Update app.py

app.py

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+import gradio as gr
+from fastai.vision.all import *
+import librosa
+import numpy as np
+import matplotlib.pyplot as plt
+from pydub import AudioSegment
+import tempfile
+
+learn = load_learner('model.pkl')
+labels = learn.dls.vocab
+
+def audio_to_spectrogram(audio_file):
+    if audio_file.endswith('.mp3'):
+        with tempfile.NamedTemporaryFile(suffix='.wav') as temp_wav:
+            audio = AudioSegment.from_mp3(audio_file)
+            audio.export(temp_wav.name, format='wav')
+            y, sr = librosa.load(temp_wav.name, sr=None)
+    else:
+        y, sr = librosa.load(audio_file, sr=None)
+
+    S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128, fmax=8000)
+    S_dB = librosa.power_to_db(S, ref=np.max)
+    fig, ax = plt.subplots()
+    img = librosa.display.specshow(S_dB, x_axis='time', y_axis='mel', sr=sr, fmax=8000, ax=ax)
+    fig.colorbar(img, ax=ax, format='%+2.0f dB')
+    ax.set(title='Mel-frequency spectrogram')
+    spectrogram_file = "spectrogram.png"
+    plt.savefig(spectrogram_file)
+    plt.close()
+    return spectrogram_file
+
+def predict(audio):
+    spectrogram_file = audio_to_spectrogram(audio)
+    img = PILImage.create(spectrogram_file)
+    img = img.resize((512, 512))
+    pred, pred_idx, probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+examples = ['example_audio.mp3']
+
+gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Audio(source="upload", type="filepath", label="Upload audio (WAV or MP3)"),
+    outputs=gr.components.Label(num_top_classes=3),
+    examples=examples,
+).launch()