Create app.py

app.py

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+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()