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import streamlit as st
import requests
import os
# Hugging Face API setup
API_URL = "https://api-inference.huggingface.co/models/MIT/ast-finetuned-audioset-10-10-0.4593"
HF_TOKEN = os.getenv("HF_TOKEN")
headers = {"Authorization": f"Bearer {HF_TOKEN}"}
# Function to send the audio file to the Hugging Face API and get the classification result
def classify_audio(audio_file_path):
with open(audio_file_path, "rb") as audio_file:
response = requests.post(
"https://api-inference.huggingface.co/models/MIT/ast-finetuned-audioset-10-10-0.4593",
headers=headers,
files={"file": audio_file}
)
# Check the response status and return JSON if successful, or an error message
if response.status_code == 200:
return response.json()
else:
return {"error": f"Failed to classify audio. Status code: {response.status_code}"}
# Streamlit interface
st.title("Audio Classifier")
# Define the folder where your audio files are located
audio_folder = "audio_files"
# List the audio files in the folder
try:
audio_files = os.listdir(audio_folder)
audio_file_options = [f for f in audio_files if f.endswith(('.mp3', '.wav'))]
except Exception as e:
st.error(f"Error accessing the audio files: {e}")
st.stop()
# Dropdown to select an audio file
selected_file = st.selectbox("Select an audio file:", audio_file_options)
# Button to classify the selected audio file
if st.button("Classify"):
# Get the full path of the selected audio file
audio_file_path = os.path.join(audio_folder, selected_file)
# Show the audio player
st.audio(audio_file_path)
# Get and display the classification results
results = classify_audio(audio_file_path)
# Check for errors
if "error" in results:
st.error(results["error"])
else:
st.write("Results:")
# Loop through each result in the list of results
for result in results:
# Check if result is a dictionary with 'label' and 'score' keys
if isinstance(result, dict) and 'label' in result and 'score' in result:
st.write(f"Label: {result['label']}, Confidence: {result['score']:.2f}")
else:
st.error(f"Unexpected result format: {result}")
# Get and display the classification results
results = classify_audio(audio_file_path)
st.write("Results:")
for result in results:
st.write(f"Label: {result['label']}, Confidence: {result['score']:.2f}") |