Added app.py

stress-detection/app.py

+from flask import Flask,request
+from flask import render_template
+from joblib import load
+import sys
+import subprocess
+import os
+import random
+import sys
+
+import glob 
+import keras
+import IPython.display as ipd
+import librosa
+import librosa.display
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import plotly.graph_objs as go
+import plotly.offline as py
+import plotly.tools as tls
+import seaborn as sns
+import scipy.io.wavfile
+import tensorflow
+from werkzeug.datastructures import RequestCacheControl
+py.init_notebook_mode(connected=True)
+
+from tensorflow.keras import regularizers
+from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping
+from tensorflow.keras.callbacks import  History, ReduceLROnPlateau, CSVLogger
+from tensorflow.keras.models import Model, Sequential
+from tensorflow.keras.layers import Dense, Embedding, LSTM
+from tensorflow.keras.layers import Input, Flatten, Dropout, Activation, BatchNormalization
+from tensorflow.keras.layers import Conv1D, MaxPooling1D, AveragePooling1D
+from tensorflow.keras.preprocessing import sequence
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.preprocessing.text import Tokenizer
+from keras.utils import np_utils
+from tensorflow.keras.utils import to_categorical
+
+from sklearn.metrics import confusion_matrix
+from sklearn.preprocessing import LabelEncoder
+
+from scipy.fftpack import fft
+from scipy import signal
+from scipy.io import wavfile
+from tqdm import tqdm
+import tensorflow as tf
+from tensorflow.keras.initializers import glorot_uniform
+import joblib
+
+stress_scaler=joblib.load('scaler.sav')
+
+input_duration=3
+
+app = Flask(__name__)
+
+@app.route('/')
+def index():
+    return render_template('stress.html')
+
+
+@app.route("/upload", methods=["POST", "GET"])
+def default():
+    f = request.files['file']
+    filePath = "uploads/audio.wav"
+    f.save(filePath)
+    return render_template('stress.html',upload_status="Uploaded")
+
+
+@app.route('/detection',methods=["GET","POST"])
+def detection():
+
+    labels=["Not Stress","Stress"]
+
+    loaded_model = tf.keras.models.load_model("models/Data_noiseNshift.h5",custom_objects={'GlorotUniform': glorot_uniform()})
+    print("Loaded model from disk")
+
+    X, sample_rate = librosa.load("uploads/audio.wav", res_type='kaiser_fast',duration=input_duration,sr=22050*2,offset=0.5)
+    
+    sample_rate = np.array(sample_rate)
+    mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=13), axis=0)
+    feature = mfccs
+    data=np.array([feature])
+
+    data=stress_scaler.transform(data)
+    data=data.reshape(data.shape[0],data.shape[1],1)
+
+    result=loaded_model.predict(data)
+    print(result)
+    category=np.argmax(result,axis=1)[0]
+    acc=round(np.max(result,axis=1)[0]*100)
+    label=labels[category]
+
+    print(' label:',label,' accuracy:',acc)
+
+    return render_template('stress_table.html',label=label,acc=acc)
+
+@app.route('/emotion_detection',methods=["GET","POST"])
+def emotion_detection():
+    labels=["Neutral","Calm","Happy","Sad", "Angry",  "Fearful"]
+
+    loaded_model = tf.keras.models.load_model("models/Data_noiseNshift_e.h5",custom_objects={'GlorotUniform': glorot_uniform()})
+    print("Loaded model from disk")
+
+    X, sample_rate = librosa.load("uploads/audio.wav", res_type='kaiser_fast',duration=input_duration,sr=22050*2,offset=0.5)
+    
+    sample_rate = np.array(sample_rate)
+    mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=13), axis=0)
+    feature = mfccs
+    data=np.array([feature])
+
+    data=stress_scaler.transform(data)
+    data=data.reshape(data.shape[0],data.shape[1],1)
+
+    result=loaded_model.predict(data)
+    print(result)
+    category=np.argmax(result,axis=1)[0]
+    acc=round(np.max(result,axis=1)[0]*100)
+    label=labels[category]
+
+    print(' label:',label,' accuracy:',acc)
+
+    return render_template('stress_table.html',label=label,acc=acc)
+# run Server
+if __name__ == "__main__":
+	app.run(
+		host="0.0.0.0",
+		port=5000,
+		debug=True,
+	)
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