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app.py

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+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
+pipe = pipeline("audio-classification", model=MODEL_ID)
+
+def classify_audio(filepath):
+    preds = pipe(filepath)
+    outputs = {}
+    for p in preds:
+        outputs[p["label"]] = 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:
+    # 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
+    st.write("Classification results:")
+    for label, score in results.items():
+        st.write(f"{label}: {score:.4f}")
+
+# Examples of audio files for classification
+st.write("Examples:")
+examples = ['normal.wav', 'murmur.wav', 'extra_systole.wav', 'extra_hystole.wav', 'artifact.wav']
+for example in examples:
+    st.write(example)
+    audio_bytes = open(example, 'rb').read()
+    st.audio(audio_bytes, format='audio/wav')
+    results = classify_audio(example)
+    for label, score in results.items():
+        st.write(f"{label}: {score:.4f}")

config.py

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+# config.py
+MODEL_ID = "arham061/distilhubert-finetuned-PASCAL_Dataset_Augmented"

requirements.txt

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+gradio
+torch
+transformers