Auto-deploy from GitHub: ffd9fa1fec8ca15ebb48cf1bb9be21334166a6cc

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@@ -32,11 +32,11 @@ RUN python3.11 -m pip install poetry && \
     poetry install --only=main
 
 # Copy application code
-COPY src/ ./src/
-COPY app/ ./app/
-COPY config/ ./config/
 COPY models/ ./models/
+COPY config/ ./config/
+COPY app/ ./app/
 COPY scripts/ ./scripts/
+COPY src/ ./src/
 
 # Create cache directories with proper permissions
 RUN mkdir -p /app/.cache/huggingface /app/.cache/torch /tmp/numba_cache && \

src/musiclime/wrapper.py

@@ -127,14 +127,21 @@ class MusicLIMEPredictor:
         pca_model = joblib.load("models/fusion/pca.pkl")
         reduced_lyrics_batch = pca_model.transform(scaled_lyrics_batch)  # (batch, 512)
 
-        # Step 5: Concatenate features
+        # Step 5: Apply scaler to PCA-scaled lyrics batch
+        print("[MusicLIME] Reapplying scaler to PCA-scaled batch")
+        pca_scaler = joblib.load("models/fusion/pca_scaler.pkl")
+        reduced_lyrics_batch = pca_scaler.transform(
+            reduced_lyrics_batch
+        )  # (batch, 512)
+
+        # Step 6: Concatenate features
         combined_features_batch = np.concatenate(
             [scaled_audio_batch, reduced_lyrics_batch], axis=1
         )  # (batch, sum of lyrics & audio vector dims)
         scaling_time = time.time() - start_time
         print(green_bold(f"[MusicLIME] Scaling completed in {scaling_time:.2f}s"))
 
-        # Step 6: Batch MLP prediction
+        # Step 7: Batch MLP prediction
         start_time = time.time()
         print("[MusicLIME] Running MLP predictions (batch)...")
         if self.classifier is None: