import gradio as gr
import torch
import time
import librosa
import soundfile
import nemo.collections.asr as nemo_asr
import tempfile
import os
import uuid

from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
import torch

# PersistDataset -----
import os
import csv
import gradio as gr
from gradio import inputs, outputs
import huggingface_hub
from huggingface_hub import Repository, hf_hub_download, upload_file
from datetime import datetime

# ---------------------------------------------
# Dataset and Token links - change awacke1 to your own HF id, and add a HF_TOKEN copy to your repo for write permissions
# This should allow you to save your results to your own Dataset hosted on HF. ---
#DATASET_REPO_URL = "https://huggingface.co./datasets/awacke1/Carddata.csv"
#DATASET_REPO_ID = "awacke1/Carddata.csv"
#DATA_FILENAME = "Carddata.csv"
#DATA_FILE = os.path.join("data", DATA_FILENAME)
#HF_TOKEN = os.environ.get("HF_TOKEN")
#SCRIPT = """

#<script>
#if (!window.hasBeenRun) {
#    window.hasBeenRun = true;
#    console.log("should only happen once");
#    document.querySelector("button.submit").click();
#}
#</script>
#"""

#try:
#    hf_hub_download(
#        repo_id=DATASET_REPO_ID,
#        filename=DATA_FILENAME,
#        cache_dir=DATA_DIRNAME,
#        force_filename=DATA_FILENAME
#    )
#except:
#    print("file not found")
#repo = Repository(
#    local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
#)
           
#def store_message(name: str, message: str):
#    if name and message:
#        with open(DATA_FILE, "a") as csvfile:
#            writer = csv.DictWriter(csvfile, fieldnames=["name", "message", "time"])
#            writer.writerow(
#                {"name": name.strip(), "message": message.strip(), "time": str(datetime.now())}
#            )
#        # uncomment line below to begin saving - 
#        commit_url = repo.push_to_hub()
#    return ""            

#iface = gr.Interface(
#    store_message,
#    [
#        inputs.Textbox(placeholder="Your name"),
#        inputs.Textbox(placeholder="Your message", lines=2),
#    ],
#    "html",
#    css="""
#    .message {background-color:cornflowerblue;color:white; padding:4px;margin:4px;border-radius:4px; }
#    """,
#    title="Reading/writing to a HuggingFace dataset repo from Spaces",
#    description=f"This is a demo of how to do simple *shared data persistence* in a Gradio Space, backed by a dataset repo.",
#    article=f"The dataset repo is [{DATASET_REPO_URL}]({DATASET_REPO_URL})",
#)


# main -------------------------
mname = "facebook/blenderbot-400M-distill"
model = BlenderbotForConditionalGeneration.from_pretrained(mname)
tokenizer = BlenderbotTokenizer.from_pretrained(mname)

def take_last_tokens(inputs, note_history, history):
    """Filter the last 128 tokens"""
    if inputs['input_ids'].shape[1] > 128:
        inputs['input_ids'] = torch.tensor([inputs['input_ids'][0][-128:].tolist()])
        inputs['attention_mask'] = torch.tensor([inputs['attention_mask'][0][-128:].tolist()])
        note_history = ['</s> <s>'.join(note_history[0].split('</s> <s>')[2:])]
        history = history[1:]
    return inputs, note_history, history

def add_note_to_history(note, note_history):
    """Add a note to the historical information"""
    note_history.append(note)
    note_history = '</s> <s>'.join(note_history)
    return [note_history]


def chat(message, history):
    history = history or []
    if history: 
        history_useful = ['</s> <s>'.join([str(a[0])+'</s> <s>'+str(a[1]) for a in history])]
    else:
        history_useful = []
    history_useful = add_note_to_history(message, history_useful)
    inputs = tokenizer(history_useful, return_tensors="pt")
    inputs, history_useful, history = take_last_tokens(inputs, history_useful, history)
    reply_ids = model.generate(**inputs)
    response = tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0]
    history_useful = add_note_to_history(response, history_useful)
    list_history = history_useful[0].split('</s> <s>')
    history.append((list_history[-2], list_history[-1]))
#    store_message(message, response) # Save to dataset - uncomment if you uncomment above to save inputs and outputs to your dataset
    return history, history
    

SAMPLE_RATE = 16000
model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("nvidia/stt_en_conformer_transducer_xlarge")
model.change_decoding_strategy(None)
model.eval()

def process_audio_file(file):
    data, sr = librosa.load(file)
    if sr != SAMPLE_RATE:
        data = librosa.resample(data, orig_sr=sr, target_sr=SAMPLE_RATE)
    # monochannel
    data = librosa.to_mono(data)
    return data


def transcribe(audio, state = ""):   
    if state is None:
        state = ""
    audio_data = process_audio_file(audio)
    with tempfile.TemporaryDirectory() as tmpdir:
        audio_path = os.path.join(tmpdir, f'audio_{uuid.uuid4()}.wav')
        soundfile.write(audio_path, audio_data, SAMPLE_RATE)
        transcriptions = model.transcribe([audio_path])
        if type(transcriptions) == tuple and len(transcriptions) == 2:
            transcriptions = transcriptions[0]
        transcriptions = transcriptions[0]
#    store_message(transcriptions, state) # Save to dataset - uncomment to store into a dataset - hint you will need your HF_TOKEN
    state = state + transcriptions + " "
    return state, state

iface = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Audio(source="microphone", type='filepath', streaming=True),
        "state",
    ],
    outputs=[
        "textbox",
        "state",
    ],
    layout="horizontal",
    theme="huggingface",
    title="🗣️LiveSpeechRecognition🧠Memory💾",
    description=f"Live Automatic Speech Recognition (ASR) with Memory💾 Dataset.",
    allow_flagging='never',
    live=True,    
#    article=f"Result Output Saved to Memory💾 Dataset: [{DATASET_REPO_URL}]({DATASET_REPO_URL})"
)
iface.launch()