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Fix bequcoup de choses : Genre OK, affichage des infos sur le front

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Benoit
2025-12-22 13:26:55 +01:00
parent dec30019e2
commit 90c841310c
2 changed files with 60 additions and 7 deletions
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35
backend/src/core/essentia_classifier.py
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@@ -168,12 +168,25 @@ class EssentiaClassifier:
predictions = predictions[0] # Remove batch dimension
# Get top predictions
top_indices = np.argsort(predictions)[::-1][:5]
labels = self.class_labels.get("genre", [])
logger.info(f"Genre predictions shape: {predictions.shape}, num_labels: {len(labels)}")
primary = labels[top_indices[0]] if labels else "unknown"
secondary = [labels[i] for i in top_indices[1:4]] if labels else []
confidence = float(predictions[top_indices[0]])
# Ensure we don't go out of bounds
if len(predictions) == 0:
logger.warning("No predictions returned from genre model")
return self._fallback_genre()
top_indices = np.argsort(predictions)[::-1][:5]
# Only use indices that are within the labels range
valid_top_indices = [i for i in top_indices if i < len(labels)]
if not valid_top_indices:
logger.warning(f"No valid indices found. Predictions: {len(predictions)}, Labels: {len(labels)}")
return self._fallback_genre()
primary = labels[valid_top_indices[0]]
secondary = [labels[i] for i in valid_top_indices[1:4]]
confidence = float(predictions[valid_top_indices[0]])
return {
"primary": primary,
@@ -218,11 +231,19 @@ class EssentiaClassifier:
predictions = predictions[0]
# Get top predictions
top_indices = np.argsort(predictions)[::-1][:5]
labels = self.class_labels.get("mood", [])
primary = labels[top_indices[0]] if labels else "unknown"
secondary = [labels[i] for i in top_indices[1:3]] if labels else []
if len(predictions) == 0:
return self._fallback_mood()
top_indices = np.argsort(predictions)[::-1][:5]
valid_top_indices = [i for i in top_indices if i < len(labels)]
if not valid_top_indices:
return self._fallback_mood()
primary = labels[valid_top_indices[0]] if valid_top_indices else "unknown"
secondary = [labels[i] for i in valid_top_indices[1:3]] if len(valid_top_indices) > 1 else []
# Estimate arousal and valence from mood labels (simplified)
arousal, valence = self._estimate_arousal_valence(primary)