#!/usr/bin/env python3
#
# Copyright      2022-2023  Xiaomi Corp.        (authors: Fangjun Kuang)
#
# See LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# References:
# https://gradio.app/docs/#dropdown

import logging
import os
import tempfile
import time
import urllib.request
from datetime import datetime

import gradio as gr
import soundfile as sf

from model import decode, get_pretrained_model, whisper_models


def convert_to_wav(in_filename: str) -> str:
    """Convert the input audio file to a wave file"""
    out_filename = in_filename + ".wav"
    logging.info(f"Converting '{in_filename}' to '{out_filename}'")

    _ = os.system(
        f"ffmpeg -hide_banner -i '{in_filename}' -ar 16000 -ac 1 '{out_filename}'"
    )

    return out_filename


def build_html_output(s: str, style: str = "result_item_success"):
    return f"""
    <div class='result'>
        <div class='result_item {style}'>
          {s}
        </div>
    </div>
    """


def process_url(
    repo_id: str,
    url: str,
):
    logging.info(f"Processing URL: {url}")
    with tempfile.NamedTemporaryFile() as f:
        try:
            urllib.request.urlretrieve(url, f.name)

            return process(
                in_filename=f.name,
                repo_id=repo_id,
            )
        except Exception as e:
            logging.info(str(e))
            return "", build_html_output(str(e), "result_item_error")


def process_uploaded_file(
    repo_id: str,
    in_filename: str,
):
    if in_filename is None or in_filename == "":
        return "", build_html_output(
            "Please first upload a file and then click "
            'the button "submit for recognition"',
            "result_item_error",
        )

    logging.info(f"Processing uploaded file: {in_filename}")
    try:
        return process(
            in_filename=in_filename,
            repo_id=repo_id,
        )
    except Exception as e:
        logging.info(str(e))
        return "", build_html_output(str(e), "result_item_error")


def process_microphone(
    repo_id: str,
    in_filename: str,
):
    if in_filename is None or in_filename == "":
        return "", build_html_output(
            "Please first click 'Record from microphone', speak, "
            "click 'Stop recording', and then "
            "click the button 'submit for recognition'",
            "result_item_error",
        )

    logging.info(f"Processing microphone: {in_filename}")
    try:
        return process(
            in_filename=in_filename,
            repo_id=repo_id,
        )
    except Exception as e:
        logging.info(str(e))
        return "", build_html_output(str(e), "result_item_error")


def process(
    repo_id: str,
    in_filename: str,
):
    logging.info(f"repo_id: {repo_id}")
    logging.info(f"in_filename: {in_filename}")

    filename = convert_to_wav(in_filename)

    now = datetime.now()
    date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f")
    logging.info(f"Started at {date_time}")

    start = time.time()

    recognizer = get_pretrained_model(repo_id)

    text = decode(recognizer, filename)

    date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f")
    end = time.time()

    info = sf.info(filename)
    duration = info.duration

    elapsed = end - start
    rtf = elapsed / duration

    logging.info(f"Finished at {date_time} s. Elapsed: {elapsed: .3f} s")

    info = f"""
    Wave duration  : {duration: .3f} s <br/>
    Processing time: {elapsed: .3f} s <br/>
    RTF: {elapsed: .3f}/{duration: .3f} = {rtf:.3f} <br/>
    """
    if rtf > 1:
        info += (
            "<br/>We are loading the model for the first run. "
            "Please run again to measure the real RTF.<br/>"
        )

    logging.info(info)
    logging.info(f"\nrepo_id: {repo_id}\nhyp: {text}")

    return text, build_html_output(info)


title = "# Speech recognition: [Next-gen Kaldi](https://github.com/k2-fsa) + [Whisper](https://github.com/openai/whisper/)"
description = """
This space shows how to do automatic speech recognition with [Next-gen Kaldi](https://github.com/k2-fsa)
using [Whisper](https://github.com/openai/whisper/) models.

It is running on a machine with 2 vCPUs with 16 GB RAM within a docker container provided by Hugging Face.

See more information by visiting the following links:

- <https://github.com/k2-fsa/sherpa-onnx>

If you want to deploy it locally, please see
<https://k2-fsa.github.io/sherpa/>
"""

# css style is copied from
# https://huggingface.co/spaces/alphacep/asr/blob/main/app.py#L113
css = """
.result {display:flex;flex-direction:column}
.result_item {padding:15px;margin-bottom:8px;border-radius:15px;width:100%}
.result_item_success {background-color:mediumaquamarine;color:white;align-self:start}
.result_item_error {background-color:#ff7070;color:white;align-self:start}
"""


demo = gr.Blocks(css=css)


with demo:
    gr.Markdown(title)
    model_choices = list(whisper_models.keys())

    model_dropdown = gr.Dropdown(
        choices=model_choices,
        label="Select a model",
        value=model_choices[0],
    )

    with gr.Tabs():
        with gr.TabItem("Upload from disk"):
            uploaded_file = gr.Audio(
                sources=["upload"],  # Choose between "microphone", "upload"
                type="filepath",
                label="Upload from disk",
            )
            upload_button = gr.Button("Submit for recognition")
            uploaded_output = gr.Textbox(label="Recognized speech from uploaded file")
            uploaded_html_info = gr.HTML(label="Info")

        with gr.TabItem("Record from microphone"):
            microphone = gr.Audio(
                sources=["microphone"],  # Choose between "microphone", "upload"
                type="filepath",
                label="Record from microphone",
            )

            record_button = gr.Button("Submit for recognition")
            recorded_output = gr.Textbox(label="Recognized speech from recordings")
            recorded_html_info = gr.HTML(label="Info")

        with gr.TabItem("From URL"):
            url_textbox = gr.Textbox(
                max_lines=1,
                placeholder="URL to an audio file",
                label="URL",
                interactive=True,
            )

            url_button = gr.Button("Submit for recognition")
            url_output = gr.Textbox(label="Recognized speech from URL")
            url_html_info = gr.HTML(label="Info")

        upload_button.click(
            process_uploaded_file,
            inputs=[
                model_dropdown,
                uploaded_file,
            ],
            outputs=[uploaded_output, uploaded_html_info],
        )

        record_button.click(
            process_microphone,
            inputs=[
                model_dropdown,
                microphone,
            ],
            outputs=[recorded_output, recorded_html_info],
        )

        url_button.click(
            process_url,
            inputs=[
                model_dropdown,
                url_textbox,
            ],
            outputs=[url_output, url_html_info],
        )

    gr.Markdown(description)

if __name__ == "__main__":
    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"

    logging.basicConfig(format=formatter, level=logging.INFO)

    demo.launch()