#!/usr/bin/python
# Copyright (C) 2016 Google Inc.
#
# 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.
"""Sample that streams audio to the Google Cloud Speech API via GRPC."""

from __future__ import division

import contextlib
import functools
import re
import signal
import sys
from google.cloud import speech
import pyaudio
from six.moves import queue
from Queue import Queue
from villa_audio import SharedMicrophoneStream

#Speech context TODO
#import xml_reader
#import os

from grpc._channel import _Rendezvous
from grpc import StatusCode

# Audio recording parameters
RATE = 16000
CHUNK = int(RATE / 10)  # 100ms

# The Speech API has a streaming limit of 60 seconds of audio*, so keep the
# connection alive for that long, plus some more to give the API time to figure
# out the transcription.
# * https://g.co/cloud/speech/limits#content
DEADLINE_SECS = 60 * 3 + 5
SPEECH_SCOPE = 'https://www.googleapis.com/auth/cloud-platform'


class AudioWrapper: 
    def __init__(self):
        # Create a thread-safe buffer of audio data
        buff = queue.Queue()

        self.audio_interface = pyaudio.PyAudio()
        self.audio_stream = self.audio_interface.open(
            format=pyaudio.paInt16,
            # The API currently only supports 1-channel (mono) audio
            # https://goo.gl/z757pE
            channels=1, rate=RATE,
            input=True, frames_per_buffer=CHUNK,
            # Run the audio stream asynchronously to fill the buffer object.
            # This is necessary so that the input device's buffer doesn't overflow
            # while the calling thread makes network requests, etc.
            stream_callback=None,
        )

        self.closed = False


    def read(self, chunk_size):
        return self.audio_stream.read(chunk_size)

def replace_numbers_with_text(phrase): 
    phrase = phrase.replace('0', 'zero ')
    phrase = phrase.replace('1', 'one ')
    phrase = phrase.replace('2', 'two ')
    phrase = phrase.replace('3', 'three ')
    phrase = phrase.replace('4', 'four ')
    phrase = phrase.replace('5', 'five ')
    phrase = phrase.replace('6', 'six ')
    phrase = phrase.replace('7', 'seven ')
    phrase = phrase.replace('8', 'eight ')
    phrase = phrase.replace('9', 'nine ')

    return phrase

def make_channel(host, port):
    """Creates a secure channel with auth credentials from the environment."""
    # Grab application default credentials from the environment
    credentials, _ = google.auth.default(scopes=[SPEECH_SCOPE])

    # Create a secure channel using the credentials.
    http_request = google.auth.transport.requests.Request()
    target = '{}:{}'.format(host, port)

    return google.auth.transport.grpc.secure_authorized_channel(
        credentials, http_request, target)


def _audio_data_generator(buff):
    """A generator that yields all available data in the given buffer.

    Args:
        buff - a Queue object, where each element is a chunk of data.
    Yields:
        A chunk of data that is the aggregate of all chunks of data in `buff`.
        The function will block until at least one data chunk is available.
    """
    stop = False
    while not stop:
        # Use a blocking get() to ensure there's at least one chunk of data.
        data = [buff.get()]

        # Now consume whatever other data's still buffered.
        while True:
            try:
                data.append(buff.get(block=False))
            except queue.Empty:
                break

        # `None` in the buffer signals that the audio stream is closed. Yield
        # the final bit of the buffer and exit the loop.
        if None in data:
            stop = True
            data.remove(None)

        yield b''.join(data)


def _fill_buffer(buff, in_data, frame_count, time_info, status_flags):
    """Continuously collect data from the audio stream, into the buffer."""
    buff.put(in_data)
    return None, pyaudio.paContinue


# [START audio_stream]
@contextlib.contextmanager
def record_audio(rate, chunk):
    """Opens a recording stream in a context manager."""
    # Create a thread-safe buffer of audio data
    buff = queue.Queue()

    audio_interface = pyaudio.PyAudio()
    audio_stream = audio_interface.open(
        format=pyaudio.paInt16,
        # The API currently only supports 1-channel (mono) audio
        # https://goo.gl/z757pE
        channels=1, rate=rate,
        input=True, frames_per_buffer=chunk,
        # Run the audio stream asynchronously to fill the buffer object.
        # This is necessary so that the input device's buffer doesn't overflow
        # while the calling thread makes network requests, etc.
        stream_callback=functools.partial(_fill_buffer, buff),
    )

    yield _audio_data_generator(buff)

    audio_stream.stop_stream()
    audio_stream.close()
    # Signal the _audio_data_generator to finish
    buff.put(None)
    audio_interface.terminate()
# [END audio_stream]


def request_stream(data_stream, rate, interim_results=True):
    """Yields `StreamingRecognizeRequest`s constructed from a recording audio
    stream.

    Args:
        data_stream: A generator that yields raw audio data to send.
        rate: The sampling rate in hertz.
        interim_results: Whether to return intermediate results, before the
            transcription is finalized.
    """
    # The initial request must contain metadata about the stream, so the
    # server knows how to interpret it.
    recognition_config = cloud_speech_pb2.RecognitionConfig(
        # There are a bunch of config options you can specify. See
        # https://goo.gl/KPZn97 for the full list.
        encoding='LINEAR16',  # raw 16-bit signed LE samples
        sample_rate=rate,  # the rate in hertz
        # See http://g.co/cloud/speech/docs/languages
        # for a list of supported languages.
        language_code='en-US',  # a BCP-47 language tag

        speech_context=SpeechContext(phrases=['bevo', 'longhorn', 'cow', 'plush toy', 'toy', 
                                              'crayon', 'crayola', 'pink crayon', 'pink crayola', 'cylinder', 'pink cylinder',
                                              'grasp', 'grab', 'hold', 'lift', 'raise', 'elevate', 'pick up', 'place', 
                                              'put down', 'drop', 'hand', 'over' 'me' 'them',
                                              'could', 'you', 'open', 'hand', 'claws', 'fingers', 'gripper', 'will', 'can', 'please'
                                              ]),
    )
    streaming_config = cloud_speech_pb2.StreamingRecognitionConfig(
        interim_results=interim_results,
        config=recognition_config,
    )

    yield cloud_speech_pb2.StreamingRecognizeRequest(
        streaming_config=streaming_config)

    for data in data_stream:
        # Subsequent requests can all just have the content
        yield cloud_speech_pb2.StreamingRecognizeRequest(audio_content=data)


def listen_print_loop(recognize_stream):
    """Iterates through server responses and prints them.

    The recognize_stream passed is a generator that will block until a response
    is provided by the server. When the transcription response comes, print it.

    In this case, responses are provided for interim results as well. If the
    response is an interim one, print a line feed at the end of it, to allow
    the next result to overwrite it, until the response is a final one. For the
    final one, print a newline to preserve the finalized transcription.
    """

    #TODO will hold alternative transcripts (i.e. n-best list). 
    responses = []

    num_chars_printed = 0
    for resp in recognize_stream:
        if resp.error.code != code_pb2.OK:
            raise RuntimeError('Server error: ' + resp.error.message)

        if not resp.results:
            continue

        # Display the top transcription
        result = resp.results[0]

        transcript = result.alternatives[0].transcript

        # Display interim results, but with a carriage return at the end of the
        # line, so subsequent lines will overwrite them.
        #
        # If the previous result was longer than this one, we need to print
        # some extra spaces to overwrite the previous result
        overwrite_chars = ' ' * max(0, num_chars_printed - len(transcript))

        if not result.is_final:
            pass
            #Commenting out to make output less noisy. 
            #sys.stdout.write(transcript + overwrite_chars + '\r')
            #sys.stdout.flush()


        else:
            #Get final transcript for this utterance and store it. 
            response = transcript + overwrite_chars
            responses.append(response)

            #Return response instead of looping so we may feed it to parser. 
            return response

            #Moved this functionality to the main loop, delete if wanted. 
            """
            # Exit recognition if any of the transcribed phrases could be
            # one of our keywords.
            if re.search(r'\b(exit|quit)\b', transcript, re.I):
                print('Exiting..')
                break

            num_chars_printed = 0
            """
