import gradio as gr
from nemo.collections.asr.models import ASRModel

# Load the NeMo ASR model
model = ASRModel.from_pretrained("nvidia/canary-1b")
model.eval()

def transcribe(audio):
    if audio is None:
        raise gr.InterfaceError("Please provide some input audio: either upload an audio file or use the microphone")

    # Perform speech recognition
    transcription = model.transcribe([audio])

    return transcription[0]

audio_input = gr.components.Audio()

iface = gr.Interface(transcribe, audio_input, "text", title="ASR with NeMo Canary Model")
iface.launch()

'''
import gradio as gr
from transformers import pipeline
# Load pipelines for Canary ASR, LLama3 QA, and VITS TTS
asr_pipeline = pipeline("automatic-speech-recognition", model="nvidia/canary-1b", device=0)
qa_pipeline = pipeline("question-answering", model="LLAMA/llama3-base-qa", tokenizer="LLAMA/llama3-base-qa")
tts_pipeline = pipeline("text-to-speech", model="patrickvonplaten/vits-large", device=0)


import gradio as gr
import json
import librosa
import os
import soundfile as sf
import tempfile
import uuid
from transformers import pipeline

import torch

from nemo.collections.asr.models import ASRModel
from nemo.collections.asr.parts.utils.streaming_utils import FrameBatchMultiTaskAED
from nemo.collections.asr.parts.utils.transcribe_utils import get_buffered_pred_feat_multitaskAED

SAMPLE_RATE = 16000 # Hz
MAX_AUDIO_SECS = 30 # wont try to transcribe if longer than this
src_lang = "en"
tgt_lang = "en"
pnc="no"

model = ASRModel.from_pretrained("nvidia/canary-1b")
model.eval()

# make sure beam size always 1 for consistency
model.change_decoding_strategy(None)
decoding_cfg = model.cfg.decoding
decoding_cfg.beam.beam_size = 1
model.change_decoding_strategy(decoding_cfg)

# setup for buffered inference
model.cfg.preprocessor.dither = 0.0
model.cfg.preprocessor.pad_to = 0

feature_stride = model.cfg.preprocessor['window_stride']
model_stride_in_secs = feature_stride * 8 # 8 = model stride, which is 8 for FastConformer

frame_asr = FrameBatchMultiTaskAED(
	asr_model=model,
	frame_len=40.0,
	total_buffer=40.0,
	batch_size=16,
)

amp_dtype = torch.float16


def convert_audio(audio_filepath, tmpdir, utt_id):
	"""
	Convert all files to monochannel 16 kHz wav files.
	Do not convert and raise error if audio too long.
	Returns output filename and duration.
	"""
	data, sr = librosa.load(audio_filepath, sr=None, mono=True)

	duration = librosa.get_duration(y=data, sr=sr)

	if duration > MAX_AUDIO_SECS:
		raise gr.Error(
			f"This demo can transcribe up to {MAX_AUDIO_MINUTES} minutes of audio. "
			"If you wish, you may trim the audio using the Audio viewer in Step 1 "
			"(click on the scissors icon to start trimming audio)."
		)

	if sr != SAMPLE_RATE:
		data = librosa.resample(data, orig_sr=sr, target_sr=SAMPLE_RATE)

	out_filename = os.path.join(tmpdir, utt_id + '.wav')

	# save output audio
	sf.write(out_filename, data, SAMPLE_RATE)

	return out_filename, duration


def transcribe(audio_filepath, src_lang, tgt_lang, pnc):

	if audio_filepath is None:
		raise gr.Error("Please provide some input audio: either upload an audio file or use the microphone")
	
	utt_id = uuid.uuid4()
	with tempfile.TemporaryDirectory() as tmpdir:
		converted_audio_filepath, duration = convert_audio(audio_filepath, tmpdir, str(utt_id))

		# make manifest file and save
		manifest_data = {
			"audio_filepath": converted_audio_filepath,
			"source_lang": src_lang,
			"target_lang": tgt_lang,
			"taskname": taskname,
			"pnc": pnc,
			"answer": "predict",
			"duration": str(duration),
		}

		manifest_filepath = os.path.join(tmpdir, f'{utt_id}.json')

		with open(manifest_filepath, 'w') as fout:
			line = json.dumps(manifest_data)
			fout.write(line + '\n')

		# call transcribe, passing in manifest filepath
		if duration < 40:
			output_text = model.transcribe(manifest_filepath)[0]
		else: # do buffered inference
			with torch.cuda.amp.autocast(dtype=amp_dtype): # TODO: make it work if no cuda
				with torch.no_grad():
					hyps = get_buffered_pred_feat_multitaskAED(
						frame_asr,
						model.cfg.preprocessor,
						model_stride_in_secs,
						model.device,
						manifest=manifest_filepath,
						filepaths=None,
					)

					output_text = hyps[0].text

	return output_text



with gr.Blocks(
	title="NeMo Canary Model",
	css="""
		textarea { font-size: 18px;}
		#model_output_text_box span {
			font-size: 18px;
			font-weight: bold;
		}
	""",
	theme=gr.themes.Default(text_size=gr.themes.sizes.text_lg) # make text slightly bigger (default is text_md )
) as demo:

	gr.HTML("<h1 style='text-align: center'>NeMo Canary model: Transcribe & Translate audio</h1>")

	with gr.Row():
		with gr.Column():
			gr.HTML(
				"<p><b>Step 1:</b> Record with your microphone.</p>"

				
			)

			audio_file = gr.Audio(sources=["microphone"], type="filepath")

			
		with gr.Column():

			gr.HTML("<p><b>Step 3:</b> Run the model.</p>")

			go_button = gr.Button(
				value="Run model",
				variant="primary", # make "primary" so it stands out (default is "secondary")
			)

			model_output_text_box = gr.Textbox(
				label="Model Output",
				elem_id="model_output_text_box",
			)

	with gr.Row():

		gr.HTML(
			"<p style='text-align: center'>"
				"🐤 <a href='https://huggingface.co/nvidia/canary-1b' target='_blank'>Canary model</a> | "
				"🧑‍💻 <a href='https://github.com/NVIDIA/NeMo' target='_blank'>NeMo Repository</a>"
			"</p>"
		)

	go_button.click(
		fn=transcribe, 
		inputs = [audio_file],
		outputs = [model_output_text_box]
	)


demo.queue()
demo.launch()




# Function to capture audio using Canary ASR
def capture_audio():
    utt_id = uuid.uuid4()
	with tempfile.TemporaryDirectory() as tmpdir:
		converted_audio_filepath, duration = convert_audio(audio_filepath, tmpdir, str(utt_id))
        
    manifest_data = {
			"audio_filepath": converted_audio_filepath,
			"source_lang": "en",
			"target_lang": "en",
			"taskname": taskname,
			"pnc": pnc,
			"answer": "predict",
			"duration": 10,
		}

		manifest_filepath = os.path.join(tmpdir, f'{utt_id}.json')

    print("Listening for cue words...")
    while True:
        audio_input = asr_pipeline(None)[0]['input_values']
        transcript = asr_pipeline(audio_input)[0]['transcription']
        if "hey canary" in transcript.lower():
            print("Cue word detected!")
            break
    print("Listening...")
    return audio_input

# AI assistant function
def ai_assistant(audio_input):
    # Perform automatic speech recognition (ASR)
    transcript = asr_pipeline(audio_input)[0]['transcription']

    # Perform question answering (QA)
    qa_result = qa_pipeline(question=transcript, context="Insert your context here")

    # Convert the QA result to speech using text-to-speech (TTS)
    tts_output = tts_pipeline(qa_result['answer'])

    return tts_output[0]['audio']

if __name__ == "__main__":
    # Create a Gradio interface
    gr.Interface(ai_assistant,
                 inputs=gr.inputs.Audio(capture=capture_audio, label="Speak Here"),
                 outputs=gr.outputs.Audio(type="audio", label="Assistant's Response"),
                 title="AI Assistant",
                 description="An AI Assistant that answers questions based on your speech input.").launch()
'''