Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+import torch
+import os
+
+# --- Performance Improvement ---
+# 1. Determine the number of available CPU cores.
+num_cpu_cores = os.cpu_count()
+
+# 2. Configure PyTorch to use all available CPU cores for its operations.
+# This is crucial for speeding up model inference on a CPU.
+if num_cpu_cores is not None:
+    torch.set_num_threads(num_cpu_cores)
+    print(f"✅ PyTorch is configured to use {num_cpu_cores} CPU cores.")
+else:
+    print("Could not determine the number of CPU cores. Using default settings.")
+
+
+# Initialize the audio classification pipeline
+# We specify device=-1 to explicitly enforce running on the CPU.
+pipe = pipeline(
+    "audio-classification",
+    model="MIT/ast-finetuned-audioset-10-10-0.4593",
+    device=-1  # -1 for CPU, 0 for the first GPU, etc.
+)
+
+# Define the function to classify an audio file
+def classify_audio(audio_filepath):
+    """
+    Takes an audio file path, classifies it using the pipeline,
+    and returns a dictionary of top labels and their scores.
+    """
+    if audio_filepath is None:
+        return "Please upload an audio file first."
+
+    # The pipeline handles the loading, preprocessing, and inference.
+    result = pipe(audio_filepath)
+    # The output is formatted for the Gradio Label component.
+    return {label['label']: label['score'] for label in result}
+
+# Set up the Gradio interface
+app = gr.Interface(
+    fn=classify_audio,                  # Function to classify audio
+    inputs=gr.Audio(type="filepath", label="Upload Audio"), # Input for uploading an audio file
+    outputs=gr.Label(num_top_classes=3, label="Top 3 Predictions"), # Output with top 3 classification results
+    title="High-Performance Audio Classification (CPU)", # App title
+    description="Upload an audio file to classify it. This app is optimized to run on all available CPU cores.",
+    examples=[
+        # You can add example audio files here if you have them locally
+        # ["path/to/your/example_audio_1.wav"],
+        # ["path/to/your/example_audio_2.mp3"],
+    ]
+)
+
+# Launch the app
+if __name__ == "__main__":
+    app.launch()