import torch
from torch import nn
import torchaudio

from audiofeat.spectral import mfcc  # Assuming we reuse existing MFCC for input

class EmotionDetector(nn.Module):
    def __init__(self):
        super(EmotionDetector, self).__init__()
        # Simple MLP for demo; in practice, use a pre-trained model like from torchaudio or Hugging Face
        self.fc1 = nn.Linear(40, 128)  # Assuming MFCC input of 40 coefficients
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 7)  # 7 classes: neutral, happy, sad, angry, fear, disgust, surprise
        self.relu = nn.ReLU()
    
    def forward(self, features):
        x = self.relu(self.fc1(features))
        x = self.relu(self.fc2(x))
        return self.fc3(x)  # Output: logits for emotion classes

def detect_emotion(waveform: torch.Tensor, sample_rate: int) -> str:
    # Extract features (e.g., MFCC)
    mfcc_features = mfcc(waveform, sample_rate)  # shape: (n_mfcc, n_frames)
    if mfcc_features.ndim == 2:
        mfcc_features = mfcc_features.mean(dim=1)
    elif mfcc_features.ndim != 1:
        mfcc_features = mfcc_features.reshape(-1)

    if mfcc_features.numel() != 40:
        if mfcc_features.numel() > 40:
            mfcc_features = mfcc_features[:40]
        else:
            mfcc_features = torch.nn.functional.pad(
                mfcc_features,
                (0, 40 - mfcc_features.numel()),
            )

    mfcc_features = mfcc_features.unsqueeze(0)
    model = EmotionDetector()  # In practice, load pre-trained weights
    with torch.no_grad():
        output = model(mfcc_features)
        predicted = torch.argmax(output, dim=1)
        emotions = ['neutral', 'happy', 'sad', 'angry', 'fear', 'disgust', 'surprise']
        return emotions[predicted.item()]  # Return the predicted emotion
