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Commit de86a677 authored by Malsha Rathnasiri's avatar Malsha Rathnasiri
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tts part

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MobileApp/authentication/Authentication.js 0 → 100644
View file @ de86a677
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 0 → 100644
View file @ de86a677
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
View file @ de86a677
......@@ -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
View file @ de86a677
......@@ -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
View file @ de86a677
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
View file @ de86a677
......@@ -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 0 → 100644
View file @ de86a677
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
View file @ de86a677
......@@ -16,6 +16,7 @@ declare global {
export type RootStackParamList = {
Root: NavigatorScreenParams<RootTabParamList> | undefined;
Modal: undefined;
Login: undefined;
NotFound: undefined;
};
......
MobileApp/yarn.lock
View file @ de86a677
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README.md
View file @ de86a677
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
View file @ de86a677
backend/data/
backend/data/
*.pyc
\ No newline at end of file
backend/backend/asgi.py
View file @ de86a677
"""
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
View file @ de86a677
from django.contrib import admin
# Register your models here.
from django.contrib import admin
# Register your models here.
backend/backend/cms/apps.py
View file @ de86a677
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
View file @ de86a677
# 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
View file @ de86a677
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))
\ No newline at end of file
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)
result = predictSamples(samples)
print("Text:",result)
return result
\ No newline at end of file
backend/backend/cms/model/train.py
View file @ de86a677
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
View file @ de86a677
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
View file @ de86a677
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
View file @ de86a677
from django.test import TestCase
# Create your tests here.
from django.test import TestCase
# Create your tests here.
backend/backend/cms/views.py
View file @ de86a677
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
View file @ de86a677
"""
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
View file @ de86a677
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
View file @ de86a677
"""
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
View file @ de86a677
#!/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()
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