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api.py

@@ -1,47 +1,2 @@
-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}")}
+def predict():
+    return {"status": "ok"}