tts part

.gitignore

+venv/

MobileApp/authentication/Authentication.js

+import React, { createContext, useContext, useState, useEffect, useLayoutEffect } from "react";
+import { nexlAxios } from "../../constants/helper";
+import AsyncStorage from "@react-native-community/async-storage";
+import {
+  IAuthContext,
+  IAuthRequest,
+  STORAGE_BEARER_TOKEN,
+  UNINITIALISED,
+  USER_EMAIL,
+} from "./IAuthContext";
+import { Loading } from "./state/Loading";
+import { getEnvironment } from "../../constants/variables";
+import { AxiosError, AxiosResponse } from "axios";
+import { rollbar } from "./ErrorBoundary";
+
+const AuthContext = createContext({
+  isLoggedIn: false,
+  token: "",
+  email: "",
+  error: "",
+  loading: false,
+  initialised: false,
+  login: UNINITIALISED,
+  logout: UNINITIALISED,
+});
+
+export const useAuthContext = () => useContext(AuthContext);
+
+export const AuthConsumer = AuthContext.Consumer;
+
+// interface IProps {
+//   children: any;
+// }
+// interface ILoginData {
+//   error: string;
+//   user: {
+//     email: string;
+//     id: string;
+//     first_name: string;
+//     last_name: string;
+//   };
+// }
+const endpoint = getEnvironment().endpoint;
+export const AuthProvider= ({ children }) => {
+  const [isLoggedIn, setIsLoggedIn] = useState(false);
+  const [token, setToken] = useState("");
+  const [email, setEmail] = useState("");
+  const [error, setError] = useState("");
+  const [loading, setLoading] = useState(false);
+  const [initialised, setInitialised] = useState(false);
+  const login = async ({ email, password, rememberMe }) => {
+    setLoading(true);
+    nexlAxios(false)
+      .post(`${endpoint}/accounts/sign_in.json`, {
+        source: "iOS app",
+        account: {
+          email,
+          password,
+          remember_me: rememberMe ? "1" : "0",
+        },
+      })
+      .then(async (response) => {
+        setLoading(false);
+        const user = response.data.user;
+        user.email && setIsLoggedIn(true);
+        const token = response.headers["authorization"];
+        setToken(token);
+        await AsyncStorage.setItem(USER_EMAIL, email);
+        setEmail(email);
+        rollbar.setPerson(user.id, `${user.first_name} ${user.last_name}`, user.email);
+      })
+      .catch((result) => {
+        setLoading(false);
+        setError(
+          result?.response?.data?.error || result?.message || "An unknown error has occurred.",
+        );
+      });
+  };
+  const loginWithBearerToken = async (bearer) => {
+    if (bearer) {
+      setLoading(true);
+      await nexlAxios(false, bearer)
+        .post(`${endpoint}/accounts/sign_in.json`, undefined, {
+          headers: {
+            Authorization: bearer,
+          },
+        })
+        .then((response) => {
+          setLoading(false);
+          const user = response.data.user;
+          user.email && setIsLoggedIn(true);
+          rollbar.setPerson(user.id, `${user.first_name} ${user.last_name}`, user.email);
+        })
+        .catch(() => {
+          setLoading(false);
+          setToken("");
+        });
+    }
+  };
+  const logout = async () => {
+    const token = await AsyncStorage.getItem(STORAGE_BEARER_TOKEN);
+    token && (await nexlAxios(false, token).post(`${endpoint}/sign_out.json`));
+    setToken("");
+    setIsLoggedIn(false);
+    await AsyncStorage.removeItem(STORAGE_BEARER_TOKEN);
+    rollbar.clearPerson();
+  };
+  // look if token / email exist in local storage on initialising the app
+  // if token exists, try log in with token
+  useLayoutEffect(() => {
+    let isMounted = true;
+    const initialise = async () => {
+      const bearerToken = await AsyncStorage.getItem(STORAGE_BEARER_TOKEN);
+      setToken(bearerToken || "");
+      const userEmail = await AsyncStorage.getItem(USER_EMAIL);
+      setEmail(userEmail || "");
+      bearerToken && (await loginWithBearerToken(bearerToken));
+      setInitialised(true);
+    };
+    if (isMounted) {
+      initialise();
+    }
+    return () => {
+      isMounted = false;
+    };
+  }, []);
+  // update token to local storage on token change
+  useEffect(() => {
+    AsyncStorage.setItem(STORAGE_BEARER_TOKEN, token);
+  }, [token]);
+  return (
+    <AuthContext.Provider
+      value={{
+        login,
+        isLoggedIn,
+        logout,
+        token,
+        email: email.toLowerCase(),
+        loading,
+        error,
+        initialised,
+      }}
+    >
+      {initialised ? children : <Loading copy="Logging in..." />}
+    </AuthContext.Provider>
+  );
+};
\ No newline at end of file

MobileApp/components/AudioRecorder.jsx

+import React from 'react'
+import { Text, View, StyleSheet, Button } from 'react-native';
+import { Audio } from 'expo-av';
+import { createIconSetFromFontello } from '@expo/vector-icons';
+import * as FileSystem from 'expo-file-system';
+
+
+export const AudioRecorder = ({ setDetectedText }) => {
+    const [recording, setRecording] = React.useState();
+
+    const [sound, setSound] = React.useState();
+
+    async function startRecording() {
+        try {
+            console.log('Requesting permissions..');
+            await Audio.requestPermissionsAsync();
+            await Audio.setAudioModeAsync({
+                allowsRecordingIOS: true,
+                playsInSilentModeIOS: true,
+            });
+            console.log('Starting recording..');
+            const { recording } = await Audio.Recording.createAsync(
+                Audio.RECORDING_OPTIONS_PRESET_HIGH_QUALITY
+            );
+            setRecording(recording);
+            console.log('Recording started');
+        } catch (err) {
+            console.error('Failed to start recording', err);
+        }
+    }
+
+    async function stopRecording() {
+        console.log('Stopping recording..');
+        setRecording(undefined);
+        await recording.stopAndUnloadAsync();
+        const uri = recording.getURI();
+
+
+
+
+        console.log('Recording stopped and stored at', uri);
+
+        console.log(recording)
+
+
+        const headers = {
+            'Content-Type': 'multipart/form-data'
+            // 'Content-Type': 'application/x-www-form-urlencoded',
+        }
+
+        playSound(uri)
+        FileSystem.uploadAsync("https://1ec4-112-134-210-155.ap.ngrok.io/mlmodels/detect/", uri, { headers: headers, uploadType: FileSystem.FileSystemUploadType.MULTIPART })
+            .then(data => JSON.parse(data.body))
+            .then(data => { console.log({ result: data }); setDetectedText(data.result); setTimeout(() => setDetectedText(''), 1000) })
+            .catch(err => console.log({ err }))
+
+
+
+    }
+    async function playSound(uri) {
+        console.log('Loading Sound');
+        // const { sound } = await Audio.Sound.createAsync(
+        //     require(uri)
+        // );
+        // setSound(sound);
+
+        // console.log('Playing Sound');
+        // await sound.playAsync();
+
+    }
+
+
+    return (
+        <Button
+            style={{ height: '100%', padding: 5 }}
+            title={recording ? 'Stop Recording' : 'Start Recording'}
+            onPress={recording ? stopRecording : startRecording}
+        />
+
+    );
+
+}
\ No newline at end of file

MobileApp/navigation/index.tsx

@@ -12,6 +12,7 @@ import { ColorSchemeName, Pressable } from 'react-native';
 
 import Colors from '../constants/Colors';
 import useColorScheme from '../hooks/useColorScheme';
+import { LoginScreen } from '../screens/loginScreen';
 import ModalScreen from '../screens/ModalScreen';
 import NotFoundScreen from '../screens/NotFoundScreen';
 import TabOneScreen from '../screens/TabOneScreen';
@@ -36,10 +37,28 @@ export default function Navigation({ colorScheme }: { colorScheme: ColorSchemeNa
 const Stack = createNativeStackNavigator<RootStackParamList>();
 
 function RootNavigator() {
+
+  const [isAuthenticated, setIsAutheticated] = React.useState(false)
+
+  const onLogin = () => {
+
+
+    setIsAutheticated(true)
+  }
+
+  const onLogout = () => {
+
+
+    setIsAutheticated(false)
+  }
+
   return (
-    <Stack.Navigator>
-      <Stack.Screen name="Root" component={BottomTabNavigator} options={{ headerShown: false }} />
+    <Stack.Navigator >
+      {isAuthenticated ? <Stack.Screen name="Root" options={{ headerShown: false }}>{(props) => <BottomTabNavigator {...props} onLogout={onLogout}/>}</Stack.Screen> :
+        <Stack.Screen name='Login'>{(props) => <LoginScreen {...props} onLogin={onLogin} />}</Stack.Screen>}
+
       <Stack.Screen name="NotFound" component={NotFoundScreen} options={{ title: 'Oops!' }} />
+
       <Stack.Group screenOptions={{ presentation: 'modal' }}>
         <Stack.Screen name="Modal" component={ModalScreen} />
       </Stack.Group>
@@ -53,7 +72,7 @@ function RootNavigator() {
  */
 const BottomTab = createBottomTabNavigator<RootTabParamList>();
 
-function BottomTabNavigator() {
+function BottomTabNavigator({onLogout}) {
   const colorScheme = useColorScheme();
 
   return (
@@ -66,11 +85,12 @@ function BottomTabNavigator() {
         name="TabOne"
         component={TabOneScreen}
         options={({ navigation }: RootTabScreenProps<'TabOne'>) => ({
-          title: 'Tab One',
+          title: 'Chat',
           tabBarIcon: ({ color }) => <TabBarIcon name="code" color={color} />,
           headerRight: () => (
             <Pressable
-              onPress={() => navigation.navigate('Modal')}
+              // onPress={() => navigation.navigate('Modal')}
+              onPress={onLogout}
               style={({ pressed }) => ({
                 opacity: pressed ? 0.5 : 1,
               })}>

MobileApp/package.json

@@ -18,14 +18,19 @@
     "@react-navigation/bottom-tabs": "^6.0.5",
     "@react-navigation/native": "^6.0.2",
     "@react-navigation/native-stack": "^6.1.0",
+    "axios": "^0.27.2",
     "expo": "~44.0.0",
     "expo-asset": "~8.4.4",
+    "expo-av": "~10.2.0",
     "expo-constants": "~13.0.0",
+    "expo-file-system": "~13.1.4",
     "expo-font": "~10.0.4",
     "expo-linking": "~3.0.0",
+    "expo-media-library": "~14.0.0",
     "expo-splash-screen": "~0.14.0",
     "expo-status-bar": "~1.2.0",
     "expo-web-browser": "~10.1.0",
+    "jwt-decode": "^3.1.2",
     "react": "17.0.1",
     "react-dom": "17.0.1",
     "react-native": "0.64.3",
@@ -37,8 +42,8 @@
     "@babel/core": "^7.12.9",
     "@types/react": "~17.0.21",
     "@types/react-native": "~0.64.12",
-    "jest-expo": "~44.0.1",
     "jest": "^26.6.3",
+    "jest-expo": "~44.0.1",
     "react-test-renderer": "17.0.1",
     "typescript": "~4.3.5"
   },

MobileApp/screens/TabOneScreen.tsx

+import React, { useState } from 'react'
 import { StyleSheet, TextInput } from 'react-native';
+import { AudioRecorder } from '../components/AudioRecorder';
 
 import EditScreenInfo from '../components/EditScreenInfo';
 import { Text, View } from '../components/Themed';
 import { RootTabScreenProps } from '../types';
 
 export default function TabOneScreen({ navigation }: RootTabScreenProps<'TabOne'>) {
+
+  const [detectedText, setDetectedText] = useState("")
+
   return (
     <View style={styles.container}>
-      <Text style={styles.title}>Tab One</Text>
-      <View style={{height: 70, width: 100, backgroundColor: ''}}><TextInput style={{height: 100, width: 1000, }} onChange={() => console.log('change')}></TextInput></View>
+      {/* <Text style={styles.title}>Tab One</Text>
+      <View style={{height: 70, width: 100, backgroundColor: ''}}><TextInput style={{borderColor: 'white', borderWidth: 1, alignSelf: }} onChange={() => console.log('change')}></TextInput></View>
       <View style={styles.separator} lightColor="#000" darkColor="rgba(255,0,255,0.1)" />
       
-      <EditScreenInfo path="/screens/TabOneScreen.tsx" />
+      <EditScreenInfo path="/screens/TabOneScreen.tsx" /> */}
+
+      <View style={{ flex: 1, width: '100%' }}>
+        <View style={{ display: 'flex', flex: 0.9, backgroundColor: 'green' }}>
+          <View style={{ alignItems: 'center', justifyContent: 'center', height: '100%'}}><Text style={{fontSize: 50, color: 'white'}}>{detectedText}</Text></View>
+        </View>
+        <View style={{ flex: 0.1,  backgroundColor: 'rgb(10,10,10)'}}>
+          <View style={{ flexDirection: 'row', display: 'flex', height: '100%' }}>
+            <View style={{ flex: 0.8,   backgroundColor: 'rgb(10,10,10)', height: '100%' }}></View>
+            <View style={{ flex: 0.2,  backgroundColor: 'rgb(10,10,10)', height:'100%'}}><AudioRecorder setDetectedText={setDetectedText} /></View>
+          </View>
+        </View>
+      </View>
     </View>
   );
 }

MobileApp/screens/TabTwoScreen.tsx

@@ -6,9 +6,7 @@ import { Text, View } from '../components/Themed';
 export default function TabTwoScreen() {
   return (
     <View style={styles.container}>
-      <Text style={styles.title}>Tab Two</Text>
-      <View style={styles.separator} lightColor="#eee" darkColor="rgba(255,255,255,0.1)" />
-      <EditScreenInfo path="/screens/TabTwoScreen.tsx" />
+    
     </View>
   );
 }

MobileApp/screens/loginScreen.js

+import React from 'react'
+import { StyleSheet, TextInput} from 'react-native';
+import EditScreenInfo from '../components/EditScreenInfo';
+import { Text, View } from '../components/Themed';
+import { TouchableOpacity } from 'react-native';
+
+
+const LoginForm = ({onLogin}) => {
+
+    var passwordInput
+
+    return (
+        <>
+            <TextInput style={styles.input}
+                autoCapitalize="none"
+                // onSubmitEditing={() => passwordInput.focus()}
+                autoCorrect={false}
+                keyboardType='email-address'
+                returnKeyType="next"
+                placeholder='Email'
+                placeholderTextColor='rgba(225,225,225,0.7)' />
+
+            <TextInput style={styles.input}
+                returnKeyType="go"
+                ref={(input) => passwordInput = input}
+                placeholder='Password'
+                placeholderTextColor='rgba(225,225,225,0.7)'
+                secureTextEntry />
+
+            <TouchableOpacity style={styles.buttonContainer}
+                // onPress={onButtonPress}
+                onPress={onLogin}
+                >
+                <Text style={styles.buttonText}>LOGIN</Text>
+            </TouchableOpacity>
+        </>
+        // define your styles
+    )
+}
+
+export const LoginScreen = ({onLogin}) => {
+
+
+    return (
+        <View style={styles.container}>
+            <View style={styles.loginContainer}>
+                {/* r<Image resizeMode="contain" style={styles.logo} source={require('../../components/images/logo-dark-bg.png')} /> */}
+            </View>
+
+            <View style={styles.formContainer}>
+                <LoginForm onLogin={onLogin}/>
+            </View>
+        </View>
+
+    );
+}
+
+
+
+
+// define your styles
+const styles = StyleSheet.create({
+    container: {
+        flex: 1,
+        backgroundColor: '#2c3e50',
+    },
+    loginContainer: {
+        alignItems: 'center',
+        flexGrow: 1,
+        justifyContent: 'center'
+    },
+    logo: {
+        position: 'absolute',
+        width: 300,
+        height: 100
+    },
+    container: {
+        padding: 20
+    },
+    input: {
+        height: 40,
+        backgroundColor: 'rgba(225,225,225,0.2)',
+        marginBottom: 10,
+        padding: 10,
+        color: '#fff'
+    },
+    buttonContainer: {
+        backgroundColor: '#2980b6',
+        paddingVertical: 15
+    },
+    buttonText: {
+        color: '#fff',
+        textAlign: 'center',
+        fontWeight: '700'
+    }
+})
\ No newline at end of file

MobileApp/types.tsx

@@ -16,6 +16,7 @@ declare global {
 export type RootStackParamList = {
   Root: NavigatorScreenParams<RootTabParamList> | undefined;
   Modal: undefined;
+  Login: undefined;
   NotFound: undefined;
 };
 

README.md

-Main objective:- Is to ensure that the passengers who are using public staff transport (Bus) are safe with covid and they are given the service according the government health regulations.
-
-
-Main Research questions: How to deliver a safe public transport during the pandemic?
-
-                         How to Ensure Passenger's health is safe?
-
-                         How to build trust and motivate passengers to use Public Transport?
-
-
-Individual research question
-
-IT18145380 - How to select the perfect seat based on passenger attributes?
-
-IT19012384 - How to manage real time tracking with the real time data?
-             How to do the payments through QR code with the public transportation?
-
-IT18094664 - Interfaces to perform in-vehicle tasks while driving?
-             voice or manual interfaces?
-
-IT19153728 - How to confirm the arrival of the relevant passenger and calculate the monthly fare based on the arrival of the passenger?
-
-
-
-
-
-Individual Objectives,
-
-IT18145380 - Provide a comfortable ride for the passenger
-
-IT19012384 - To be able to use the time effectively with the help of route tracking.
-             To decrease the use of paper and cash usage by using QR payment method.
-             To facilitate daily activities in transportation.
-
-
-IT18094664 - The main objective of this proposed research is to develop an application for the prevention of driver-related accidents and to make driver and passenger communication more efficient and convenient.
-
-
-IT19153728 - The main objective of this research is marking the passenger's arrival and identify the passenger.
+Main objective:- Is to ensure that the passengers who are using public staff transport (Bus) are safe with covid and they are given the service according the government health regulations.
+
+
+Main Research questions: How to deliver a safe public transport during the pandemic?
+
+                         How to Ensure Passenger's health is safe?
+
+                         How to build trust and motivate passengers to use Public Transport?
+
+
+Individual research question
+
+IT18145380 - How to select the perfect seat based on passenger attributes?
+
+IT19012384 - How to manage real time tracking with the real time data?
+             How to do the payments through QR code with the public transportation?
+
+IT18094664 - Interfaces to perform in-vehicle tasks while driving?
+             voice or manual interfaces?
+
+IT19153728 - How to confirm the arrival of the relevant passenger and calculate the monthly fare based on the arrival of the passenger?
+
+
+
+
+
+Individual Objectives,
+
+IT18145380 - Provide a comfortable ride for the passenger
+
+IT19012384 - To be able to use the time effectively with the help of route tracking.
+             To decrease the use of paper and cash usage by using QR payment method.
+             To facilitate daily activities in transportation.
+
+
+IT18094664 - The main objective of this proposed research is to develop an application for the prevention of driver-related accidents and to make driver and passenger communication more efficient and convenient.
+
+
+IT19153728 - The main objective of this research is marking the passenger's arrival and identify the passenger.

backend/.gitignore

-backend/data/
+backend/data/
 *.pyc 
\ No newline at end of file

backend/backend/asgi.py

-"""
-ASGI config for backend project.
-
-It exposes the ASGI callable as a module-level variable named ``application``.
-
-For more information on this file, see
-https://docs.djangoproject.com/en/4.0/howto/deployment/asgi/
-"""
-
-import os
-
-from django.core.asgi import get_asgi_application
-
-os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'backend.settings')
-
-application = get_asgi_application()
+"""
+ASGI config for backend project.
+
+It exposes the ASGI callable as a module-level variable named ``application``.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/4.0/howto/deployment/asgi/
+"""
+
+import os
+
+from django.core.asgi import get_asgi_application
+
+os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'backend.settings')
+
+application = get_asgi_application()

backend/backend/cms/admin.py

-from django.contrib import admin
-
-# Register your models here.
+from django.contrib import admin
+
+# Register your models here.

backend/backend/cms/apps.py

-from django.apps import AppConfig
-
-
-class CmsConfig(AppConfig):
-    default_auto_field = 'django.db.models.BigAutoField'
-    name = 'backend.cms'
+from django.apps import AppConfig
+
+
+class CmsConfig(AppConfig):
+    default_auto_field = 'django.db.models.BigAutoField'
+    name = 'backend.cms'

backend/backend/cms/migrations/0001_initial.py

-# Generated by Django 4.0.4 on 2022-04-25 19:51
-
-from django.db import migrations, models
-
-
-class Migration(migrations.Migration):
-
-    initial = True
-
-    dependencies = [
-    ]
-
-    operations = [
-        migrations.CreateModel(
-            name='MlModel',
-            fields=[
-                ('id', models.AutoField(primary_key=True, serialize=False)),
-                ('description', models.TextField(blank=True)),
-                ('timestamp', models.DateTimeField(auto_now=True)),
-                ('details', models.JSONField()),
-            ],
-        ),
-    ]
+# Generated by Django 4.0.4 on 2022-04-25 19:51
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    initial = True
+
+    dependencies = [
+    ]
+
+    operations = [
+        migrations.CreateModel(
+            name='MlModel',
+            fields=[
+                ('id', models.AutoField(primary_key=True, serialize=False)),
+                ('description', models.TextField(blank=True)),
+                ('timestamp', models.DateTimeField(auto_now=True)),
+                ('details', models.JSONField()),
+            ],
+        ),
+    ]

backend/backend/cms/model/predict.py

-import pickle
-from keras.models import load_model
-
-
-import numpy as np
-
-
-import IPython.display as ipd
-
-import random
-
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import LabelEncoder
-
-
-def predict():
-    model=load_model(r'./best_model_final.hdf5')
-
-    f1 = open('all_label.txt', 'rb')
-    all_label = pickle.load(f1)
-    print('loaded labels')
-
-    f2 = open('all_waves_file.txt', 'rb')
-    all_wave = pickle.load(f2)
-    print('loaded waves')
-
-    le = LabelEncoder()
-    y = le.fit_transform(all_label)
-    classes = list(le.classes_)
-
-    train_data_file = open("train_data_file.txt", 'rb')
-    [x_tr, x_val, y_tr, y_val] = np.load(train_data_file, allow_pickle=True)
-    train_data_file.close()
-
-    def predictSamples(audio):
-        prob=model.predict(audio.reshape(1,8000,1))
-        index=np.argmax(prob[0])
-        return classes[index]
-
-        
-    index=random.randint(0,len(x_val)-1)
-    samples=x_val[index].ravel()
-    print("Audio:",classes[np.argmax(y_val[index])])
-    ipd.Audio(samples, rate=8000)
-
-    print("Text:",predictSamples(samples))
+import pickle
+from keras.models import load_model
+
+
+import numpy as np
+
+
+import IPython.display as ipd
+
+import random
+
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+
+
+def predict(samples):
+    model=load_model(r'./best_model_final.hdf5')
+
+    f1 = open('all_label.txt', 'rb')
+    all_label = pickle.load(f1)
+    print('loaded labels')
+
+    f2 = open('all_waves_file.txt', 'rb')
+    all_wave = pickle.load(f2)
+    print('loaded waves')
+
+    le = LabelEncoder()
+    y = le.fit_transform(all_label)
+    classes = list(le.classes_)
+
+    train_data_file = open("train_data_file.txt", 'rb')
+    [x_tr, x_val, y_tr, y_val] = np.load(train_data_file, allow_pickle=True)
+    train_data_file.close()
+
+    def predictSamples(audio):
+        prob=model.predict(audio.reshape(1,8000,1))
+        index=np.argmax(prob[0])
+        return classes[index]
+
+        
+    # index=random.randint(0,len(x_val)-1)
+    # samples=x_val[index].ravel()
+    print(samples)
+    # print("Audio:",classes[np.argmax(y_val[index])])
+    ipd.Audio(samples, rate=8000)
+
\ No newline at end of file
+    result = predictSamples(samples)
+
+    print("Text:",result)
+
+    return result
\ No newline at end of file

backend/backend/cms/model/train.py

-import pickle
-from matplotlib import pyplot
-import os
-import librosa
-import IPython.display as ipd
-import matplotlib.pyplot as plt
-import numpy as np
-from scipy.io import wavfile
-import warnings
-
-from sklearn.preprocessing import LabelEncoder
-
-from keras.utils import np_utils
-
-from sklearn.model_selection import train_test_split
-
-from keras.layers import Dense, Dropout, Flatten, Conv1D, Input, MaxPooling1D
-from keras.models import Model
-from keras.callbacks import EarlyStopping, ModelCheckpoint
-from keras import backend as K
-K.clear_session()
-
-
-warnings.filterwarnings("ignore")
-
-# os.listdir('../../../data/')
-
-classes = ['down', 'go', 'left', 'no', 'off',
-           'on', 'right', 'stop', 'up', 'yes']
-
-
-def train():
-
-    print('1')
-    train_audio_path = r'./backend/data/train/train/audio/'
-    samples, sample_rate = librosa.load(
-        train_audio_path+'yes/0a7c2a8d_nohash_0.wav', sr=16000)
-    # fig = plt.figure(figsize=(14, 8))
-    # ax1 = fig.add_subplot(211)
-    # ax1.set_title('Raw wave of ' + r'../input/train/audio/yes/0a7c2a8d_nohash_0.wav')
-    # ax1.set_xlabel('time')
-    # ax1.set_ylabel('Amplitude')
-    # ax1.plot(np.linspace(0, sample_rate/len(samples), sample_rate), samples)
-
-    ipd.Audio(samples, rate=sample_rate)
-
-    print(sample_rate)
-
-    samples = librosa.resample(samples, sample_rate, 8000)
-    ipd.Audio(samples, rate=8000)
-
-    labels = os.listdir(train_audio_path)
-
-    # find count of each label and plot bar graph
-    no_of_recordings = []
-    for label in labels:
-        waves = [f for f in os.listdir(
-            train_audio_path + '/' + label) if f.endswith('.wav')]
-        no_of_recordings.append(len(waves))
-
-    # plot
-    # plt.figure(figsize=(30,5))
-    index = np.arange(len(labels))
-    # plt.bar(index, no_of_recordings)
-    # plt.xlabel('Commands', fontsize=12)
-    # plt.ylabel('No of recordings', fontsize=12)
-    # plt.xticks(index, labels, fontsize=15, rotation=60)
-    # plt.title('No. of recordings for each command')
-    # plt.show()
-
-    print('2')
-
-    labels = ["yes", "no", "up", "down", "left",
-              "right", "on", "off", "stop", "go"]
-
-    # labels_file = open('./labels_file.bin', 'wb+')
-    # pickle.dump(obj=labels, file=labels_file)
-    # labels_file.close()
-
-    # # file = open('./labels_file.bin', 'rb')
-    # # dict = pickle.load(file)
-    # # print('loaded')
-    # # print(dict)
-    # # print('fdnasf')
-
-    duration_of_recordings = []
-    for label in labels:
-        print('2.1', label)
-        waves = [f for f in os.listdir(
-            train_audio_path + '/' + label) if f.endswith('.wav')]
-        for wav in waves:
-            sample_rate, samples = wavfile.read(
-                train_audio_path + '/' + label + '/' + wav)
-            duration_of_recordings.append(float(len(samples)/sample_rate))
-
-    plt.hist(np.array(duration_of_recordings))
-
-    train_audio_path = r'./backend/data/train/train/audio/'
-
-    all_wave = []
-    all_label = []
-
-    f1 = open('all_label.txt', 'rb')
-    all_label = pickle.load(f1)
-
-    f2 = open('all_waves_file.txt', 'rb')
-    all_wave = pickle.load(f2)
-
-    if(all_wave and all_label):
-        print('loaded labels and waves')
-    else:
-        print('Creating labels and waves files')
-        for label in labels:
-            print(label)
-            waves = [f for f in os.listdir(
-                train_audio_path + '/' + label) if f.endswith('.wav')]
-            for wav in waves:
-                samples, sample_rate = librosa.load(
-                    train_audio_path + '/' + label + '/' + wav, sr=16000)
-                samples = librosa.resample(samples, sample_rate, 8000)
-                if(len(samples) == 8000):
-                    all_wave.append(samples)
-                    all_label.append(label)
-
-        # print('3')
-
-        all_labels_file = open('all_label.txt', 'wb+')
-        pickle.dump(file=all_labels_file, obj=all_label)
-        all_labels_file.close()
-
-        all_waves_file = open('all_waves_file.txt', 'wb+')
-        pickle.dump(file=all_waves_file, obj=all_wave)
-        all_waves_file.close()
-
-        print('Done: creating labels and waves files')
-
-    le = LabelEncoder()
-    y = le.fit_transform(all_label)
-    classes = list(le.classes_)
-
-    print('4')
-
-    y = np_utils.to_categorical(y, num_classes=len(labels))
-
-    all_wave = np.array(all_wave).reshape(-1, 8000, 1)
-
-    x_tr, x_val, y_tr, y_val = train_test_split(np.array(all_wave), np.array(
-        y), stratify=y, test_size=0.2, random_state=777, shuffle=True)
-
-    train_data_file = open('train_data_file.txt', 'wb+')
-    np.save(file=train_data_file, arr=np.array([x_tr, x_val, y_tr, y_val]))
-    train_data_file.close()
-
-    inputs = Input(shape=(8000, 1))
-
-    # First Conv1D layer
-    conv = Conv1D(8, 13, padding='valid', activation='relu', strides=1)(inputs)
-    conv = MaxPooling1D(3)(conv)
-    conv = Dropout(0.3)(conv)
-
-    # Second Conv1D layer
-    conv = Conv1D(16, 11, padding='valid', activation='relu', strides=1)(conv)
-    conv = MaxPooling1D(3)(conv)
-    conv = Dropout(0.3)(conv)
-
-    # Third Conv1D layer
-    conv = Conv1D(32, 9, padding='valid', activation='relu', strides=1)(conv)
-    conv = MaxPooling1D(3)(conv)
-    conv = Dropout(0.3)(conv)
-
-    # Fourth Conv1D layer
-    conv = Conv1D(64, 7, padding='valid', activation='relu', strides=1)(conv)
-    conv = MaxPooling1D(3)(conv)
-    conv = Dropout(0.3)(conv)
-
-    # Flatten layer
-    conv = Flatten()(conv)
-
-    # Dense Layer 1
-    conv = Dense(256, activation='relu')(conv)
-    conv = Dropout(0.3)(conv)
-
-    # Dense Layer 2
-    conv = Dense(128, activation='relu')(conv)
-    conv = Dropout(0.3)(conv)
-
-    outputs = Dense(len(labels), activation='softmax')(conv)
-
-    model = Model(inputs, outputs)
-    model.summary()
-
-    model.compile(loss='categorical_crossentropy',
-                  optimizer='adam', metrics=['accuracy'])
-
-    es = EarlyStopping(monitor='val_loss', mode='min',
-                       verbose=1, patience=10, min_delta=0.0001)
-    mc = ModelCheckpoint('best_model.hdf5', monitor='val_accuracy',
-                         verbose=1, save_best_only=True, mode='max')
-
-    history = model.fit(x_tr, y_tr, epochs=100, callbacks=[
-                        es, mc], batch_size=32, validation_data=(x_val, y_val))
-
-    # pyplot.plot(history.history['loss'], label='train')
-    # pyplot.plot(history.history['val_loss'], label='test')
-    # pyplot.legend()
-
-    # pyplot.show()
-
-    return history
+import pickle
+from matplotlib import pyplot
+import os
+import librosa
+import IPython.display as ipd
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.io import wavfile
+import warnings
+
+from sklearn.preprocessing import LabelEncoder
+
+from keras.utils import np_utils
+
+from sklearn.model_selection import train_test_split
+
+from keras.layers import Dense, Dropout, Flatten, Conv1D, Input, MaxPooling1D
+from keras.models import Model
+from keras.callbacks import EarlyStopping, ModelCheckpoint
+from keras import backend as K
+K.clear_session()
+
+
+warnings.filterwarnings("ignore")
+
+# os.listdir('../../../data/')
+
+classes = ['down', 'go', 'left', 'no', 'off',
+           'on', 'right', 'stop', 'up', 'yes']
+
+
+def train():
+
+    print('1')
+    train_audio_path = r'./backend/data/train/train/audio/'
+    samples, sample_rate = librosa.load(
+        train_audio_path+'yes/0a7c2a8d_nohash_0.wav', sr=16000)
+    # fig = plt.figure(figsize=(14, 8))
+    # ax1 = fig.add_subplot(211)
+    # ax1.set_title('Raw wave of ' + r'../input/train/audio/yes/0a7c2a8d_nohash_0.wav')
+    # ax1.set_xlabel('time')
+    # ax1.set_ylabel('Amplitude')
+    # ax1.plot(np.linspace(0, sample_rate/len(samples), sample_rate), samples)
+
+    ipd.Audio(samples, rate=sample_rate)
+
+    print(sample_rate)
+
+    samples = librosa.resample(samples, sample_rate, 8000)
+    ipd.Audio(samples, rate=8000)
+
+    labels = os.listdir(train_audio_path)
+
+    # find count of each label and plot bar graph
+    no_of_recordings = []
+    for label in labels:
+        waves = [f for f in os.listdir(
+            train_audio_path + '/' + label) if f.endswith('.wav')]
+        no_of_recordings.append(len(waves))
+
+    # plot
+    # plt.figure(figsize=(30,5))
+    index = np.arange(len(labels))
+    # plt.bar(index, no_of_recordings)
+    # plt.xlabel('Commands', fontsize=12)
+    # plt.ylabel('No of recordings', fontsize=12)
+    # plt.xticks(index, labels, fontsize=15, rotation=60)
+    # plt.title('No. of recordings for each command')
+    # plt.show()
+
+    print('2')
+
+    labels = ["yes", "no", "up", "down", "left",
+              "right", "on", "off", "stop", "go"]
+
+    # labels_file = open('./labels_file.bin', 'wb+')
+    # pickle.dump(obj=labels, file=labels_file)
+    # labels_file.close()
+
+    # # file = open('./labels_file.bin', 'rb')
+    # # dict = pickle.load(file)
+    # # print('loaded')
+    # # print(dict)
+    # # print('fdnasf')
+
+    duration_of_recordings = []
+    for label in labels:
+        print('2.1', label)
+        waves = [f for f in os.listdir(
+            train_audio_path + '/' + label) if f.endswith('.wav')]
+        for wav in waves:
+            sample_rate, samples = wavfile.read(
+                train_audio_path + '/' + label + '/' + wav)
+            duration_of_recordings.append(float(len(samples)/sample_rate))
+
+    plt.hist(np.array(duration_of_recordings))
+
+    train_audio_path = r'./backend/data/train/train/audio/'
+
+    all_wave = []
+    all_label = []
+
+    f1 = open('all_label.txt', 'rb')
+    all_label = pickle.load(f1)
+
+    f2 = open('all_waves_file.txt', 'rb')
+    all_wave = pickle.load(f2)
+
+    if(all_wave and all_label):
+        print('loaded labels and waves')
+    else:
+        print('Creating labels and waves files')
+        for label in labels:
+            print(label)
+            waves = [f for f in os.listdir(
+                train_audio_path + '/' + label) if f.endswith('.wav')]
+            for wav in waves:
+                samples, sample_rate = librosa.load(
+                    train_audio_path + '/' + label + '/' + wav, sr=16000)
+                samples = librosa.resample(samples, sample_rate, 8000)
+                if(len(samples) == 8000):
+                    all_wave.append(samples)
+                    all_label.append(label)
+
+        # print('3')
+
+        all_labels_file = open('all_label.txt', 'wb+')
+        pickle.dump(file=all_labels_file, obj=all_label)
+        all_labels_file.close()
+
+        all_waves_file = open('all_waves_file.txt', 'wb+')
+        pickle.dump(file=all_waves_file, obj=all_wave)
+        all_waves_file.close()
+
+        print('Done: creating labels and waves files')
+
+    le = LabelEncoder()
+    y = le.fit_transform(all_label)
+    classes = list(le.classes_)
+
+    print('4')
+
+    y = np_utils.to_categorical(y, num_classes=len(labels))
+
+    all_wave = np.array(all_wave).reshape(-1, 8000, 1)
+
+    x_tr, x_val, y_tr, y_val = train_test_split(np.array(all_wave), np.array(
+        y), stratify=y, test_size=0.2, random_state=777, shuffle=True)
+
+    train_data_file = open('train_data_file.txt', 'wb+')
+    np.save(file=train_data_file, arr=np.array([x_tr, x_val, y_tr, y_val]))
+    train_data_file.close()
+
+    inputs = Input(shape=(8000, 1))
+
+    # First Conv1D layer
+    conv = Conv1D(8, 13, padding='valid', activation='relu', strides=1)(inputs)
+    conv = MaxPooling1D(3)(conv)
+    conv = Dropout(0.3)(conv)
+
+    # Second Conv1D layer
+    conv = Conv1D(16, 11, padding='valid', activation='relu', strides=1)(conv)
+    conv = MaxPooling1D(3)(conv)
+    conv = Dropout(0.3)(conv)
+
+    # Third Conv1D layer
+    conv = Conv1D(32, 9, padding='valid', activation='relu', strides=1)(conv)
+    conv = MaxPooling1D(3)(conv)
+    conv = Dropout(0.3)(conv)
+
+    # Fourth Conv1D layer
+    conv = Conv1D(64, 7, padding='valid', activation='relu', strides=1)(conv)
+    conv = MaxPooling1D(3)(conv)
+    conv = Dropout(0.3)(conv)
+
+    # Flatten layer
+    conv = Flatten()(conv)
+
+    # Dense Layer 1
+    conv = Dense(256, activation='relu')(conv)
+    conv = Dropout(0.3)(conv)
+
+    # Dense Layer 2
+    conv = Dense(128, activation='relu')(conv)
+    conv = Dropout(0.3)(conv)
+
+    outputs = Dense(len(labels), activation='softmax')(conv)
+
+    model = Model(inputs, outputs)
+    model.summary()
+
+    model.compile(loss='categorical_crossentropy',
+                  optimizer='adam', metrics=['accuracy'])
+
+    es = EarlyStopping(monitor='val_loss', mode='min',
+                       verbose=1, patience=10, min_delta=0.0001)
+    mc = ModelCheckpoint('best_model.hdf5', monitor='val_accuracy',
+                         verbose=1, save_best_only=True, mode='max')
+
+    history = model.fit(x_tr, y_tr, epochs=100, callbacks=[
+                        es, mc], batch_size=32, validation_data=(x_val, y_val))
+
+    # pyplot.plot(history.history['loss'], label='train')
+    # pyplot.plot(history.history['val_loss'], label='test')
+    # pyplot.legend()
+
+    # pyplot.show()
+
+    return history

backend/backend/cms/models.py

-from django.db import models
-
-# Create your models here.
-class MlModel(models.Model):
-    id = models.AutoField(primary_key=True)
-    description = models.TextField(blank=True)
-    timestamp = models.DateTimeField(blank=True, auto_now=True)
-    details = models.JSONField()
+from django.db import models
+
+# Create your models here.
+class MlModel(models.Model):
+    id = models.AutoField(primary_key=True)
+    description = models.TextField(blank=True)
+    timestamp = models.DateTimeField(blank=True, auto_now=True)
+    details = models.JSONField()

backend/backend/cms/serializers.py

-from django.contrib.auth.models import User, Group
-from .models import MlModel
-from rest_framework import serializers
-
-
-class UserSerializer(serializers.HyperlinkedModelSerializer):
-    class Meta:
-        model = User
-        fields = ['url', 'username', 'email', 'groups']
-
-
-class GroupSerializer(serializers.HyperlinkedModelSerializer):
-    class Meta:
-        model = Group
-        fields = ['url', 'name']
-
-class MlModelSerializer(serializers.ModelSerializer):
-    class Meta:
-        model = MlModel
-        fields = (
-            '__all__'
+from django.contrib.auth.models import User, Group
+from .models import MlModel
+from rest_framework import serializers
+
+
+class UserSerializer(serializers.HyperlinkedModelSerializer):
+    class Meta:
+        model = User
+        fields = ['url', 'username', 'email', 'groups']
+
+
+class GroupSerializer(serializers.HyperlinkedModelSerializer):
+    class Meta:
+        model = Group
+        fields = ['url', 'name']
+
+class MlModelSerializer(serializers.ModelSerializer):
+    class Meta:
+        model = MlModel
+        fields = (
+            '__all__'
         )
\ No newline at end of file

backend/backend/cms/tests.py

-from django.test import TestCase
-
-# Create your tests here.
+from django.test import TestCase
+
+# Create your tests here.

backend/backend/cms/views.py

-from http.client import HTTPResponse
-from django.contrib.auth.models import User, Group
-from rest_framework import viewsets
-from rest_framework import permissions
-from backend.cms.serializers import MlModelSerializer
-from backend.cms.serializers import UserSerializer, GroupSerializer
-from rest_framework.decorators import action
-from rest_framework.response import Response
-
-from .models import MlModel
-
-from .model.train import train
-
-from .model.predict import predict
-
-
-class UserViewSet(viewsets.ModelViewSet):
-    """
-    API endpoint that allows users to be viewed or edited.
-    """
-    queryset = User.objects.all().order_by('-date_joined')
-    serializer_class = UserSerializer
-    permission_classes = [permissions.IsAuthenticated]
-
-    @action(detail=False)
-    def runFunction(*args, **kwargs):
-
-
-        print('Function ran')
-        results = train()
-        print(results)
-
-        return Response({'success': True})
-
-
-class GroupViewSet(viewsets.ModelViewSet):
-    """
-    API endpoint that allows groups to be viewed or edited.
-    """
-    queryset = Group.objects.all()
-    serializer_class = GroupSerializer
-    permission_classes = [permissions.IsAuthenticated]
-
-class MlModelViewSet(viewsets.ViewSet):
-
-    queryset = MlModel.objects.all()
-    serializer_class = MlModelSerializer
-    permission_classes = [permissions.IsAuthenticated]
-
-    @action(detail=False)
-    def train(*args, **kwargs):
-        print('Function ran')
-        results = train()
-        print(results)
-        return Response({'success': True})
-
-
-    @action(detail=False)
-    def predict(*args, **kwargs):
-        print('Function ran')
-        results = predict()
-        print(results)
-        return Response({'success': True})
-
-    
-
-    
+from http.client import HTTPResponse
+from django.contrib.auth.models import User, Group
+from rest_framework import viewsets
+from rest_framework import permissions
+from backend.cms.serializers import MlModelSerializer
+from backend.cms.serializers import UserSerializer, GroupSerializer
+from rest_framework.decorators import action
+from rest_framework.response import Response
+import mimetypes
+import os
+from rest_framework.parsers import MultiPartParser
+from io import BytesIO
+
+import librosa
+
+
+from django.core.files.storage import FileSystemStorage
+
+from .models import MlModel
+
+from .model.train import train
+
+from .model.predict import predict
+
+
+from pydub import AudioSegment
+import numpy as np
+
+
+class UserViewSet(viewsets.ModelViewSet):
+    """
+    API endpoint that allows users to be viewed or edited.
+    """
+    queryset = User.objects.all().order_by('-date_joined')
+    serializer_class = UserSerializer
+    permission_classes = [permissions.IsAuthenticated]
+
+    @action(detail=False)
+    def runFunction(*args, **kwargs):
+
+
+        print('Function ran')
+        results = train()
+        print(results)
+
+        return Response({'success': True})
+
+
+class GroupViewSet(viewsets.ModelViewSet):
+    """
+    API endpoint that allows groups to be viewed or edited.
+    """
+    queryset = Group.objects.all()
+    serializer_class = GroupSerializer
+    permission_classes = [permissions.IsAuthenticated]
+
+class MlModelViewSet(viewsets.ViewSet):
+
+    queryset = MlModel.objects.all()
+    serializer_class = MlModelSerializer
+    permission_classes = [permissions.AllowAny]
+    parser_classes = [MultiPartParser]
+
+    @action(detail=False)
+    def train(*args, **kwargs):
+        print('Function ran')
+        results = train()
+        print(results)
+        return Response({'success': True})
+
+
+    @action(detail=False, methods=["POST"])
+    def detect(self, request, *args, **kwargs):
+        print('Function ran')
+        print(request.headers)
+        bytesio_object = list(request.data.dict().values())[0].file
+        print(bytesio_object)
+
+        
+        with open("output.m4a", "wb") as f:
+            f.write(bytesio_object.getbuffer())
+        
+
+            # convert wav to mp3                                                            
+        audSeg = AudioSegment.from_file("output.m4a")
+        audSeg.export('output.wav', format="wav")
+
+
+        samples, sample_rate = librosa.load(
+                    'output.wav', sr=16000)
+        samples = librosa.resample(samples, sample_rate, 8000)
+
+        print('---------------------------------------------------------')
+        if(samples.shape[0] > 8000): 
+            print('grateer -------------------------------------')
+            samples = samples[-8000:]
+            print(samples.shape)
+        else:
+            len = 8000 - samples.shape[0]
+            new_arr = np.zeroes(len, )
+            samples = np.concatenate((samples, new_arr))
+            print(samples.shape)
+
+
+        # audio_file = request.data
+
+        # # Using File storage to save file for future converting
+        # fs = FileSystemStorage()
+        # file_name = fs.save(audio_file.name, audio_file)
+        # audio_file_url = fs.url(file_name)
+
+        # # Preparing paths for convertion
+        # upstream = os.path.dirname(os.path.dirname(os.path.dirname(
+        #     os.path.abspath(__file__))))
+        # path = os.path.join(upstream, 'media', audio_file.name)
+        # mp3_filename = '.'.join([audio_file.name.split('.')[0], 'mp3'])
+        # new_path = os.path.join(
+        #     upstream, 'media', mp3_filename)
+
+        # # Converting to mp3 here
+        # wma_version = AudioSegment.from_file(path, "wav")
+        # wma_version.export(new_path, format="mp3")
+        
+        # user_id = self.request.user.id
+        
+        # # I was trying to create a Track instance, the mp3 get's saved but it is not being saved using location specified in models.
+        # track = Track.objects.create(user_id=user_id)
+        # track.file.name = mp3_filename
+        # track.save()
+
+
+
+        results = predict(samples)
+        print(results)
+        return Response({'success': True, 'result': results})
+
+    
+
+    

backend/backend/settings.py

-"""
-Django settings for backend project.
-
-Generated by 'django-admin startproject' using Django 4.0.4.
-
-For more information on this file, see
-https://docs.djangoproject.com/en/4.0/topics/settings/
-
-For the full list of settings and their values, see
-https://docs.djangoproject.com/en/4.0/ref/settings/
-"""
-
-from pathlib import Path
-
-# Build paths inside the project like this: BASE_DIR / 'subdir'.
-BASE_DIR = Path(__file__).resolve().parent.parent
-
-
-# Quick-start development settings - unsuitable for production
-# See https://docs.djangoproject.com/en/4.0/howto/deployment/checklist/
-
-# SECURITY WARNING: keep the secret key used in production secret!
-SECRET_KEY = 'django-insecure-54-4$#$7yzk9*+myixyk2fu(-u0sg(wtf8a$ets11yeqs5w0ly'
-
-# SECURITY WARNING: don't run with debug turned on in production!
-DEBUG = True
-
-ALLOWED_HOSTS = []
-
-
-# Application definition
-
-INSTALLED_APPS = [
-    'django.contrib.admin',
-    'django.contrib.auth',
-    'django.contrib.contenttypes',
-    'django.contrib.sessions',
-    'django.contrib.messages',
-    'django.contrib.staticfiles',
-    'rest_framework',
-    'backend.cms'
-]
-
-MIDDLEWARE = [
-    'django.middleware.security.SecurityMiddleware',
-    'django.contrib.sessions.middleware.SessionMiddleware',
-    'django.middleware.common.CommonMiddleware',
-    'django.middleware.csrf.CsrfViewMiddleware',
-    'django.contrib.auth.middleware.AuthenticationMiddleware',
-    'django.contrib.messages.middleware.MessageMiddleware',
-    'django.middleware.clickjacking.XFrameOptionsMiddleware',
-]
-
-ROOT_URLCONF = 'backend.urls'
-
-TEMPLATES = [
-    {
-        'BACKEND': 'django.template.backends.django.DjangoTemplates',
-        'DIRS': [],
-        'APP_DIRS': True,
-        'OPTIONS': {
-            'context_processors': [
-                'django.template.context_processors.debug',
-                'django.template.context_processors.request',
-                'django.contrib.auth.context_processors.auth',
-                'django.contrib.messages.context_processors.messages',
-            ],
-        },
-    },
-]
-
-WSGI_APPLICATION = 'backend.wsgi.application'
-
-
-# Database
-# https://docs.djangoproject.com/en/4.0/ref/settings/#databases
-
-DATABASES = {
-    'default': {
-        'ENGINE': 'django.db.backends.sqlite3',
-        'NAME': BASE_DIR / 'db.sqlite3',
-    }
-}
-
-
-# Password validation
-# https://docs.djangoproject.com/en/4.0/ref/settings/#auth-password-validators
-
-AUTH_PASSWORD_VALIDATORS = [
-    {
-        'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
-    },
-    {
-        'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
-    },
-    {
-        'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
-    },
-    {
-        'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
-    },
-]
-
-
-# Internationalization
-# https://docs.djangoproject.com/en/4.0/topics/i18n/
-
-LANGUAGE_CODE = 'en-us'
-
-TIME_ZONE = 'UTC'
-
-USE_I18N = True
-
-USE_TZ = True
-
-
-# Static files (CSS, JavaScript, Images)
-# https://docs.djangoproject.com/en/4.0/howto/static-files/
-
-STATIC_URL = 'static/'
-
-# Default primary key field type
-# https://docs.djangoproject.com/en/4.0/ref/settings/#default-auto-field
-
-DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'
-
-REST_FRAMEWORK = {
-    'DEFAULT_PAGINATION_CLASS': 'rest_framework.pagination.PageNumberPagination',
-    'PAGE_SIZE': 10
+"""
+Django settings for backend project.
+
+Generated by 'django-admin startproject' using Django 4.0.4.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/4.0/topics/settings/
+
+For the full list of settings and their values, see
+https://docs.djangoproject.com/en/4.0/ref/settings/
+"""
+
+from pathlib import Path
+
+import datetime
+
+# Build paths inside the project like this: BASE_DIR / 'subdir'.
+BASE_DIR = Path(__file__).resolve().parent.parent
+
+
+# Quick-start development settings - unsuitable for production
+# See https://docs.djangoproject.com/en/4.0/howto/deployment/checklist/
+
+# SECURITY WARNING: keep the secret key used in production secret!
+SECRET_KEY = 'django-insecure-54-4$#$7yzk9*+myixyk2fu(-u0sg(wtf8a$ets11yeqs5w0ly'
+
+# SECURITY WARNING: don't run with debug turned on in production!
+DEBUG = True
+
+ALLOWED_HOSTS = ['*']
+
+
+# Application definition
+
+INSTALLED_APPS = [
+    'django.contrib.admin',
+    'django.contrib.auth',
+    'django.contrib.contenttypes',
+    'django.contrib.sessions',
+    'django.contrib.messages',
+    'django.contrib.staticfiles',
+    'rest_framework',
+    'backend.cms'
+]
+
+MIDDLEWARE = [
+    'django.middleware.security.SecurityMiddleware',
+    'django.contrib.sessions.middleware.SessionMiddleware',
+    'django.middleware.common.CommonMiddleware',
+    'django.middleware.csrf.CsrfViewMiddleware',
+    'django.contrib.auth.middleware.AuthenticationMiddleware',
+    'django.contrib.messages.middleware.MessageMiddleware',
+    'django.middleware.clickjacking.XFrameOptionsMiddleware',
+]
+
+ROOT_URLCONF = 'backend.urls'
+
+TEMPLATES = [
+    {
+        'BACKEND': 'django.template.backends.django.DjangoTemplates',
+        'DIRS': [],
+        'APP_DIRS': True,
+        'OPTIONS': {
+            'context_processors': [
+                'django.template.context_processors.debug',
+                'django.template.context_processors.request',
+                'django.contrib.auth.context_processors.auth',
+                'django.contrib.messages.context_processors.messages',
+            ],
+        },
+    },
+]
+
+WSGI_APPLICATION = 'backend.wsgi.application'
+
+
+# Database
+# https://docs.djangoproject.com/en/4.0/ref/settings/#databases
+
+DATABASES = {
+    'default': {
+        'ENGINE': 'django.db.backends.sqlite3',
+        'NAME': BASE_DIR / 'db.sqlite3',
+    }
+}
+
+
+# Password validation
+# https://docs.djangoproject.com/en/4.0/ref/settings/#auth-password-validators
+
+AUTH_PASSWORD_VALIDATORS = [
+    {
+        'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
+    },
+    {
+        'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
+    },
+    {
+        'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
+    },
+    {
+        'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
+    },
+]
+
+
+# Internationalization
+# https://docs.djangoproject.com/en/4.0/topics/i18n/
+
+LANGUAGE_CODE = 'en-us'
+
+TIME_ZONE = 'UTC'
+
+USE_I18N = True
+
+USE_TZ = True
+
+
+# Static files (CSS, JavaScript, Images)
+# https://docs.djangoproject.com/en/4.0/howto/static-files/
+
+STATIC_URL = 'static/'
+
+# Default primary key field type
+# https://docs.djangoproject.com/en/4.0/ref/settings/#default-auto-field
+
+DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'
+
+REST_FRAMEWORK = {
+    'DEFAULT_PAGINATION_CLASS': 'rest_framework.pagination.PageNumberPagination',
+    'PAGE_SIZE': 10,
+    'DEFAULT_AUTHENTICATION_CLASSES': (
+        'rest_framework_simplejwt.authentication.JWTAuthentication',
+        'rest_framework.authentication.SessionAuthentication',
+    )
+}
+
+SIMPLE_JWT = {
+    'ACCESS_TOKEN_LIFETIME': datetime.timedelta(minutes=14*24*60),
+    'REFRESH_TOKEN_LIFETIME': datetime.timedelta(days=30),
+    'ROTATE_REFRESH_TOKENS': True,
+    'BLACKLIST_AFTER_ROTATION': True,
+
+    'ALGORITHM': 'HS256',
+    'SIGNING_KEY': SECRET_KEY,
+    'VERIFYING_KEY': None,
+
+    'AUTH_HEADER_TYPES': ('Bearer',),
+    'USER_ID_FIELD': 'id',
+    'USER_ID_CLAIM': 'user_id',
+
+    'AUTH_TOKEN_CLASSES': ('rest_framework_simplejwt.tokens.AccessToken',),
+    'TOKEN_TYPE_CLAIM': 'token_type',
+
+    'SLIDING_TOKEN_REFRESH_EXP_CLAIM': 'refresh_exp',
+    'SLIDING_TOKEN_LIFETIME': datetime.timedelta(minutes=5),
+    'SLIDING_TOKEN_REFRESH_LIFETIME': datetime.timedelta(days=1),
 }
\ No newline at end of file

backend/backend/urls.py

-from django.urls import include, path
-from rest_framework import routers
-from backend.cms import views
-
-router = routers.DefaultRouter()
-router.register(r'users', views.UserViewSet)
-router.register(r'groups', views.GroupViewSet)
-router.register(r'mlmodels', views.MlModelViewSet)
-
-# Wire up our API using automatic URL routing.
-# Additionally, we include login URLs for the browsable API.
-urlpatterns = [
-    path('', include(router.urls)),
-    path('api-auth/', include('rest_framework.urls', namespace='rest_framework'))
+from django.urls import include, path, re_path
+from rest_framework import routers
+from backend.cms import views
+from rest_framework_simplejwt.views import TokenObtainPairView, TokenRefreshView
+
+router = routers.DefaultRouter()
+router.register(r'users', views.UserViewSet)
+router.register(r'groups', views.GroupViewSet)
+router.register(r'mlmodels', views.MlModelViewSet)
+
+# Wire up our API using automatic URL routing.
+# Additionally, we include login URLs for the browsable API.
+urlpatterns = [
+    path('', include(router.urls)),
+    path('api-auth/', include('rest_framework.urls', namespace='rest_framework')),
+    re_path(r'^api/auth/token/obtain/$', TokenObtainPairView.as_view()),
+    re_path(r'^api/auth/token/refresh/$', TokenRefreshView.as_view())
 ]
\ No newline at end of file

backend/backend/wsgi.py

-"""
-WSGI config for backend project.
-
-It exposes the WSGI callable as a module-level variable named ``application``.
-
-For more information on this file, see
-https://docs.djangoproject.com/en/4.0/howto/deployment/wsgi/
-"""
-
-import os
-
-from django.core.wsgi import get_wsgi_application
-
-os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'backend.settings')
-
-application = get_wsgi_application()
+"""
+WSGI config for backend project.
+
+It exposes the WSGI callable as a module-level variable named ``application``.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/4.0/howto/deployment/wsgi/
+"""
+
+import os
+
+from django.core.wsgi import get_wsgi_application
+
+os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'backend.settings')
+
+application = get_wsgi_application()

backend/manage.py

-#!/usr/bin/env python
-"""Django's command-line utility for administrative tasks."""
-import os
-import sys
-
-
-def main():
-    """Run administrative tasks."""
-    os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'backend.settings')
-    try:
-        from django.core.management import execute_from_command_line
-    except ImportError as exc:
-        raise ImportError(
-            "Couldn't import Django. Are you sure it's installed and "
-            "available on your PYTHONPATH environment variable? Did you "
-            "forget to activate a virtual environment?"
-        ) from exc
-    execute_from_command_line(sys.argv)
-
-
-if __name__ == '__main__':
-    main()
+#!/usr/bin/env python
+"""Django's command-line utility for administrative tasks."""
+import os
+import sys
+
+
+def main():
+    """Run administrative tasks."""
+    os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'backend.settings')
+    try:
+        from django.core.management import execute_from_command_line
+    except ImportError as exc:
+        raise ImportError(
+            "Couldn't import Django. Are you sure it's installed and "
+            "available on your PYTHONPATH environment variable? Did you "
+            "forget to activate a virtual environment?"
+        ) from exc
+    execute_from_command_line(sys.argv)
+
+
+if __name__ == '__main__':
+    main()

requirements.txt

 absl-py==1.0.0