Merge branch 'IT20201364_Thathsarani_R.P.H.S.R'

IT20201364/model_test.py

+import librosa
+import librosa.display
+import numpy as np
+import matplotlib.pyplot as plt
+import tensorflow as tf
+import os
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+import sys
+import glob
+import warnings
+import keras
+from keras.preprocessing.sequence import pad_sequences
+
+from keras.models import Sequential
+from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from keras.utils import to_categorical
+from tqdm import tqdm
+
+warnings.filterwarnings("ignore")
+
+dataList = os.listdir('/RawData')
+classLabels = ('Angry', 'Fear', 'Disgust', 'Happy', 'Sad', 'Surprised', 'Neutral')
+data = []
+labels = []
+
+for number, path in enumerate(tqdm(dataList)):
+    X, sample_rate = librosa.load('/content/drive/MyDrive/Dataset/newP/RawData/' + path, res_type='kaiser_best',
+                                  duration=2.5, sr=22050 * 2, offset=0.5)
+    sample_rate = np.array(sample_rate)
+    mfccs = librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=39)
+    feature = mfccs
+    data.append(feature)
+
+    if path[6:8] == '01' or path[0:1] == 'n':
+        labels.append(6)
+    if path[6:8] == '02':
+        labels.append(6)
+    if path[6:8] == '03' or path[0:1] == 'h':
+        labels.append(3)
+    if path[6:8] == '04' or path[0:2] == 'sa':
+        labels.append(4)
+    if path[6:8] == '05' or path[0:1] == 'a':
+        labels.append(0)
+    if path[6:8] == '06' or path[0:1] == 'f':
+        labels.append(1)
+    if path[6:8] == '07' or path[0:1] == 'd':
+        labels.append(2)
+    if path[6:8] == '08' or path[0:2] == 'su':
+        labels.append(5)
+
+max_len = 216
+data = np.array([pad_sequences(x, maxlen=max_len, padding='post', truncating='post') for x in data])
+
+
+labels = np.array(labels)
+
+X_train, X_test, Y_train, Y_test = train_test_split(data, labels, test_size=0.3, random_state=42)
+
+numLabels = len(classLabels)
+
+Y_train = to_categorical(Y_train)
+Y_test = to_categorical(Y_test)
+
+X_train = X_train[..., np.newaxis]
+X_test = X_test[..., np.newaxis]
+
+model = Sequential()
+model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(X_train.shape[1:])))
+model.add(BatchNormalization())
+model.add(MaxPooling2D(pool_size=(2, 2)))
+
+model.add(Conv2D(64, (3, 3), activation='relu'))
+model.add(BatchNormalization())
+model.add(MaxPooling2D(pool_size=(2, 2)))
+
+model.add(Conv2D(128, (3, 3), activation='relu'))
+model.add(BatchNormalization())
+model.add(MaxPooling2D(pool_size=(2, 2)))
+
+model.add(Flatten())
+model.add(Dense(256, activation='relu'))
+model.add(Dropout(0.3))
+model.add(Dense(numLabels, activation='softmax'))
+
+model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+print(model.summary())
+
+best_acc = 0
+epochs = 50
+
+for i in tqdm(range(epochs)):
+    model.fit(X_train, Y_train, batch_size=32, epochs=1)
+    loss, acc = model.evaluate(X_test, Y_test)
+    if acc > best_acc:
+        best_acc = acc
+
+model.evaluate(X_test, Y_test)
+best_acc
+
+# After training the model
+
+# Save the model
+model.save("my_model.h5")
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IT20201364/models.py

+from flask_sqlalchemy import SQLAlchemy
+from datetime import datetime
+from flask_login import UserMixin
+
+db = SQLAlchemy()
+
+
+class User(db.Model, UserMixin):
+    __tablename__ = 'user'  # Specify the existing table name
+    __table_args__ = {'extend_existing': True}  # Add this line
+
+    id = db.Column(db.Integer, primary_key=True)
+    username = db.Column(db.String(255), unique=True, nullable=False)
+    password = db.Column(db.String(255), nullable=False)
+    login_time = db.Column(db.DateTime, default=datetime.utcnow)
+
+    def __init__(self, username, password):
+        self.username = username
+        self.password = password
+
+    def is_active(self):
+        return True  # You can customize this logic based on your application's requirements
+
+class StressLevel(db.Model):
+    __tablename__ = 'stress_level'  # Specify the table name
+
+    id = db.Column(db.Integer, primary_key=True)
+    user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)
+    stress_level = db.Column(db.String(255), nullable=False)
+    login_time = db.Column(db.DateTime, default=datetime.utcnow)  # Include the login_time field
+
+    user = db.relationship('User', backref=db.backref('stress_levels', lazy=True))
+
+

IT20201364/setup.py

+from setuptools import setup
+
+setup(
+    name='New_Voice_Assistant',
+    version='',
+    packages=[''],
+    url='',
+    license='',
+    author='malithg',
+    author_email='',
+    description=''
+)

IT20201364/text_to_speech.py

+import pyttsx3
+from flask import jsonify
+
+
+def text_to_speech(text):
+
+    print("Dev -->", text)
+    engine = pyttsx3.init()
+    engine.say(text)
+    engine.runAndWait()
+
+    # Send the conversation data to the front-end using jsonify (Flask)
+    jsonify({'message': text})

IT20201364/utils.py

+import datetime
+
+def wake_up(text, name):
+    if text is not None:
+        return True if name in text.lower() else False
+    return False
+
+
+def action_time():
+    return datetime.datetime.now().time().strftime('%H:%M')
+
+def calculate_stress_level(sentiment_analysis):
+    # Determine stress level based on detected emotions
+    if any(emotion in sentiment_analysis for emotion in ["Angry", "Fear", "Sad", "Disgust"]):
+        return "High Stress"
+    elif any(emotion in sentiment_analysis for emotion in ["Happy", "Disgust"]):
+        return "Low Stress"
+    else:
+        return "Low Stress"
+
+def calculate_emotion_percentages(sentiment_analysis):
+    total_emotions = len(sentiment_analysis)
+    emotion_count = {}
+    for emotion in sentiment_analysis:
+        emotion_count[emotion] = emotion_count.get(emotion, 0) + 1
+    emotion_percentages = {}
+    for emotion, count in emotion_count.items():
+        percentage = (count / total_emotions) * 100
+        emotion_percentages[emotion] = round(percentage, 2)
+    return emotion_percentages
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