def chat(passage):
st.write("Excited to know more about the place?!")
st.write("Here's HAL9000 to help you with your queries!")
st.write("**type _bye_ to end the chat")
while True:
inp = st.text_input("You: ", key='1')
if inp.lower() == "bye":
st.text_area("Bot:",
value="Thank You, Have a nice day!",
height=200,
max_chars=None,
key=None)
break
result = predictor.predict(passage=passage, question=inp)
response = result['best_span_str']
st.text_area("Bot:",
value=response,
height=200,
max_chars=None,
key=None)
mytext = response
myobj = gTTS(text=mytext, lang=language, slow=False)
date_string = datetime.datetime.now().strftime("%d%m%Y%H%M%S")
filename = "voice" + date_string + ".ogg"
myobj.save(filename)
name = "C:\\Users\\Arpitha Shibu\\Documents\\BDA SEM III\\1 MOM\\MOM AI Project\\Final\\" + filename
audio_file = open(name, 'rb')
audio_bytes = audio_file.read()
st.audio(audio_bytes, format='audio/ogg')
os.remove(name)
def play_file_uploaded(file_upload, file_type):
if 'audio' in file_type:
audio_bytes = file_upload.read()
st.audio(audio_bytes, format="audio/wav")
elif 'video' in file_type:
video_bytes = file_upload.read()
st.video(video_bytes)
def pdf_to_audio(pdf, slow):
with st.spinner("Converting PDF to audio... "):
audio = convert_pdf_to_audio(pdf, slow)
audio.save('audiobook.wav')
st.audio('audiobook.wav', format='audio/wav')
os.remove('audiobook.wav')
def show_media_widgets() -> None:
DATA = Path('data/external')
st.write("""
### Display images """)
st.write("#### Reference images")
with st.echo():
st.image(
"https://www.dogalize.com/wp-content/uploads/2018/03/ceiling-cat.jpg",
caption="Ceiling cat",
use_column_width=True)
st.write("#### Embed image")
with st.echo():
st.image(
"""data:image/jpeg;base64,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""",
caption="embedded image",
width=500)
# audio
st.write("------")
st.subheader("Embed audio")
with st.echo():
audio_file = open(DATA / 'applause7.mp3', 'rb')
st.audio(audio_file, format='audio/mp3')
st.write("_Note: seems to have trouble in Firefox_")
# video
st.write("------")
st.subheader("Embed video")
with st.echo():
st.video("https://www.youtube.com/watch?v=B2iAodr0fOo", start_time=2)
def about():
set_png_as_page_bg('oil2.png')
st.title("About present work")
st.subheader(
"Billions of dollars are spent in oil rig operations including the safety on deck, quick analysis, efficiency etc. While multiple systems and heavy machinery are"
" used for various tasks at hand, there always are avenues that can be explored to bring the efficiency and safety at optimum level."
)
st.subheader(
"Multiple sounds are generated at the rigs during the extraction process and classifying the sounds correctly can help the engineers in reinforcing their"
" initial estimates and quick decisioing.")
audio_file = open(
"/Users/prashantmudgal/Documents/Quantplex Labs/Sound_app/machine_6.wav",
'rb')
audio_bytes = audio_file.read()
st.audio(audio_bytes, format='audio/wav')
st.subheader(
"In the present POC, we are classifying the sounds obtained from oil rigs into 10 categories:"
)
Final_Sound = [
'Blowout', 'Gas Emission', 'Rock Bed', 'Heavy Gas', 'Heavy Metal',
'Oil Drill Rig Exterior', 'Operatre Pump', 'Dieseling', 'Fracturing',
'Hydraulic'
]
df = pd.DataFrame(Final_Sound, columns=['Sound Class'])
st.table(df)
def present_audio(audio_samples, label, fig, plotting_slot, row):
"Show audio player and plot"
# show audio player
st.header(label)
temp_file_name = "temp.wav"
sf.write(temp_file_name, audio_samples.T, FS)
st.audio(temp_file_name)
remove(temp_file_name)
# plot audio in subplot
if fig is None:
if label == "original":
n_rows = 3
titles = ["original", "input", "enhanced"]
height = 600
else:
n_rows = 2
titles = ["input", "enhanced"]
height = 400
fig = make_subplots(
rows=n_rows,
cols=1,
subplot_titles=titles,
shared_xaxes=True,
)
fig.update_layout(showlegend=False, height=height)
fig.update_yaxes(fixedrange=True, range=[-1.0, 1.0], title="level")
fig.update_xaxes(title="time [s]")
time = np.arange(len(audio_samples)) / FS
fig.append_trace(
go.Scatter(x=time, y=audio_samples, line=dict(width=1)),
row=row,
col=1,
)
plotting_slot.plotly_chart(fig, use_container_width=True)
return fig
def play(raw_text, idioma_key):
tts = gTTS(text=raw_text, lang=idioma_key)
tts.save("audio.mp3")
audio_file = open("audio.mp3", "rb")
audio_bytes = audio_file.read()
#st.write("To download -> see options on the right side")
st.audio(audio_bytes, format="audio/mp3")
def run(alarmH, alarmM):
while (True):
if (alarmH == datetime.datetime.now().hour
and alarmM == datetime.datetime.now().minute):
st.write("Time to wake up")
audio_file = open("song.mp3", "rb")
st.audio(audio_file, format='audio/mp3')
break
def main():
st.sidebar.subheader(
'Saiba tudo sobre o seu artista favorito no Spotify! \nConfira popularidade, número de seguidores e músicas mais tocadas.\n'
)
name_input = st.sidebar.text_input('Digite o nome do artista:')
st.sidebar.markdown('[GitHub](https://github.com/mariromildo)')
if name_input:
result = sp.search(q='artist:' + name_input, type='artist')
result_id = result['artists']['items'][0]['id']
query = 'spotify:artist:' + result_id
artist = sp.artist(query)
# Artist infos
name = artist['name']
genres = artist['genres'][0]
popularity = artist['popularity']
followers = artist['followers']['total']
image = artist['images'][0]['url']
st.header(name)
st.write('**Gênero musical:**', genres)
st.write('**Popularidade:**', popularity)
st.write('**Seguidores no Spotify:**', followers)
st.image(image, width=200)
st.write('__________________________________________________________')
# Top 3 track infos
top_tracks = sp.artist_top_tracks(query)['tracks']
st.header('Top tracks')
for track in range(3):
music_name = top_tracks[track]['name']
album = top_tracks[track]['album']['name']
album_image = top_tracks[track]['album']['images'][track]['url']
preview = top_tracks[track]['preview_url']
feat = top_tracks[track]['artists']
feat_name = []
for i in range(len(feat)):
feat_name.append(feat[i]['name'])
if len(feat_name) > 0:
feat_name.remove(name)
st.write('**Música:**', music_name)
if len(
feat_name
) > 0: # Returning featured artists only when the value isn't the searched artist name
st.write('**Participação:**',
', '.join(str(p) for p in feat_name))
st.write('**Album:**', album)
st.image(album_image, width=200) # track audio preview
st.audio(preview)
st.write(
'__________________________________________________________')
def alarm(alarmH,alarmM,ap):
if ap == 'pm':
alarmH=alarmM+12
while(True):
if(alarmH==datetime.datetime.now().hour and alarm==datetime.datetime.now().minute):
st.write("Time to wake up")
audio_file=open("song.mp3","rb")
st.audio(audio_file,format='audio/mp3')
break
def text2speech(input_text):
synthesizer = torch.hub.load('coqui-ai/TTS:dev', 'tts', source='github')
wav = synthesizer.tts(input_text)
audio_obj = IPython.display.Audio(wav, rate=synthesizer.ap.sample_rate)
with open(WAVE_OUTPUT_FILE, 'wb') as f:
f.write(audio_obj.data)
with open(WAVE_OUTPUT_FILE, 'rb') as audio_file:
st.audio(audio_file, format='audio/wav')
def main():
st.title('Hello World')
st.header('')
st.subheader('')
st.text('')
st.image('logo.png')
st.subheader('')
st.audio('name.wav')
st.video('name.mov')
def myplaysound(filename):
# plays audio file filename in streamlit
# have to click button though
audio_file = open(filename, "rb")
audio_bytes = audio_file.read()
st.audio(audio_bytes, format="audio/ogg")
# audio_file.close
# os.remove(filename)
st.write('Click play button to play')
def main():
st.header('This is a header')
st.subheader('This is a subheader')
st.text("It's a text")
st.image('image.png')
st.audio('record.wav')
st.video('video.mov')
def present_audio(
audio_samples, label, fig, plotting_slot, row, signal_presentation, pesq_score
):
"Show audio player and plot"
# show audio player
st.header(label)
st.write("pesq: " + str(round(pesq_score, 2)))
temp_file_name = "temp.wav"
sf.write(temp_file_name, audio_samples.T, FS)
st.audio(temp_file_name)
remove(temp_file_name)
# plot audio in subplot
if fig is None:
if label == "original":
n_rows = 3
titles = ["original", "input", "enhanced"]
height = 600
else:
n_rows = 2
titles = ["input", "enhanced"]
height = 400
fig = make_subplots(
rows=n_rows,
cols=1,
subplot_titles=titles,
shared_xaxes=True,
)
fig.update_layout(showlegend=False, height=height)
if signal_presentation == "Time signal":
fig.update_yaxes(fixedrange=True, range=[-1.0, 1.0], title="level")
else:
fig.update_yaxes(fixedrange=True, title="Frequency Hz")
fig.update_xaxes(title="time [s]")
time = np.arange(len(audio_samples)) / FS
if signal_presentation == "Time signal":
fig.append_trace(
go.Scatter(x=time, y=audio_samples, line=dict(width=1)),
row=row,
col=1,
)
else:
freqs, bins, Pxx = signal.spectrogram(
audio_samples,
fs=FS,
window="hann",
nfft=512,
)
fig.append_trace(
go.Heatmap(
x=bins, y=freqs, z=10 * np.log10(Pxx), colorscale="Jet", showscale=False
),
row=row,
col=1,
)
plotting_slot.plotly_chart(fig, use_container_width=True)
return fig
def main():
st.title('Hello World')
st.header('This is a header')
st.subheader('This is a subheader')
st.text('This is a text')
st.image('logo.png')
st.subheader('This is a audio')
st.audio('record.wav')
st.subheader('This is a video')
st.video('sentiment_motion.mov')
def np_audio(np_array, stem_name, samplerate=44100):
soundfile.write('temp.wav', np_array, samplerate, 'PCM_24')
st.audio('temp.wav', format='audio/wav')
with open("temp.wav", "rb") as f:
file_id = _calculate_file_id(f.read(), "audio/wav")
wav_url = f'{STATIC_MEDIA_ENDPOINT}/{file_id}.wav'
stem_urls.append({
'src': wav_url,
'name': stem_name,
'customClass': stem_name,
})
def test_st_audio_options(self):
"""Test st.audio with options."""
fake_audio_data = "\x11\x22\x33\x44\x55\x66".encode("utf-8")
st.audio(fake_audio_data, format="audio/mp3", start_time=10)
el = self.get_delta_from_queue().new_element
# Manually base64 encoded payload above via
# base64.b64encode(bytes('\x11\x22\x33\x44\x55\x66'.encode('utf-8')))
self.assertEqual(el.audio.data, "ESIzRFVm")
self.assertEqual(el.audio.format, "audio/mp3")
self.assertEqual(el.audio.start_time, 10)
def render_trial_sound(label, trials_per_class):
# read the files from the label directory
label_dir = './data/' + label
wav_files = [f for f in listdir(label_dir) if isfile(join(label_dir, f))]
# print('Wav files', wav_files)
# render samples sounds
random_wav_files = random.sample(wav_files, trials_per_class)
for wav_file in random_wav_files:
audio_file = open(label_dir + '/' + wav_file, 'rb')
audio_bytes = audio_file.read()
st.audio(audio_bytes)
def test_st_audio_options(self):
"""Test st.audio with options."""
from streamlit.media_file_manager import _calculate_file_id
fake_audio_data = "\x11\x22\x33\x44\x55\x66".encode("utf-8")
st.audio(fake_audio_data, format="audio/mp3", start_time=10)
el = self.get_delta_from_queue().new_element
self.assertEqual(el.audio.start_time, 10)
self.assertTrue(el.audio.url.startswith(STATIC_MEDIA_ENDPOINT))
self.assertTrue(_calculate_file_id(fake_audio_data, "audio/mp3"), el.audio.url)
def audio_section(idx, file_name):
# print('file name', file_name)
audio_file = open(file_name, 'rb')
audio_bytes = audio_file.read()
st.audio(audio_bytes)
options = copy.deepcopy(random_labels)
options.insert(0, DEFAULT_OPTION)
options.append(UNKNOWN_OPTION)
radio_answer = st.radio("Audio label", options, key=file_name)
st.write('You selected label: **{}**'.format(radio_answer))
global answers
answers[idx] = [file_name, radio_answer]
def main():
st.title('Hello World')
st.header('This is header')
st.subheader('This is subheader')
st.text('This is text')
st.subheader('Imagens')
st.image('0.jpeg')
st.image('logo.png')
st.subheader('Audio')
st.audio('record.mov')
st.subheader('Video')
st.video('formaturaNicolas.mp4')
def main():
title = "Music Genre Classifier"
st.title(title)
image = Image.open('./presentation/turntable-2154823_1920.jpg')
st.image(image, use_column_width=True)
if st.button('Record'):
with st.spinner('Recording...'):
signal = sd.rec(int(6 * RECORD_SR),
samplerate=RECORD_SR,
channels=1,
blocking=True,
dtype='float64')
#sd.wait()
signal = signal.reshape(signal.shape[0])
#st.write(signal.shape)
#st.write(signal)
new_num_samples = round(
len(signal) * float(DESIRED_SR) / RECORD_SR)
signal = sps.resample(signal, new_num_samples)
# st.write(signal.shape)
# st.write(type(signal))
# st.write(signal)
st.experimental_set_query_params(signal=signal)
st.success("Recording completed")
app_state = st.experimental_get_query_params()
if st.button('Play'):
try:
signal = np.array(app_state["signal"], dtype='float')
# st.write(type(signal))
# st.write(signal.shape)
temp_file = io.BytesIO()
write(temp_file, 22050, signal)
st.audio(temp_file, format='audio/wav')
except:
st.write("Please record sound first")
if st.button('Classify'):
with st.spinner("Thinking..."):
signal = np.array(app_state["signal"], dtype='float')
# st.write(type(signal))
# st.write(signal.shape)
# st.write(signal)
pred = model.predict(get_harm_perc(signal))
st.success("Classification completed")
labels[np.argmax(pred)]
def main():
img = Image.open('data/image_03.jpg')
st.image(img, use_column_width=True) # 이미지를 페이지 화면에 맞게 폭을 늘려서 출력함
# 인터넷에 있는 이미지도 가능
# st.image('') 여기안에 url 주소 가져오면 됨
video_file = open('data/secret_of_success.mp4', 'rb').read() # 비디오 파일 불러오기
st.video(video_file) # 비디오파일 불러온것을 실행해라
audio_file = open('data/song.mp3', 'rb').read() # 오디오 파일 불러오기
st.audio(audio_file) # 오디오 실행
def test_st_audio(self):
"""Test st.audio."""
# TODO(armando): generate real audio data
# For now it doesnt matter cause browser is the one that uses it.
fake_audio_data = "\x11\x22\x33\x44\x55\x66".encode("utf-8")
st.audio(fake_audio_data)
el = self.get_delta_from_queue().new_element
# Manually base64 encoded payload above via
# base64.b64encode(bytes('\x11\x22\x33\x44\x55\x66'.encode('utf-8')))
self.assertEqual(el.audio.data, "ESIzRFVm")
self.assertEqual(el.audio.format, "audio/wav")
def cs_display_media():
st.markdown(
"<h5 style='text-align: center ; color: black;'>Rendered -- Display Media Section</h5>",
unsafe_allow_html=True)
st.markdown('***')
st.markdown('**Image**')
st.markdown("- **st.image**('_`path`_')")
st.image('./brain.png', width=300)
st.markdown('***')
st.markdown('**Audio**')
st.markdown("- **st.audio**(_`data`_)")
audio_code = '''
audio_file = open('./Media/Cornfield_Chase.wav', 'rb')
audio_bytes = audio_file.read()
st.audio(audio_bytes, format='audio/wav')
'''
st.code(audio_code)
audio_file = open('./Media/Cornfield_Chase.wav', 'rb')
audio_bytes = audio_file.read()
st.audio(audio_bytes, format='audio/wav')
st.markdown(
"<h6 style='text-align: center ;'>Source ~ Interstellar ✨( Cornfield Chase ) </h6>",
unsafe_allow_html=True)
st.markdown('***')
st.markdown('**Video**')
st.markdown("- **st.video**(_`data`_)")
video_code = '''
video_file = open('./Media/Star-6962.mp4', 'rb')
video_bytes = video_file.read()
st.video(video_bytes)
'''
st.code(video_code)
video_file = open('./Media/Star-6962.mp4', 'rb')
video_bytes = video_file.read()
st.video(video_bytes)
st.markdown(
"<h6 style='text-align: center ;'>Creator - fxxu, Source - Pixbay </h6>",
unsafe_allow_html=True)
st.markdown('***')
info = st.beta_expander('Direct References')
info.markdown(
'''[<img src='data:image/png;base64,{}' class='img-fluid' width=32 height=32 style='float:left; vertical-align:middle'>](https://docs.streamlit.io/en/stable/api.html#display-media) <small style='color:black; font-size:16px;'>Link to the Official Documentation of this Section </small>'''
.format(img_to_bytes("logo.png")),
unsafe_allow_html=True)
st.markdown('***')
def main():
col1, mid, col2 = st.beta_columns([10, 1, 10])
with col1:
image = Image.open(os.path.join(IMAGE_DIR, 'speak.jpg'))
st.image(image, width=200)
with col2:
image = Image.open(os.path.join(IMAGE_DIR, 'unicorn.jpg'))
st.image(image, width=200)
title = "Talk to Transformer"
st.title(title)
session_state = SessionState.get(name="", button_sent=False)
record_button_clicked = st.button('Record')
if record_button_clicked or session_state.button_sent: # <-- first time is button interaction, next time use state:
st.empty()
session_state.button_sent = True
if record_button_clicked:
# you can get some voice samples from here: https://huggingface.co/facebook/wav2vec2-base-960h
with st.spinner(f'Recording for {DURATION} seconds ....'):
sound.record()
st.success("Recording completed")
if st.button('Play'):
try:
audio_file = open(WAVE_OUTPUT_FILE, 'rb')
audio_bytes = audio_file.read()
st.audio(audio_bytes, format='audio/wav')
except:
st.write("Please record sound first")
st.subheader("Here is what you spoke:\n")
if record_button_clicked:
session_state.input_text = speech2text(WAVE_OUTPUT_FILE)
session_state.input_text = session_state.input_text.capitalize()
for question_str in [
'Which ', 'Why ', 'Who ', 'Whose ', 'Whom ', 'What ',
'How '
]:
if session_state.input_text.startswith(question_str):
session_state.input_text += '?'
break
st.write(session_state.input_text)
st.subheader("Generating text using Transformer(DistilGPT2) model: \n")
if record_button_clicked:
session_state.gen_txt = generate_text(session_state.input_text)
st.write(session_state.gen_txt)
with st.spinner('Converting text to speech...'):
text2speech(session_state.gen_txt)
def render_file(wavfile, transcript_path, PathToFixed):
TranscriptFile = os.path.join(
transcript_path,
wavfile.split(DELIM)[-1].replace(".wav", ".txt"))
with open(TranscriptFile, 'r', encoding='utf-8') as f:
data = f.read()
key = wavfile.split(DELIM)[-1]
st.subheader("Name of File = " + key)
st.audio(open(wavfile, 'rb').read(), format='audio/wav')
content = st_ace(value=data,
theme=st.sidebar.selectbox("Theme.",
options=THEMES,
key=key),
font_size=st.sidebar.slider("Font size.",
5,
24,
24,
key=key),
tab_size=st.sidebar.slider("Tab size.", 1, 8, 4, key=key),
show_gutter=st.sidebar.checkbox("Show gutter.",
value=True,
key=key),
show_print_margin=st.sidebar.checkbox(
"Show print margin.", value=True, key=key),
wrap=st.sidebar.checkbox("Wrap enabled.",
value=True,
key=key),
key=key)
st.title(content)
if st.sidebar.button("Reject"):
print("Reject", key)
SessionState.get().current_index += 1
SessionState.sync()
SavePickleState(SessionState.get().current_index,
key.replace(".wav", ".txt"))
return
if st.sidebar.button("Go to next file"):
FixedTranscript = os.path.join(PathToFixed,
key.replace(".wav", ".txt"))
print(FixedTranscript)
open(FixedTranscript, "w", encoding="utf-8").write(content)
SessionState.get().current_index += 1
SessionState.sync()
SavePickleState(SessionState.get().current_index)
def display_media_audio(path: Path, start_second: int = 0):
format_ = path.name.split(".")[-1]
if format_ == "mp3":
format_ = "audio/mp3"
elif format_ == "wav":
format_ = "audio/wav"
elif format_ == "flac":
format_ = "audio/x-flac"
else:
st.warning("Selected type is not readable format")
if format_ in {"audio/wav", "audio/mp3", "audio/x-flac"}:
st.audio(read_audio_bytes(path),
start_time=start_second,
format=format_)
def display_media_audio_from_ndarray(y: np.ndarray, sr: int):
max_num = 32767.0 / y.max()
y_hex = (y * max_num).astype(np.int16)
binary_wave = struct.pack("h" * len(y_hex), *(y_hex.tolist()))
with tempfile.TemporaryFile() as fp:
w = wave.Wave_write(fp) # type: ignore
params = (1, 2, sr, len(binary_wave), "NONE", "not compressed")
w.setparams(params) # type: ignore
w.writeframes(binary_wave)
w.close()
fp.seek(0)
bytesio = io.BytesIO(fp.read())
st.audio(bytesio)
