Update app.py

app.py

@@ -1,91 +0,0 @@
-# https://github.com/ayushkumarshah/Guitar-Chords-recognition
-# https://github.com/ayushkumarshah/Guitar-Chords-recognition/blob/master/app.py
-# https://raw.githubusercontent.com/ayushkumarshah/Guitar-Chords-recognition/master/app.py
-
-import time, os
-import logging
-import streamlit as st
-import numpy as np
-import librosa, librosa.display
-import matplotlib.pyplot as plt
-from PIL import Image
-from settings import IMAGE_DIR, DURATION, WAVE_OUTPUT_FILE
-from src.sound import sound
-from src.model import CNN
-from setup_logging import setup_logging
-
-setup_logging()
-logger = logging.getLogger('app')
-
-def init_model():
-    cnn = CNN((128, 87))
-    cnn.load_model()
-    return cnn
-
-def get_spectrogram(type='mel'):
-    logger.info("Extracting spectrogram")
-    y, sr = librosa.load(WAVE_OUTPUT_FILE, duration=DURATION)
-    ps = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128)
-    logger.info("Spectrogram Extracted")
-    format = '%+2.0f'
-    if type == 'DB':
-        ps = librosa.power_to_db(ps, ref=np.max)
-        format = ''.join[format, 'DB']
-        logger.info("Converted to DB scale")
-    return ps, format
-
-def display(spectrogram, format):
-    plt.figure(figsize=(10, 4))
-    librosa.display.specshow(spectrogram, y_axis='mel', x_axis='time')
-    plt.title('Mel-frequency spectrogram')
-    plt.colorbar(format=format)
-    plt.tight_layout()
-    st.pyplot(clear_figure=False)
-
-def main():
-    title = "Guitar Chord Recognition"
-    st.title(title)
-    image = Image.open(os.path.join(IMAGE_DIR, 'app_guitar.jpg'))
-    st.image(image, use_column_width=True)
-
-    if st.button('Record'):
-        with st.spinner(f'Recording for {DURATION} seconds ....'):
-            sound.record()
-        st.success("Recording completed")
-
-    if st.button('Play'):
-        # sound.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")
-
-    if st.button('Classify'):
-        cnn = init_model()
-        with st.spinner("Classifying the chord"):
-            chord = cnn.predict(WAVE_OUTPUT_FILE, False)
-        st.success("Classification completed")
-        st.write("### The recorded chord is **", chord + "**")
-        if chord == 'N/A':
-            st.write("Please record sound first")
-        st.write("\n")
-
-    # Add a placeholder
-    if st.button('Display Spectrogram'):
-        # type = st.radio("Scale of spectrogram:",
-        #                 ('mel', 'DB'))
-        if os.path.exists(WAVE_OUTPUT_FILE):
-            spectrogram, format = get_spectrogram(type='mel')
-            display(spectrogram, format)
-        else:
-            st.write("Please record sound first")
-
-if __name__ == '__main__':
-    main()
-    # for i in range(100):
-    #   # Update the progress bar with each iteration.
-    #   latest_iteration.text(f'Iteration {i+1}')
-    #   bar.progress(i + 1)
-    #   time.sleep(0.1)