# emotion_detector.py
import os
import torchaudio
import tempfile
from speechbrain.inference.classifiers import AudioClassifier

# Set HF_HOME to writable temp directory
os.environ["HF_HOME"] = os.path.join(tempfile.gettempdir(), "hf_cache")

# Also set Hugging Face cache dir explicitly (optional but helps)
os.environ["HF_DATASETS_CACHE"] = os.environ["HF_HOME"]
os.environ["TRANSFORMERS_CACHE"] = os.environ["HF_HOME"]
os.environ["HUGGINGFACE_HUB_CACHE"] = os.environ["HF_HOME"]

# Use a temp directory for SpeechBrain model saving
model_dir = os.path.join(tempfile.gettempdir(), "emotion_model")

# Load pretrained model
classifier = AudioClassifier.from_hparams(
    source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP",
    savedir=model_dir
)

EMOTION_EMOJIS = {
    "angry": "😠",
    "happy": "😄",
    "neutral": "😐",
    "sad": "😢",
    "fearful": "😨"
}

def detect_emotion(audio_path):
    signal, fs = torchaudio.load(audio_path)
    prediction = classifier.classify_file(audio_path)
    emotion = prediction[3]  # Predicted label
    emoji = EMOTION_EMOJIS.get(emotion.lower(), "❓")
    return emotion, emoji