Update app.py

app.py

@@ -19,7 +19,7 @@ def load_class_map():
 
 class_names = load_class_map()
 
-# Classification function for binary audio input
+# Classification function
 def classify_audio(audio, sample_rate):
     try:
         # Convert stereo to mono
@@ -29,7 +29,7 @@ def classify_audio(audio, sample_rate):
         # Normalize
         audio = audio / np.max(np.abs(audio))
 
-        # Resample if needed
+        # Resample to 16kHz if needed
         target_sr = 16000
         if sample_rate != target_sr:
             duration = audio.shape[0] / sample_rate
@@ -47,7 +47,7 @@ def classify_audio(audio, sample_rate):
 
         # Extract predictions
         top_prediction = class_names[top_5[0]]
-        top_scores = {class_names[i]: float(mean_scores[i]) for i in top_5]
+        top_scores = {class_names[i]: float(mean_scores[i]) for i in top_5}
 
         # Create waveform plot
         fig, ax = plt.subplots()
@@ -62,7 +62,7 @@ def classify_audio(audio, sample_rate):
     except Exception as e:
         return f"Error: {str(e)}", {}, None
 
-# Gradio Interface (IMPORTANT: type="numpy" allows binary POSTs from n8n)
+# Gradio Interface (binary audio compatible for n8n)
 interface = gr.Interface(
     fn=classify_audio,
     inputs=gr.Audio(source="upload", type="numpy", label="Upload .wav or .mp3"),