intialized flask server and implemeted the voice emotion detection api

back-end/.gitignore

+__pycache__
+Emotion_Voice_Detection_Model.pkl
+output10.wav
\ No newline at end of file

back-end/.gitkeep

-initiating back-end root
\ No newline at end of file

back-end/app.py

+from flask import Flask, render_template
+from src.components.voice_emotion_detection.voice_emotion_detection import predictVoiceEmotion
+
+app = Flask(__name__)
+
+@app.route('/')
+def index():
+    return "welcome to SMART INFORTAITEMENT SYSTEM"
+
+
+@app.route('/voice-emotion-detection')
+def voice_emotion_detection():
+    return predictVoiceEmotion()
+
+
+    if __name == '__main__':
+        app.run(debug=True)
+

back-end/models/it18226324/.gitkeep

-initiating branch
-reset testing of git user credintials
\ No newline at end of file

back-end/models/it18229080/.gitkeep

-initiating branch
-testing git credential changes
\ No newline at end of file

back-end/models/it18230666/.gitkeep

-Branch initiated
-Test git credential changes
\ No newline at end of file

back-end/models/it18258240/.gitkeep

-initiating branch
-test git credentials changes
\ No newline at end of file

back-end/requirements.txt

+click==8.0.4
+colorama==0.4.4
+Flask==2.0.3
+itsdangerous==2.1.1
+Jinja2==3.0.3
+MarkupSafe==2.1.0
+Werkzeug==2.0.3

back-end/src/components/voice_emotion_detection/voice_emotion_detection.py

+def predictVoiceEmotion():
+
+    #Install all the Reqiuired Libraries and Packages 
+    import os
+    import glob
+    from tqdm import tqdm
+    import pandas as pd
+    import numpy as np
+    from sklearn.feature_extraction import DictVectorizer
+    import matplotlib.pyplot as plt
+    from scipy.io import wavfile
+    from python_speech_features import mfcc , logfbank
+    import librosa as lr
+    import os, glob, pickle
+    import librosa
+    from scipy import signal
+    import noisereduce as nr
+    from glob import glob
+    import librosa
+    # get_ipython().magic('matplotlib inline')
+    #All the Required Packages and Libraies are installed.
+    import soundfile
+    from tensorflow.keras.layers import Conv2D,MaxPool2D, Flatten, LSTM
+    from keras.layers import Dropout,Dense,TimeDistributed
+    from keras.models import Sequential
+    from tensorflow.keras.utils import to_categorical
+    from sklearn.utils.class_weight import compute_class_weight
+    from sklearn.model_selection import train_test_split
+    from sklearn.neural_network import MLPClassifier
+    from sklearn.metrics import accuracy_score
+    import speech_recognition as sr
+    from scipy.io import wavfile as wav
+    from scipy.fftpack import fft
+    import glob,pickle
+    import soundfile
+    import os
+    import glob
+    from tqdm import tqdm
+    import pandas as pd
+    import numpy as np
+    from sklearn.feature_extraction import DictVectorizer
+    import matplotlib.pyplot as plt
+    from scipy.io import wavfile
+    from python_speech_features import mfcc , logfbank
+    import librosa as lr
+    import os, glob, pickle
+    import librosa
+    from scipy import signal
+    import noisereduce as nr
+    from glob import glob
+    import librosa
+    # get_ipython().magic('matplotlib inline')
+    #All the Required Packages and Libraies are installed.
+    import soundfile
+    from tensorflow.keras.layers import Conv2D,MaxPool2D, Flatten, LSTM
+    from keras.layers import Dropout,Dense,TimeDistributed
+    from keras.models import Sequential
+    from tensorflow.keras.utils import to_categorical
+    from sklearn.utils.class_weight import compute_class_weight
+    from sklearn.model_selection import train_test_split
+    from sklearn.neural_network import MLPClassifier
+    from sklearn.metrics import accuracy_score
+    #SAVING THE MODEL
+    import pickle
+    import pyaudio
+    import wave
+    from glob import glob
+    import os
+    import glob
+    import os
+    import librosa as lr
+    import pandas as pd
+    import librosa.display
+    import glob 
+    import librosa
+
+    #Feature Extraction of Audio Files Function 
+    #Extract features (mfcc, chroma, mel) from a sound file
+    def extract_feature(file_name, mfcc, chroma, mel):
+        with soundfile.SoundFile(file_name) as sound_file:
+            X = sound_file.read(dtype="float32")
+            sample_rate=sound_file.samplerate
+            if chroma:
+                stft=np.abs(librosa.stft(X))
+            result=np.array([])
+            if mfcc:
+                mfccs=np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T, axis=0)
+            result=np.hstack((result, mfccs))
+            if chroma:
+                chroma=np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)
+            result=np.hstack((result, chroma))
+            if mel:
+                mel=np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0)
+            result=np.hstack((result, mel))
+        return result
+
+
+    #Emotions in the RAVDESS dataset to be classified Audio Files based on . 
+    emotions={
+    '01':'neutral',
+    '02':'calm',
+    '03':'happy',
+    '04':'sad',
+    '05':'angry',
+    '06':'fearful',
+    '07':'disgust',
+    '08':'surprised'
+    }
+    #These are the emotions User wants to observe more :
+    observed_emotions=['calm', 'happy', 'fearful', 'disgust']
+
+    # Save the Modle to file in the current working directory
+    #For any new testing data other than the data in dataset
+
+    Pkl_Filename = "src/models/Emotion_Voice_Detection_Model.pkl"  
+
+    # Load the Model back from file
+    
+    with open(Pkl_Filename, 'rb') as file:  
+        Emotion_Voice_Detection_Model = pickle.load(file)
+
+    Emotion_Voice_Detection_Model
+
+    #RECORDED USING MICROPHONE:
+
+    CHUNK = 1024 
+    FORMAT = pyaudio.paInt16 #paInt8
+    CHANNELS = 1 
+    RATE = 44100 #sample rate
+    RECORD_SECONDS = 5
+    WAVE_OUTPUT_FILENAME = "output10.wav"
+
+    p = pyaudio.PyAudio()
+
+    stream = p.open(format=FORMAT,
+                    channels=CHANNELS,
+                    rate=RATE,
+                    input=True,
+                    frames_per_buffer=CHUNK) #buffer
+
+    print("* recording")
+    frames = []
+
+    for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
+        data = stream.read(CHUNK)
+        frames.append(data) # 2 bytes(16 bits) per channel
+
+    print("* done recording")
+
+    stream.stop_stream()
+    stream.close()
+    p.terminate()
+
+    wf = wave.open(WAVE_OUTPUT_FILENAME, 'wb')
+    wf.setnchannels(CHANNELS)
+    wf.setsampwidth(p.get_sample_size(FORMAT))
+    wf.setframerate(RATE)
+    wf.writeframes(b''.join(frames))
+    wf.close()
+
+    #The file 'output10.wav' in the next cell is the file that was recorded live using the code :
+    data, sampling_rate = librosa.load('output10.wav')
+
+    ## Appying extract_feature function on random file and then loading model to predict the result 
+    file = 'output10.wav'
+    
+    ans =[]
+    new_feature  = extract_feature(file, mfcc=True, chroma=True, mel=True)
+    ans.append(new_feature)
+    ans = np.array(ans)
+    #data.shape
+    print(Emotion_Voice_Detection_Model.predict(ans))
+    n = Emotion_Voice_Detection_Model.predict(ans)
+
+    output = ""
+    for o in n:
+        output += o
+
+    return output
\ No newline at end of file

client/.idea/compiler.xml

 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
   <component name="CompilerConfiguration">
-    <bytecodeTargetLevel target="11" />
+    <bytecodeTargetLevel target="1.8" />
   </component>
 </project>
\ No newline at end of file

client/.idea/gradle.xml

@@ -7,6 +7,7 @@
         <option name="testRunner" value="GRADLE" />
         <option name="distributionType" value="DEFAULT_WRAPPED" />
         <option name="externalProjectPath" value="$PROJECT_DIR$" />
+        <option name="gradleJvm" value="11" />
         <option name="modules">
           <set>
             <option value="$PROJECT_DIR$" />

client/.idea/misc.xml

@@ -9,7 +9,7 @@
       </map>
     </option>
   </component>
-  <component name="ProjectRootManager" version="2" languageLevel="JDK_11" default="true" project-jdk-name="1.8" project-jdk-type="JavaSDK">
+  <component name="ProjectRootManager" version="2" languageLevel="JDK_1_8" default="true" project-jdk-name="1.8" project-jdk-type="JavaSDK">
     <output url="file://$PROJECT_DIR$/build/classes" />
   </component>
   <component name="ProjectType">