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

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

def take_last_tokens(inputs, note_history, history):
    filterTokenCount = 128 # filter last 128 tokens
    if inputs['input_ids'].shape[1] > filterTokenCount:
        inputs['input_ids'] = torch.tensor([inputs['input_ids'][0][-filterTokenCount:].tolist()])
        inputs['attention_mask'] = torch.tensor([inputs['attention_mask'][0][-filterTokenCount:].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):
    note_history.append(note)
    note_history = '</s> <s>'.join(note_history)
    return [note_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)
    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]
        
    
    state = state + transcriptions
    return state, state

gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Audio(source="microphone", type='filepath', streaming=True),
        "state",
    ],
    outputs=[
        "textbox",
        "state"
    ],
    layout="horizontal",
    theme="huggingface",
    title="ASR",
    description=f"Automatic Speech Recognition (ASR)",
    allow_flagging='never',
    live=True
).launch(debug=True)