species": species,
        "date": timestamp,
        "location": location or "Not specified",
        "notes": notes or "",
    }
    sighting_log.append(entry)
    return format_log()


def format_log():
    if not sighting_log:
        return "No sightings logged yet. Identify a bird and add it to your log!"

    lines = [f"### Sighting Log ({len(sighting_log)} entries)\n"]
    for i, entry in enumerate(sighting_log, 1):
        lines.append(f"**{i}. {entry['species']}**")
        lines.append(f"   {entry['date']}  |  {entry['location']}")
        if entry["notes"]:
            lines.append(f"   Notes: {entry['notes']}")
        lines.append("")

    lines.append("---")
    lines.append(
        "*This log is stored in memory and will reset when the Space restarts. "
        "Copy your log if you want to save it!*"
    )
    return "\n".join(lines)


# Build the interface with tabs
with gr.Blocks(theme=gr.themes.Soft(), title="The Backyard Birder") as demo:
    gr.Markdown(
        """
        # The Backyard Birder
        A multi-feature birding assistant. Identify birds from audio recordings,
        learn about the species, and keep a sighting log.

        **Model:** `dima806/bird_sounds_classification` - 50 species (Tinamous, Guans, Chachalacas, and relatives).
        These are neotropical birds, not typical North American backyard species. The tool demonstrates how
        audio classification pipelines work; with a different model (like BirdNET), it could identify local birds too.

        ---
        """
    )

    # State for passing species between tabs
    current_species = gr.State("")

    with gr.Tabs():
        # Tab 1: Identify
        with gr.Tab("Identify"):
            gr.Markdown("Upload a bird recording to identify the species. Best results with clean recordings of 3+ seconds.")
            with gr.Row():
                with gr.Column():
                    audio_input = gr.Audio(
                        label="Upload or Record Audio",
                        type="numpy",
                    )
                    classify_btn = gr.Button("Identify Bird", variant="primary")
                with gr.Column():
                    classification_output = gr.Textbox(
                        label="Top 3 Predictions",
                        lines=10,
                        interactive=False,
                    )
                    species_display = gr.Textbox(
                        label="Top prediction",
                        visible=False,
                    )

        # Tab 2: Learn
        with gr.Tab("Learn"):
            gr.Markdown("Select a species to learn about it. Descriptions available for 15 of the 50 species.")
            species_dropdown = gr.Dropdown(
                choices=ALL_SPECIES,
                label="Select a Species",
                value="Great Tinamou",
            )
            bird_info_output = gr.Markdown(
                value=get_bird_info("Great Tinamou"),
            )

        # Tab 3: Log
        with gr.Tab("Log Sightings"):
            gr.Markdown("Keep track of what you hear. Add species to your sighting log with location and notes.")
            with gr.Row():
                with gr.Column():
                    log_species = gr.Dropdown(
                        choices=ALL_SPECIES,
                        label="Species",
                        allow_custom_value=True,
                    )
                    log_location = gr.Textbox(
                        label="Location",
                        placeholder="e.g., Backyard, Local park, Trail near school...",
                    )
                    log_notes = gr.Textbox(
                        label="Notes",
                        placeholder="e.g., Heard at dawn, two birds calling back and forth...",
                        lines=2,
                    )
                    log_btn = gr.Button("Add to Log", variant="primary")
                with gr.Column():
                    log_output = gr.Markdown(value=format_log())

    # Wire up events
    classify_btn.click(
        fn=classify_bird,
        inputs=[audio_input],
        outputs=[classification_output, species_dropdown, species_display],
    )

    species_dropdown.change(
        fn=get_bird_info,
        inputs=[species_dropdown],
        outputs=[bird_info_output],
    )

    log_btn.click(
        fn=add_sighting,
        inputs=[log_species, log_location, log_notes],
        outputs=[log_output],
    )

    gr.Markdown(
        """
        ---
        *Riley's Space 3 - AI + Research Level 2*

        **How this works:** The Identify tab uses an audio classification model to predict which species
        is singing. The Learn tab shows hand-written descriptions for 15 species. The Log tab lets you
        track your sightings during this session. This is a multi-model pipeline: audio classification
        feeds species information, and errors in identification propagate to wrong descriptions.
        """
    )

demo.launch()