Update app.py

app.py

@@ -1,44 +1,58 @@
-import streamlit as st
-import librosa
-import soundfile as sf
-import tempfile
-import os
-from utils.noise_removal import remove_noise
-from utils.vad_segmentation import vad_segmentation
-from utils.speaker_diarization import diarize_speakers
-from utils.noise_classification import classify_noise
-
-st.set_page_config(page_title="Audio Analyzer", layout="wide")
-st.title(" Audio Analysis Pipeline")
-
-uploaded_file = st.file_uploader("Upload an audio file", type=["wav", "mp3"])
-
-if uploaded_file:
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
-        tmp.write(uploaded_file.read())
-        tmp_path = tmp.name
-
-    st.audio(tmp_path, format='audio/wav')
-
-    st.subheader("1️⃣ Noise Removal")
-    denoised_path = tmp_path.replace(".wav", "_denoised.wav")
-    remove_noise(tmp_path, denoised_path)
-    st.audio(denoised_path, format="audio/wav")
-
-    st.subheader("2️⃣ Speech Segmentation")
-    speech_segments = vad_segmentation(denoised_path)
-    st.write(f"Detected {len(speech_segments)} speech segments.")
-    for i, (start, end) in enumerate(speech_segments[:5]):
-        st.write(f"Segment {i+1}: {start:.2f}s to {end:.2f}s")
-
-    st.subheader("3️⃣ Speaker Diarization")
-    diarization = diarize_speakers(denoised_path)
-    st.text("Speakers detected:")
-    for turn, _, speaker in diarization.itertracks(yield_label=True):
-        st.write(f"{turn.start:.2f}s - {turn.end:.2f}s: {speaker}")
-
-    st.subheader("4️⃣ Noise Classification")
-    noise_predictions = classify_noise(denoised_path)
-    st.write("Top predicted noise classes:")
-    for cls, prob in noise_predictions:
-        st.write(f"{cls}: {prob:.2f}")
+import streamlit as st
+import tempfile
+from utils.noise_removal import remove_noise
+from utils.vad_segmentation import vad_segmentation
+from utils.speaker_diarization import diarize_speakers
+from utils.noise_classification import classify_noise
+
+st.set_page_config(page_title="Audio Analyzer", layout="wide")
+st.title(" Audio Analysis Pipeline")
+
+uploaded_file = st.file_uploader("📤 Upload an audio file", type=["wav", "mp3"])
+
+if uploaded_file:
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
+        tmp.write(uploaded_file.read())
+        tmp_path = tmp.name
+
+    st.audio(tmp_path, format='audio/wav')
+
+    try:
+        st.subheader("1️⃣ Noise Removal")
+        denoised_path = tmp_path.replace(".wav", "_denoised.wav")
+        with st.spinner("Removing noise..."):
+            remove_noise(tmp_path, denoised_path)
+        st.audio(denoised_path, format="audio/wav")
+    except Exception as e:
+        st.error(f"Noise removal failed: {e}")
+
+    try:
+        st.subheader("2️⃣ Speech Segmentation")
+        with st.spinner("Running Voice Activity Detection..."):
+            speech_annotation = vad_segmentation(denoised_path)
+            segments = [(seg.start, seg.end) for seg in speech_annotation.itersegments()]
+        st.write(f" Detected {len(segments)} speech segments.")
+        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}")
+
+    try:
+        st.subheader("3️⃣ Speaker Diarization")
+        with st.spinner("Diarizing speakers..."):
+            diarization = diarize_speakers(denoised_path)
+        st.text(" Speakers detected:")
+        for turn, _, speaker in diarization.itertracks(yield_label=True):
+            st.write(f"{turn.start:.2f}s - {turn.end:.2f}s: Speaker {speaker}")
+    except Exception as e:
+        st.error(f"Speaker diarization failed: {e}")
+
+    try:
+        st.subheader("4️⃣ Noise Classification")
+        with st.spinner("Classifying background noise..."):
+            noise_predictions = classify_noise(denoised_path)
+        st.write(" Top predicted noise classes:")
+        for cls, prob in noise_predictions:
+            st.write(f"{cls}: {prob:.2f}")
+    except Exception as e:
+        st.error(f"Noise classification failed: {e}")