Update app.py

app.py

@@ -12,17 +12,21 @@ st.title("Audio Analysis Pipeline")
 
 uploaded_file = st.file_uploader("Upload an audio file", type=["wav", "mp3", "m4a", "mp4a"])
 
-def convert_to_wav(uploaded_file):
+def prepare_audio(uploaded_file):
+    file_ext = uploaded_file.name.split('.')[-1].lower()
     with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as out_wav:
-        audio = AudioSegment.from_file(uploaded_file, format=uploaded_file.name.split('.')[-1])
-        audio.export(out_wav.name, format="wav")
+        if file_ext == "wav":
+            out_wav.write(uploaded_file.read())
+        else:
+            audio = AudioSegment.from_file(uploaded_file, format=file_ext)
+            audio.export(out_wav.name, format="wav")
         return out_wav.name
 
 if uploaded_file:
     st.audio(uploaded_file, format="audio/wav")
 
-    with st.spinner(" Converting to WAV..."):
-        tmp_path = convert_to_wav(uploaded_file)
+    with st.spinner("🔄 Preparing audio..."):
+        tmp_path = prepare_audio(uploaded_file)
 
     try:
         st.subheader("1️⃣ Noise Removal")
@@ -31,7 +35,7 @@ if uploaded_file:
             remove_noise(tmp_path, denoised_path)
         st.audio(denoised_path, format="audio/wav")
     except Exception as e:
-        st.error(f"Noise removal failed: {e}")
+        st.error(f" Noise removal failed: {e}")
 
     try:
         st.subheader("2️⃣ Speech Segmentation")
@@ -42,7 +46,7 @@ if uploaded_file:
         for i, (start, end) in enumerate(segments[:5]):
             st.write(f"Segment {i+1}: {start:.2f}s to {end:.2f}s")
     except Exception as e:
-        st.error(f"VAD failed: {e}")
+        st.error(f" VAD failed: {e}")
 
     try:
         st.subheader("3️⃣ Speaker Diarization")
@@ -58,7 +62,7 @@ if uploaded_file:
         st.subheader("4️⃣ Noise Classification")
         with st.spinner("Classifying background noise..."):
             noise_predictions = classify_noise(denoised_path)
-        st.write(" Top predicted noise classes:")
+        st.write("Top predicted noise classes:")
         for cls, prob in noise_predictions:
             st.write(f"{cls}: {prob:.2f}")
     except Exception as e: