Added flask skeleton code

GUI/application.py

@@ -0,0 +1,90 @@
+import os
+import sys
+print(sys.executable)
+
+from flask import Flask, request, redirect, url_for, send_from_directory, render_template
+from tensorflow.keras.layers import Dense, Embedding
+from tensorflow.keras.models import Sequential, model_from_json
+import tensorflow as tf
+import librosa
+import numpy as np
+import math
+from tcn import TCN, tcn_full_summary
+
+from werkzeug.utils import secure_filename
+import numpy as np
+
+ALLOWED_EXTENSIONS = set(['wav'])
+UPLOAD_FOLDER = 'uploads'
+
+def load_model():
+    loaded_json = open('./native/model.json', 'r').read()
+    model = model_from_json(loaded_json, custom_objects={'TCN': TCN})
+    model.load_weights('./native/weights.h5')
+    return model
+
+model = load_model()
+
+def allowed_file(filename):
+    return '.' in filename and \
+           filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS
+
+def predict(file):
+    waveform, sample_rate = librosa.load(file, sr=22050, duration=4.0)
+
+    # make sure that the audio is sample_rate * 4 seconds long
+    if len(waveform) != (sample_rate * 4):
+        repeat_num = math.ceil((sample_rate*4) / len(waveform))
+        waveform = np.repeat(waveform, repeat_num)
+        waveform = waveform[:(sample_rate*4)]
+
+    matrix = librosa.stft(waveform, center=False, n_fft=1024, hop_length=256)
+
+    matrix = np.stack([np.real(matrix), np.imag(matrix)], -1)
+    matrix = np.swapaxes(matrix, 0, 1)
+    matrix = np.expand_dims(matrix, axis=0)
+    output = model.predict(matrix)
+    output = np.squeeze(output)
+
+    output = {
+        "air_conditioner": output[0],
+        "car_horn": output[1],
+        "children_playing": output[2],
+        "dog_bark": output[3],
+        "drilling": output[4],
+        "engine_idling": output[5],
+        "gun_shot": output[6],
+        "jackhammer": output[7],
+        "siren": output[8],
+        "street_music": output[9]
+    }
+    return output
+
+app = Flask(__name__)
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+
+@app.route("/")
+def template_test():
+    return render_template('home.html', label='', imagesource='file://null')
+
+
+@app.route('/', methods=['GET', 'POST'])
+def upload_file():
+    if request.method == 'POST':
+        file = request.files['input-b1']
+
+        if file and allowed_file(file.filename):
+            filename = secure_filename(file.filename)
+            file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+            file.save(file_path)
+            output = predict(file_path)
+            return render_template("home.html", label=output, audiosource=file_path)
+
+
+@app.route('/uploads/<filename>')
+def uploaded_file(filename):
+    return send_from_directory(app.config['UPLOAD_FOLDER'],
+                               filename)
+
+if __name__ == "__main__":
+    app.run(debug=False, threaded=False)

GUI/requirements.txt

@@ -0,0 +1,42 @@
+absl-py==0.8.1
+astor==0.8.0
+cachetools==3.1.1
+certifi==2019.9.11
+chardet==3.0.4
+Click==7.0
+Flask==1.1.1
+gast==0.2.2
+google-auth==1.6.3
+google-auth-oauthlib==0.4.1
+google-pasta==0.1.7
+grpcio==1.24.3
+h5py==2.10.0
+idna==2.8
+itsdangerous==1.1.0
+Jinja2==2.10.3
+json5==0.8.5 
+Keras==2.3.1
+Keras-Applications==1.0.8
+Keras-Preprocessing==1.1.0
+Markdown==3.1.1
+MarkupSafe==1.1.1
+numpy==1.17.3
+oauthlib==3.1.0
+opt-einsum==3.1.0
+Pillow==6.2.1
+protobuf==3.10.0
+pyasn1==0.4.7
+pyasn1-modules==0.2.7
+PyYAML==5.1.2
+requests==2.22.0
+requests-oauthlib==1.2.0
+rsa==4.0
+scipy==1.3.1
+six==1.12.0
+tensorboard==2.0.1
+tensorflow==2.0.0
+tensorflow-estimator==2.0.1
+termcolor==1.1.0
+urllib3==1.25.6
+Werkzeug==0.16.0
+wrapt==1.11.2

GUI/static/hot-colormap.json

@@ -0,0 +1 @@
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