import os
from pathlib import Path
import numpy as np
import sherpa_onnx
import scipy.signal
from opencc import OpenCC

# Ensure Hugging Face cache is in a user-writable directory
CACHE_DIR = Path(__file__).parent / "hf_cache"
os.makedirs(CACHE_DIR, exist_ok=True)

to_ZHTW = OpenCC('s2t')
to_ZHCN = OpenCC('t2s')

# Streaming Zipformer model registry: paths relative to repo root

# Audio resampling utility
def resample_audio(audio: np.ndarray, orig_sr: int, target_sr: int) -> np.ndarray:
    return scipy.signal.resample_poly(audio, target_sr, orig_sr)

# Create an online recognizer for a given model and precision
# model_id: full HF repo ID
# precision: "int8" or "fp32"
def create_recognizer():

    tokens_path = 'app/model parts/tokens.txt'
    encoder_path = 'app/model parts/encoder-epoch-35-avg-7-chunk-32-left-256.fp16.onnx'
    decoder_path = 'app/model parts/decoder-epoch-35-avg-7-chunk-32-left-256.fp16.onnx'
    joiner_path = 'app/model parts/joiner-epoch-35-avg-7-chunk-32-left-256.fp16.onnx'


    # ——— Fallback to original greedy-search (no hotword biasing) ———
    return sherpa_onnx.OnlineRecognizer.from_transducer(
        tokens=tokens_path,
        encoder=encoder_path,
        decoder=decoder_path,
        joiner=joiner_path,
        provider="cpu",
        # device=0,
        num_threads=1,
        sample_rate=16000,
        feature_dim=80,
        decoding_method="greedy_search",
        # endpoint detection parameters
        enable_endpoint_detection=True,
    )

def stream_audio(raw_pcm_bytes, stream, recognizer, orig_sr):
    audio = np.frombuffer(raw_pcm_bytes, dtype=np.float32)
    if audio.size == 0:
        return "", 0.0

    resampled = resample_audio(audio, orig_sr, 16000)
    rms = float(np.sqrt(np.mean(resampled ** 2)))

    stream.accept_waveform(16000, resampled)
    if recognizer.is_ready(stream):
        # print(stream)
        recognizer.decode_streams([stream])
    result = recognizer.get_result(stream)
    return to_ZHTW.convert(result), rms