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import requests
import gradio as gr
import os
import torch
import json
# Check if CUDA is available and set the device accordingly
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
API_URL = "https://api-inference.huggingface.co/models/MIT/ast-finetuned-audioset-10-10-0.4593"
headers = {"Authorization": f"Bearer {os.environ.get('HF_TOKEN')}"}
def format_error(message):
"""Helper function to format error messages as JSON"""
return [{"error": message}]
def classify_audio(audio_file):
"""
Classify the uploaded audio file using Hugging Face AST model
"""
if audio_file is None:
return format_error("Please upload an audio file.")
try:
# Debug: Print token status (masked)
token = os.environ.get('HF_TOKEN')
if not token:
return format_error("Error: HF_TOKEN environment variable is not set. Please set your Hugging Face API token.")
print(f"Token present: {'Yes' if token else 'No'}, Token length: {len(token) if token else 0}")
# Debug: Print audio file info
print(f"Audio file path: {audio_file.name}")
print(f"Audio file size: {os.path.getsize(audio_file.name)} bytes")
with open(audio_file.name, "rb") as f:
data = f.read()
print("Sending request to Hugging Face API...")
response = requests.post(API_URL, headers=headers, data=data)
# Print response for debugging
print(f"Response status code: {response.status_code}")
print(f"Response headers: {dict(response.headers)}")
print(f"Response content: {response.content.decode('utf-8', errors='ignore')}")
if response.status_code == 200:
results = response.json()
# Format results for better readability
formatted_results = []
for result in results:
formatted_results.append({
'label': result['label'],
'score': f"{result['score']*100:.2f}%"
})
return formatted_results
elif response.status_code == 401:
return format_error("Error: Invalid or missing API token. Please check your Hugging Face API token.")
elif response.status_code == 503:
return format_error("Error: Model is loading. Please try again in a few seconds.")
else:
error_msg = f"Error: API returned status code {response.status_code}\n"
error_msg += f"Response headers: {dict(response.headers)}\n"
error_msg += f"Response: {response.text}"
return format_error(error_msg)
except Exception as e:
import traceback
error_details = traceback.format_exc()
return format_error(f"Error processing audio: {str(e)}\nDetails:\n{error_details}")
# Create Gradio interface
iface = gr.Interface(
fn=classify_audio,
inputs=gr.Audio(type="filepath", label="Upload Audio File"),
outputs=gr.JSON(label="Classification Results"),
title="Audio Classification using AST Model",
description="Upload an audio file to get its classification results using the Audio Spectrogram Transformer model.",
examples=[],
)
# Launch the interface
if __name__ == "__main__":
iface.launch(server_name="0.0.0.0", server_port=7860) |