import pdb
import time

import streamlit as st
import os
from utils import wm_add_v2, file_reader, model_util, wm_decode_v2, bin_util
from models import my_model_v7_recover
import torch
import uuid
import datetime
import numpy as np
import soundfile
from huggingface_hub import hf_hub_download, HfApi


# Function to add watermark to audio
def add_watermark(audio_path, watermark_text):
    assert len(watermark_text) == 5

    start_bit, msg_bit, watermark = wm_add_v2.create_parcel_message(len_start_bit, 32, watermark_text)

    data, sr, audio_length_second = file_reader.read_as_single_channel_16k(audio_path, 16000)

    _, signal_wmd, time_cost = wm_add_v2.add_watermark(watermark, data, 16000, 0.1, device, model)

    tmp_file_name = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + "_" + str(uuid.uuid4()) + ".wav"
    tmp_file_path = '/tmp/' + tmp_file_name
    soundfile.write(tmp_file_path, signal_wmd, sr)
    return tmp_file_path


# Function to decode watermark from audio
def decode_watermark(audio_path):
    data, sr, audio_length_second = file_reader.read_as_single_channel_16k(audio_path, 16000)
    data = data[0:5 * sr]
    start_bit = wm_add_v2.fix_pattern[0:len_start_bit]
    support_count, mean_result, results = wm_decode_v2.extract_watermark_v2(
        data,
        start_bit,
        0.1,
        16000,
        0.3,
        model,
        device, "best")

    if mean_result is None:
        return "No Watermark"

    payload = mean_result[len_start_bit:]
    return bin_util.binArray2HexStr(payload)


# Main web app
def main():
    max_upload_size = 20 * 1024 * 1024  # 20 MB in bytes

    if "def_value" not in st.session_state:
        st.session_state.def_value = bin_util.binArray2HexStr(np.random.choice([0, 1], size=32 - len_start_bit))

    st.title("Neural Audio Watermark")
    st.write("Choose the action you want to perform:")

    action = st.selectbox("Select Action", ["Add Watermark", "Decode Watermark"])

    if action == "Add Watermark":
        audio_file = st.file_uploader("Upload Audio File (WAV)", type=["wav"], accept_multiple_files=False,
                                      max_upload_size=max_upload_size)
        if audio_file:
            tmp_input_audio_file = os.path.join("/tmp/", audio_file.name)
            with open(tmp_input_audio_file, "wb") as f:
                f.write(audio_file.getbuffer())
            st.audio(tmp_input_audio_file, format="audio/wav")

            watermark_text = st.text_input("Enter Watermark Text (5 English letters)", value=st.session_state.def_value)

            add_watermark_button = st.button("Add Watermark", key="add_watermark_btn")
            if add_watermark_button:  # 点击按钮后执行的
                if audio_file and watermark_text:
                    with st.spinner("Adding Watermark..."):
                        # add_watermark_button.empty()
                        # st.button("Add Watermark", disabled=True)
                        # st.button("Add Watermark", disabled=True, key="add_watermark_btn_disabled")
                        t1 = time.time()

                        watermarked_audio = add_watermark(tmp_input_audio_file, watermark_text)
                        encode_time_cost = time.time() - t1

                        st.write("Watermarked Audio:")
                        st.audio(watermarked_audio, format="audio/wav")
                        st.write("Time Cost:%d seconds" % encode_time_cost)

                        # st.button("Add Watermark", disabled=False)

    elif action == "Decode Watermark":
        audio_file = st.file_uploader("Upload Audio File (WAV/MP3)", type=["wav", "mp3"], accept_multiple_files=False,
                                      max_upload_size=max_upload_size)
        if audio_file:
            if st.button("Decode Watermark"):
                # 1.保存
                tmp_file_for_decode_path = os.path.join("/tmp/", audio_file.name)
                with open(tmp_file_for_decode_path, "wb") as f:
                    f.write(audio_file.getbuffer())

                # 2.执行
                with st.spinner("Decoding..."):
                    t1 = time.time()
                    decoded_watermark = decode_watermark(tmp_file_for_decode_path)
                    decode_cost = time.time() - t1

                print("decoded_watermark", decoded_watermark)
                # Display the decoded watermark
                st.write("Decoded Watermark:", decoded_watermark)
                st.write("Time Cost:%d seconds" % (decode_cost))


def load_model(resume_path):
    n_fft = 1000
    hop_length = 400
    # https://huggingface.co/M4869/InvertibleWM/blob/main/step59000_snr39.99_pesq4.35_BERP_none0.30_mean1.81_std1.81.pkl
    # api_key = st.secrets["api_key"]
    # print(api_key, api_key)
    api_key = "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"
    model_ckpt_path = hf_hub_download(repo_id="M4869/InvertibleWM",
                                      filename="step59000_snr39.99_pesq4.35_BERP_none0.30_mean1.81_std1.81.pkl",
                                      token=api_key
                                      )
    # print("model_ckpt_path", model_ckpt_path)
    resume_path = model_ckpt_path
    # return

    model = my_model_v7_recover.Model(16000, 32, n_fft, hop_length,
                                      use_recover_layer=False, num_layers=8).to(device)
    checkpoint = torch.load(resume_path, map_location=torch.device('cpu'))
    state_dict = model_util.map_state_dict(checkpoint['model'])
    model.load_state_dict(state_dict, strict=True)
    model.eval()
    return model


if __name__ == "__main__":
    len_start_bit = 12

    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

    model = load_model("./data/step59000_snr39.99_pesq4.35_BERP_none0.30_mean1.81_std1.81.pkl")

    main()
    # decode_watermark("/Users/my/Downloads/7a95b353a46893903e9f946c24170b210ce14e8c52c63bb2ab3d144e.wav")