Update tasks/audio.py

tasks/audio.py

@@ -48,6 +48,17 @@ async def evaluate_audio(request: AudioEvaluationRequest):
     train_test = dataset["train"]
     test_dataset = dataset["test"]
 
+    
+    # Start tracking emissions
+    tracker.start()
+    tracker.start_task("inference")
+    
+    #--------------------------------------------------------------------------------------------
+    # YOUR MODEL INFERENCE CODE HERE
+    # Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.
+    #--------------------------------------------------------------------------------------------   
+
+
     def preprocess_data(row, sr):
         new_row = librosa.resample(row['audio']['array'], orig_sr=row['audio']['sampling_rate'], target_sr=sr)
         new_row = np.pad(new_row, (0, 3 * sr - len(new_row)), 'constant')
@@ -59,16 +70,6 @@ async def evaluate_audio(request: AudioEvaluationRequest):
     model_filename = "lightgbm_10_mfcc.pkl"
     clf = joblib.load(model_filename)
     
-    # Start tracking emissions
-    tracker.start()
-    tracker.start_task("inference")
-    
-    #--------------------------------------------------------------------------------------------
-    # YOUR MODEL INFERENCE CODE HERE
-    # Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.
-    #--------------------------------------------------------------------------------------------   
-    
-    # Make random predictions (placeholder for actual model inference)
     true_labels = test_dataset["label"]
     predictions = clf.predict(test_list_mfcc)