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
import PIL

learn = load_learner('model.pkl')
labels = learn.dls.vocab

def audio_to_spectrogram(audio_file):
    if isinstance(audio_file, str):
        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)
    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))}

gr.Interface(
    fn=predict,
    inputs=[
        gr.Audio(sources=["upload", "microphone"], type="filepath", label="Upload or Record audio (WAV or MP3)"),
    ],
    outputs=gr.components.Label(num_top_classes=3),
    live=True
).launch()