Create app.py

app.py

@@ -0,0 +1,99 @@
+import gradio as gr
+from transformers import pipeline
+import numpy as np
+
+classifier = pipeline(
+    "audio-classification",
+    model="dima806/bird_sounds_classification",
+    device=-1,
+)
+
+# Get the full species list from the model config
+SPECIES_LIST = sorted(set(
+    classifier.model.config.id2label.values()
+))
+
+def classify_bird(audio):
+    if audio is None:
+        return "Please upload or record an audio file."
+
+    sr, y = audio
+
+    # Convert to float32 and normalize
+    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 stereo, take first channel
+    if len(y.shape) > 1:
+        y = y[:, 0]
+
+    # Resample to 16kHz if needed (model expects 16kHz)
+    if sr != 16000:
+        # Simple resampling using numpy interpolation
+        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=5)
+
+    # Format output
+    lines = []
+    for i, pred in enumerate(results, 1):
+        score = pred["score"]
+        label = pred["label"]
+
+        if i == 1 and 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.")
+            lines.append(f"Best guess: {label} ({score:.0%})")
+            lines.append("")
+            lines.append("Top 5 predictions:")
+            lines.append(f"  1. {label} - {score:.1%}")
+            continue
+
+        bar_length = int(score * 20)
+        bar = "#" * bar_length + "." * (20 - bar_length)
+        lines.append(f"{i}. {label}")
+        lines.append(f"   {bar}  {score:.1%}")
+
+    return "\n".join(lines)
+
+
+demo = gr.Interface(
+    fn=classify_bird,
+    inputs=gr.Audio(
+        label="Upload or Record a Bird Song",
+        type="numpy",
+    ),
+    outputs=gr.Textbox(label="Classification Results", lines=12),
+    title="Bird Song Classifier",
+    description=(
+        "Upload a bird song recording and this model will try to identify the species. "
+        "Uses dima806/bird_sounds_classification, a wav2vec2-based classifier trained on "
+        "50 bird species (mostly Tinamous, Guans, and Chachalacas - neotropical birds). "
+        "Best results with clean recordings of 3+ seconds.\n\n"
+        "Note: This model was trained on tropical/neotropical species. "
+        "It won't recognize common North American backyard birds like cardinals or robins. "
+        "That's a training data limitation, not an architecture limitation.\n\n"
+        "Try recordings from Xeno-Canto (https://xeno-canto.org/) - search for species like "
+        "Great Tinamou, Plain Chachalaca, or Crested Guan."
+    ),
+    article=(
+        "### Species this model knows\n\n"
+        + ", ".join(SPECIES_LIST)
+        + "\n\n---\n*Riley's Space 2 - AI + Research Level 2*"
+    ),
+    theme=gr.themes.Soft(),
+    allow_flagging="never",
+)
+
+demo.launch()