Update app.py

app.py

@@ -1,6 +1,5 @@
 import streamlit as st
 from transformers import pipeline
-import torchaudio
 from config import MODEL_ID
 
 # Load the model and pipeline using the model_id variable
@@ -39,15 +38,18 @@ if uploaded_file is not None:
     results_str = "\n".join([f"{label}: {score:.2f}" for label, score in results.items()])
     results_box.text(results_str)
 
-# Examples of audio files for classification
-st.write("Examples:")
+# Audio Test Samples for classification
+st.write("Audio Test Samples:")
 examples = ['normal.wav', 'murmur.wav', 'extra_systole.wav', 'extra_hystole.wav', 'artifact.wav']
-for example in examples:
-    if st.button(example):
-        st.subheader(f"Example: {example}")
+cols = st.columns(3)
+
+for idx, example in enumerate(examples):
+    col = cols[idx % 3]  # Rotate columns for better arrangement
+    if col.button(example):
+        col.subheader(f"Sample Audio: {example}")
         audio_bytes = open(example, 'rb').read()
-        st.audio(audio_bytes, format='audio/wav')
+        col.audio(audio_bytes, format='audio/wav')
         results = classify_audio(example)
-        st.write("Results:")
-        results_str = "\n".join([f"{label}: {score:.4f}" for label, score in results.items()])
-        st.text(results_str)
+        col.write("Results:")
+        results_str = "\n".join([f"{label}: {score:.2f}" for label, score in results.items()])
+        col.text(results_str)