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import streamlit as st
from transformers import pipeline
from config import MODEL_ID
# Load the model and pipeline using the model_id variable
pipe = pipeline("audio-classification", model=MODEL_ID)
def classify_audio(filepath):
preds = pipe(filepath)
outputs = {"normal": 0.0, "murmur": 0.0, "artifact": 0.0}
for p in preds:
label = p["label"].replace('_', ' ')
if label in outputs:
outputs[label] += p["score"]
else:
outputs["normal"] += p["score"]
return outputs
# Streamlit app layout
st.title("Heartbeat Sound Classification")
# File uploader for audio files
uploaded_file = st.file_uploader("Upload an audio file", type=["wav", "mp3"])
if uploaded_file is not None:
st.subheader("Uploaded Audio File")
# Load and display the audio file
audio_bytes = uploaded_file.read()
st.audio(audio_bytes, format='audio/wav')
# Save the uploaded file to a temporary location
with open("temp_audio_file.wav", "wb") as f:
f.write(audio_bytes)
# Classify the audio file
st.write("Classifying the audio...")
results = classify_audio("temp_audio_file.wav")
# Display the classification results in a dedicated output box
st.subheader("Classification Results")
results_box = st.empty()
results_str = "\n".join([f"{label}: {score:.2f}" for label, score in results.items()])
results_box.text(results_str)
# Audio Test Samples for classification
st.write("Audio Test Samples:")
examples = ['normal.wav', 'murmur.wav', 'extra_systole.wav', 'extra_hystole.wav', 'artifact.wav']
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()
col.audio(audio_bytes, format='audio/wav')
results = classify_audio(example)
col.write("Results:")
results_str = "\n".join([f"{label}: {score:.2f}" for label, score in results.items()])
col.text(results_str)
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