import os
import streamlit as st

CACHE_DIR = "/tmp/pretrained_models"

import torch
import gc
import time

from speechbrain.pretrained.interfaces import foreign_class
from faster_whisper import WhisperModel

# Ensure the folder exists and is writable
os.makedirs(CACHE_DIR, exist_ok=True)

# -------------------------------
# Load Model (Cached)
# -------------------------------

@st.cache_resource(show_spinner="Loading model...") # making sure we only load the model once per every app instance
def load_accent_model():
    """Loads custom accent classification model."""
    if not os.getenv("HF_TOKEN"):
        st.error("Hugging Face token not found.")
        st.stop()
    try:
        return foreign_class(
            source="Jzuluaga/accent-id-commonaccent_xlsr-en-english",
            pymodule_file="custom_interface.py",
            classname="CustomEncoderWav2vec2Classifier",
            
        )
    except Exception as e:
        st.error(f"Error loading model: {e}")
        st.stop()

@st.cache_resource(show_spinner="Loading Whisper...")
def load_whisper():
    return WhisperModel("tiny", device="cpu", compute_type="int8_float32")

def unload_model(model):
    del model
    torch.cuda.empty_cache()
    gc.collect()
    time.sleep(5)  # give system time to clean up before moving on