import librosa
import numpy as np
from scipy.signal import butter, lfilter
from PIL import Image as PILImage

# --- Parâmetros Padrão ---
TARGET_SR = 2000
IMAGE_HEIGHT = 128

def butter_bandpass_filter(data, lowcut, highcut, fs, order=3):
    """Aplica um filtro passa-banda de Butterworth nos dados."""
    nyq = 0.5 * fs
    low = lowcut / nyq
    high = highcut / nyq
    if high >= 1 or low <= 0:
        return data
    b, a = butter(order, [low, high], btype='band')
    return lfilter(b, a, data)

def create_spectrogram_image(y_cleaned, sr):
    """Cria uma imagem de espectrograma a partir de um sinal de áudio já limpo."""
    mel_spec = librosa.feature.melspectrogram(y=y_cleaned, sr=sr, n_mels=IMAGE_HEIGHT)
    S_DB = librosa.power_to_db(mel_spec, ref=np.max)
    img_array = (S_DB - S_DB.min()) / (S_DB.max() - S_DB.min() + 1e-6) * 255.0
    img_array = img_array.astype(np.uint8)
    return PILImage.fromarray(img_array).convert("RGB")

def calculate_bpm(y_cleaned, sr):
    """Calcula o BPM de um sinal de áudio limpo usando o estimador de ritmo."""
    onset_env = librosa.onset.onset_strength(y=y_cleaned, sr=sr, aggregate=np.mean)
    bpm = librosa.beat.tempo(onset_envelope=onset_env, sr=sr)[0]
    return bpm