Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+import os
+
+# Define your Hugging Face token; secure this appropriately
+HF_TOKEN = os.getenv('HF_TOKEN')
+
+# Set up Hugging Face pipeline for audio classification using the specified model
+model_name = "MIT/ast-finetuned-audioset-10-10-0.4593"
+
+@st.cache(allow_output_mutation=True)
+def load_model(token, model_name):
+    return pipeline("audio-classification", model=model_name, use_auth_token=token)
+
+audio_classifier = load_model(HF_TOKEN, model_name)
+
+# Pre-uploaded audio files
+audio_files = {
+    "Labrador Barking": "labrador-barking.mp3",
+    "Tolling Bell": "tolling-bell.mp3",
+    "Airplane Landing": "airplane-landing.mp3",
+    "Old Car Engine": "old-car-engine.mp3",
+    "Hard Shoes": "hard_shoes.mp3",
+    "Alien Spaceship": "alien-spaceship.mp3",
+}
+
+# Streamlit UI
+st.title("Audio Classification with Pre-uploaded Files")
+
+# Audio file selection
+selected_audio_name = st.selectbox("Select an audio file", list(audio_files.keys()))
+audio_file_path = audio_files[selected_audio_name]
+
+# Perform classification
+if st.button("Classify"):
+    # Read audio file
+    with open(audio_file_path, "rb") as audio_file:
+        audio_bytes = audio_file.read()
+
+    results = audio_classifier(audio_bytes)
+
+    # Displaying results
+    st.write("Classification Results:")
+    for result in results:
+        label = result['label']
+        score = round(result['score'], 4)  # Adjust rounding as needed
+        st.write(f"Label: {label}, Score: {score}")