app.py

import gradio as gr
from fastai.vision.all import *
import numpy as np
import matplotlib.pyplot as plt
import tempfile
import sounddevice as sd
import soundfile as sf

# Load your trained model and define labels
learn = load_learner('model.pkl')
labels = learn.dls.vocab

def record_audio(duration=3, sr=44100, channels=1):
    print("Recording...")
    audio = sd.rec(int(duration * sr), samplerate=sr, channels=channels, dtype='float32')
    sd.wait()
    print("Recording stopped.")
    return audio, sr

def audio_to_spectrogram(audio_file, sr): 
        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=audio[:, 0], 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):
    audio_data, sr = sf.read(audio)
    spectrogram_file = audio_to_spectrogram(audio_data, sr)
    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))}

# Launch the interface
examples = [['example_audio.mp3']]
gr.Interface(
    fn=predict,
    inputs=gr.Audio(sources="microphone", type="file", label="Record audio (WAV)"),
    outputs=gr.components.Label(num_top_classes=3),
    examples=examples,  
).launch()