import gradio as gr
import numpy as np
import torch
import transformers
from pathlib import Path
from transformers import pipeline
from transformers.utils import logging

# Log

#logging.set_verbosity_debug()
logger = logging.get_logger("transformers")

# Pipelines

## Automatic Speech Recognition
## https://huggingface.co/docs/transformers/task_summary#automatic-speech-recognition
## Require ffmpeg to be installed
asr_device = "cuda:0" if torch.cuda.is_available() else "cpu"
asr_torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
asr_model = "openai/whisper-tiny"
asr = pipeline(
    "automatic-speech-recognition",
    model=asr_model,
    torch_dtype=asr_torch_dtype,
    device=asr_device
)

## Token Classification / Name Entity Recognition
## https://huggingface.co/docs/transformers/task_summary#token-classification
tc_device = 0 if torch.cuda.is_available() else "cpu"
tc_model = "dslim/distilbert-NER"
tc = pipeline(
    "token-classification", # ner
    model=tc_model,
    device=tc_device
)

# ---

# Transformers

# https://www.gradio.app/main/docs/gradio/audio#behavior
# As output component: expects audio data in any of these formats:
# - a str or pathlib.Path filepath
# - or URL to an audio file,
# - or a bytes object (recommended for streaming),
# - or a tuple of (sample rate in Hz, audio data as numpy array)
def transcribe(audio: str | Path | bytes | tuple[int, np.ndarray] | None):
    logger.debug(">Transcribe")

    if audio is None:
        return "..."
    # TODO Manage str/Path

    text = ""

    # https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline.__call__
    # Whisper input format for tuple differ from output provided by gradio audio component
    if asr_model.startswith("openai/whisper") and type(audio) is tuple:
        sampling_rate, raw = audio

        # Convert to mono if stereo
        if raw.ndim > 1:
            raw = raw.mean(axis=1)

        # Convert according to asr_torch_dtype
        raw = raw.astype(np.float16 if type(asr_torch_dtype) is torch.float16 else np.float32)
        raw /= np.max(np.abs(raw))

        inputs = {"sampling_rate": sampling_rate, "raw": raw}

        logger.debug(inputs)

        transcript = asr(inputs)
        text = transcript['text']

    logger.debug(text)

    return text

def tokenize(text: str):
    logger.debug(">Tokenize")

    entities = tc(text)

    logger.debug(entities)

    # TODO Add Text Classification for sentiment analysis
    return {"text": text, "entities": entities}

def classify(text: str):
    logger.debug(">Classify")

    return None

def transcribe_tokenize(*arg):
    return tokenize(transcribe(arg))

# ---

# Gradio

## Interfaces

# https://www.gradio.app/main/docs/gradio/audio
input_audio = gr.Audio(
    sources=["upload", "microphone"],
    show_share_button=False
)

## App

asrner_app = gr.Interface(
    transcribe_tokenize,
    inputs=[
        input_audio
    ],
    outputs=[
        gr.HighlightedText()
    ],
    title="ASR>NER",
    description=(
        "Transcribe, Tokenize, Classify"
    ),
    flagging_mode="never"
)

ner_app = gr.Interface(
    tokenize,
    inputs=[
        gr.Textbox()
    ],
    outputs=[
        gr.HighlightedText()
    ],
    title="NER",
    description=(
        "Tokenize, Classify"
    ),
    flagging_mode="never"
)

gradio_app = gr.TabbedInterface(
    interface_list=[
        asrner_app,
        ner_app
    ],
    tab_names=[
        asrner_app.title,
        ner_app.title
    ],
    title="ASRNERSBX"
)

## Start!
gradio_app.launch()