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api.py

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+import torch, io, torchaudio
+import numpy as np
+from pipeline import BioacousticEngine
+
+engine = None
+
+def get_engine():
+    global engine
+    if engine is None:
+        engine = BioacousticEngine()
+    return engine
+
+def predict_bird(audio_bytes, distance_threshold=0.8):
+    """Clean API Endpoint mapping raw bytes directly to spatial taxonomy clusters."""
+    try:
+        ae = get_engine()
+        waveform, sample_rate = torchaudio.load(io.BytesIO(audio_bytes))
+        processed_wave = ae.process_waveform(waveform, sample_rate)
+        
+        with torch.no_grad():
+            mel_spec = ae.preprocessor.process(processed_wave).unsqueeze(0)
+            mel_spec = mel_spec[:, :, :, :184] if mel_spec.shape[-1] >= 184 else torch.nn.functional.pad(mel_spec, (0, 184 - mel_spec.shape[-1]))
+            embedding = ae.model(mel_spec).cpu().numpy().flatten().reshape(1, -1)
+            
+        coords = ae.reducer.transform(embedding)
+        x, y = coords[0][0], coords[0][1]
+        
+        valid_df = ae.df[ae.df['Cluster_ID'] != -1].copy()
+        distances = np.sqrt((valid_df['UMAP_X'] - x)**2 + (valid_df['UMAP_Y'] - y)**2)
+        min_dist = distances.min()
+        
+        if min_dist > distance_threshold:
+            return {"status": "NO_BIRD_DETECTED", "x": float(x), "y": float(y), "distance": float(min_dist)}
+            
+        match_row = valid_df.loc[distances.idxmin()]
+        confidence = max(0.0, (distance_threshold - min_dist) / distance_threshold) * 100
+        
+        return {
+            "status": "SUCCESS",
+            "species": match_row['Target_Bird'].upper(),
+            "confidence": round(confidence, 2),
+            "x": float(x),
+            "y": float(y),
+            "anchor_id": match_row['File_ID']
+        }
+    except Exception as e:
+        return {"status": "ERROR", "message": str(f"Pipeline failure: {e}")}