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})"
    article=f"Important Videos to understanding AI and NLP Clinical Terminology, Assessment, and Value Based Care AI include Huggingfaces Course Series here: https://www.youtube.com/c/HuggingFace , AI NLP Innovations in 2022 for Clinical and Mental Health Care here: https://www.youtube.com/watch?v=r38lXjz3g6M&list=PLHgX2IExbFov_5_4WfkesR7gnWPHHG-a1  and this link to see and manage playlist here: https://www.youtube.com/playlist?list=PLHgX2IExbFov_5_4WfkesR7gnWPHHG-a1  Review at your leisure to understand AI and NLP impact to helping the world develop Clinical systems of the future using AI and NLP for Clinical Terminology and alignment to worldwide Value Based Care objectives to help people be healthy."
)
iface.launch()