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Commit 7cca654f authored by Malsha Rathnasiri's avatar Malsha Rathnasiri
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add authentication and temporary remove tensorflow

parent 88281141
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MobileApp/api/Authentication.js 0 → 100644
View file @ 7cca654f
import React, { createContext, useContext, useState, useEffect, useLayoutEffect } from "react";
import axios from "axios";
import { LOCALHOST } from "./constants";
// import { nexlAxios } from "../../constants/helper";
import AsyncStorage from "@react-native-async-storage/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";
// import { ENDPOINT as endpoint } from "./constants";
// 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 login = async (username, password) => {
const endpoint = false ? 'register' : 'login';
const payload = { username: username, password: password }
const headers = new Headers({ "Content-Type": 'application./json','mode': 'no-cors', 'Access-Control-Allow-Headers': '*' })
const options = { method: 'POST', data: payload, headers }
axios.defaults.baseURL = 'http://127.0.0.1:8000';
axios.defaults.timeout = 1500;
console.log({payload})
return axios.post('/api/auth/token/obtain/', payload)
.then(response => {
const { access, refresh, user } = response.data;
axios.defaults.headers.common.Authorization = `Token ${token}`;
AsyncStorage.setItem('user_id', user.id)
AsyncStorage.setitem('username',user.username)
AsyncStorage.setItem('user_email', user.email)
AsyncStorage.setitem('access_token', access)
AsyncStorage.setItem('refresh_token', refresh)
console.log("login successful")
return true
// Actions.main();
})
.catch(error => {
throw Error(error)
});
}
// export const AuthProvider = ({ children }) => {
// const [isLoggedIn, setIsLoggedIn] = useState(false);
// const [token, setToken] = useState("");
// const [email, setEmail] = useState("");
// const [error, setError] = useState("");
// const [loading, setLoading] = useState(false);
// const [initialised, setInitialised] = useState(false);
// const login = async ({ email, password, rememberMe }) => {
// setLoading(true);
// nexlAxios(false)
// .post(`${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/api/constants.js 0 → 100644
View file @ 7cca654f
export const LOCALHOST = "http://127.0.0.1:8000"
\ No newline at end of file
MobileApp/authentication/Authentication.js deleted 100644 → 0
View file @ 88281141
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/navigation/index.tsx
View file @ 7cca654f
......@@ -9,13 +9,14 @@ import { NavigationContainer, DefaultTheme, DarkTheme } from '@react-navigation/
import { createNativeStackNavigator } from '@react-navigation/native-stack';
import * as React from 'react';
import { ColorSchemeName, Pressable } from 'react-native';
import { login } from '../api/Authentication';
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';
import ChatScreen from '../screens/TabOneScreen';
import TabTwoScreen from '../screens/TabTwoScreen';
import { RootStackParamList, RootTabParamList, RootTabScreenProps } from '../types';
import LinkingConfiguration from './LinkingConfiguration';
......@@ -40,22 +41,18 @@ function RootNavigator() {
const [isAuthenticated, setIsAutheticated] = React.useState(false)
const onLogin = () => {
setIsAutheticated(true)
const onLogin = async (username: String, password: String) => {
await login(username, password).then(() => setIsAutheticated(true)).catch(e => console.log(e))
}
const onLogout = () => {
setIsAutheticated(false)
}
return (
<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>}
{isAuthenticated ? <Stack.Screen name="Root" options={{ headerShown: false }}>{(props) => <BottomTabNavigator {...props} onLogout={onLogout} />}</Stack.Screen> :
<Stack.Screen name='Root'>{(props) => <LoginScreen {...props} onLogin={onLogin} />}</Stack.Screen>}
<Stack.Screen name="NotFound" component={NotFoundScreen} options={{ title: 'Oops!' }} />
......@@ -72,7 +69,7 @@ function RootNavigator() {
*/
const BottomTab = createBottomTabNavigator<RootTabParamList>();
function BottomTabNavigator({onLogout}) {
function BottomTabNavigator({ onLogout }) {
const colorScheme = useColorScheme();
return (
......@@ -83,7 +80,7 @@ function BottomTabNavigator({onLogout}) {
}}>
<BottomTab.Screen
name="TabOne"
component={TabOneScreen}
component={ChatScreen}
options={({ navigation }: RootTabScreenProps<'TabOne'>) => ({
title: 'Chat',
tabBarIcon: ({ color }) => <TabBarIcon name="code" color={color} />,
......
MobileApp/package.json
View file @ 7cca654f
......@@ -15,6 +15,7 @@
},
"dependencies": {
"@expo/vector-icons": "^12.0.0",
"@react-native-async-storage/async-storage": "~1.15.0",
"@react-navigation/bottom-tabs": "^6.0.5",
"@react-navigation/native": "^6.0.2",
"@react-navigation/native-stack": "^6.1.0",
......
MobileApp/screens/TabOneScreen.jsx 0 → 100644
View file @ 7cca654f
import React, { useEffect, useState } from 'react'
import { FlatList, StyleSheet, TextInput, SectionList } from 'react-native';
import { AudioRecorder } from '../components/AudioRecorder';
import _ from 'lodash'
import EditScreenInfo from '../components/EditScreenInfo';
import { Text, View } from '../components/Themed';
import { RootTabScreenProps } from '../types';
export default function ChatScreen({ navigation }) {
const currentTime = new Date()
const defaultChatData = [{ from: 'esh', to: "dri", message: 'msg1', time: currentTime.setMinutes(currentTime.getMinutes() - 1) },
{ from: 'esh', to: "dri", message: 'msg2', time: currentTime.setMinutes(currentTime.getMinutes() - 2) },
{ from: 'dri', to: "esh", message: 'msg3', time: currentTime.setMinutes(currentTime.getMinutes() - 3) },
{ from: 'esh', to: "dri", message: 'msg4', time: currentTime.setMinutes(currentTime.getMinutes() - 4) },
{ from: 'dri', to: "esh", message: 'msg5', time: currentTime.setMinutes(currentTime.getMinutes() - 5) },
{ from: 'esh', to: "dri", message: 'msg1', time: currentTime.setDate(currentTime.getDate() - 1) },
{ from: 'esh', to: "dri", message: 'msg2', time: currentTime.setDate(currentTime.getDate() - 2) },
{ from: 'dri', to: "esh", message: 'msg3', time: currentTime.setDate(currentTime.getDate() - 3) },
{ from: 'esh', to: "dri", message: 'msg4', time: currentTime.setDate(currentTime.getDate() - 4) },
{ from: 'dri', to: "esh", message: 'msg5', time: currentTime.setDate(currentTime.getDate() - 5) },
]
const [detectedText, setDetectedText] = useState("")
const [chatDetails, setChatDetails] = useState({ from: "esh", to: 'dri' })
const [chats, setChats] = useState(defaultChatData)
const ws = new WebSocket('ws://localhost:3000/ws')
const sections = [...new Set(chats.map(chat => new Date(chat.time).setHours(0, 0, 0, 0)))];
const sectionChats = sections.map(section => ({ title: section, data: chats.filter(chat => new Date(chat.time).setHours(0, 0, 0, 0) == section) }))
// useEffect(() => {
// startWebsocket()
// }, [])
const startWebsocket = () => {
ws.onopen = () => {
// on connecting, do nothing but log it to the console
console.log('connected')
}
ws.onmessage = evt => {
// listen to data sent from the websocket server
const message = JSON.parse(evt.data)
// setState({ dataFromServer: message })
console.log(message)
}
ws.onclose = () => {
console.log('disconnected')
// automatically try to reconnect on connection loss
}
}
return (
<View style={styles.container}>
{/* <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" /> */}
<View style={{ flex: 1, width: '100%' }}>
<View style={{ display: 'flex', flex: 0.9, justifyContent: 'space-between' }}>
{/* <View style={{ alignItems: 'center', justifyContent: 'center', flex: 1}}><Text style={{ fontSize: 50, color: 'white' }}>{detectedText}</Text>
</View> */}
<SectionList
inverted={true}
sections={sectionChats}
keyExtractor={(item, index) => item + index}
renderItem={({ item }) => {
// console.log({ item })
return (
<View style={{ margin: 5, padding: 5 }}><Text
style={[{ textAlign: chatDetails.from == item.from ? 'right' : 'left', backgroundColor: 'blue', borderRadius: 5, padding: 5 },
chatDetails.from == item.from ? { marginLeft: 'auto' } : { marginRight: 'auto' }
]}
key={item.time}>{item.message}</Text>
<Text style={{ textAlign: chatDetails.from == item.from ? 'right' : 'left', color: 'gray' }}>{new Date(item.time).toLocaleTimeString().slice(0, 5)}</Text></View>
)
}}
invertStickyHeaders
renderSectionFooter={({ section: { title } }) => {
var date = ""
if (new Date(title).toLocaleDateString() == new Date().toLocaleDateString()) {
date = "Today"
}
// else if (new Date(title).toLocaleDateString() == new Date().setDate(new Date().getDate() - 1).toLocaleDateString()){
// date = "Yesterday"
// }
else {
date = new Date(title).toLocaleDateString()
}
return (
<Text style={styles.header}>{date}</Text>
)
}}
/>
{/* <FlatList
inverted={true}
style={{ alignContent: 'flex-end' }}
data={chats}
keyExtractor={(item) => item.currentTime}
renderItem={({ item }) => {
console.log({ item })
return (
<View style={{ margin: 5, padding: 5 }}><Text
style={[{ textAlign: chatDetails.from == item.from ? 'right' : 'left', backgroundColor: 'blue', borderRadius: 5, padding: 5 },
chatDetails.from == item.from ? { marginLeft: 'auto' } : { marginRight: 'auto' }
]}
key={item.time}>{item.message}</Text>
<Text style={{ textAlign: chatDetails.from == item.from ? 'right' : 'left', color: 'gray' }}>{new Date(item.time).toLocaleTimeString().slice(0, 5)}</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>
);
}
const styles = StyleSheet.create({
container: {
flex: 1,
alignItems: 'center',
justifyContent: 'center',
},
title: {
fontSize: 20,
fontWeight: 'bold',
},
separator: {
marginVertical: 30,
height: 1,
width: '80%',
},
convoFooter: {
},
header: { textAlign: 'center', backgroundColor: 'gray', marginRight: 'auto', marginLeft: 'auto', padding: 3, borderRadius: 5 }
});
MobileApp/screens/TabOneScreen.tsx deleted 100644 → 0
View file @ 88281141
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={{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" /> */}
<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>
);
}
const styles = StyleSheet.create({
container: {
flex: 1,
alignItems: 'center',
justifyContent: 'center',
},
title: {
fontSize: 20,
fontWeight: 'bold',
},
separator: {
marginVertical: 30,
height: 1,
width: '80%',
},
convoFooter: {
}
});
MobileApp/screens/loginScreen.js
View file @ 7cca654f
import React from 'react'
import { StyleSheet, TextInput} from 'react-native';
import React, { useState } 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}) => {
const LoginForm = ({ onLogin }) => {
var passwordInput
const [username, setUsername] = useState('')
const [password, setPassword] = useState('')
return (
<>
<TextInput style={styles.input}
defaultValue={username}
onChange={(e) => {
console.log(username);
setUsername(e.target.value)}}
autoCapitalize="none"
// onSubmitEditing={() => passwordInput.focus()}
onSubmitEditing={() => passwordInput.focus()}
autoCorrect={false}
keyboardType='email-address'
returnKeyType="next"
......@@ -21,6 +28,8 @@ const LoginForm = ({onLogin}) => {
placeholderTextColor='rgba(225,225,225,0.7)' />
<TextInput style={styles.input}
defaultValue={password}
onChange={(e) => setPassword(e.target.value)}
returnKeyType="go"
ref={(input) => passwordInput = input}
placeholder='Password'
......@@ -29,8 +38,11 @@ const LoginForm = ({onLogin}) => {
<TouchableOpacity style={styles.buttonContainer}
// onPress={onButtonPress}
onPress={onLogin}
>
onPress={() => {
console.log({ username, password })
onLogin(username, password)
}}
>
<Text style={styles.buttonText}>LOGIN</Text>
</TouchableOpacity>
</>
......@@ -38,7 +50,7 @@ const LoginForm = ({onLogin}) => {
)
}
export const LoginScreen = ({onLogin}) => {
export const LoginScreen = ({ onLogin }) => {
return (
......@@ -48,7 +60,7 @@ export const LoginScreen = ({onLogin}) => {
</View>
<View style={styles.formContainer}>
<LoginForm onLogin={onLogin}/>
<LoginForm onLogin={onLogin} />
</View>
</View>
......
MobileApp/yarn.lock
View file @ 7cca654f
......@@ -1556,6 +1556,13 @@
"@nodelib/fs.scandir" "2.1.5"
fastq "^1.6.0"
"@react-native-async-storage/async-storage@~1.15.0":
version "1.15.17"
resolved "https://registry.yarnpkg.com/@react-native-async-storage/async-storage/-/async-storage-1.15.17.tgz#0dae263a52e476ffce871086f1fef5b8e44708eb"
integrity sha512-NQCFs47aFEch9kya/bqwdpvSdZaVRtzU7YB02L8VrmLSLpKgQH/1VwzFUBPcc1/JI1s3GU4yOLVrEbwxq+Fqcw==
dependencies:
merge-options "^3.0.4"
"@react-native-community/cli-debugger-ui@^5.0.1":
version "5.0.1"
resolved "https://registry.yarnpkg.com/@react-native-community/cli-debugger-ui/-/cli-debugger-ui-5.0.1.tgz#6b1f3367b8e5211e899983065ea2e72c1901d75f"
......@@ -4077,6 +4084,11 @@ is-plain-obj@^1.0.0:
resolved "https://registry.yarnpkg.com/is-plain-obj/-/is-plain-obj-1.1.0.tgz#71a50c8429dfca773c92a390a4a03b39fcd51d3e"
integrity sha1-caUMhCnfync8kqOQpKA7OfzVHT4=
is-plain-obj@^2.1.0:
version "2.1.0"
resolved "https://registry.yarnpkg.com/is-plain-obj/-/is-plain-obj-2.1.0.tgz#45e42e37fccf1f40da8e5f76ee21515840c09287"
integrity sha512-YWnfyRwxL/+SsrWYfOpUtz5b3YD+nyfkHvjbcanzk8zgyO4ASD67uVMRt8k5bM4lLMDnXfriRhOpemw+NfT1eA==
is-plain-object@^2.0.3, is-plain-object@^2.0.4:
version "2.0.4"
resolved "https://registry.yarnpkg.com/is-plain-object/-/is-plain-object-2.0.4.tgz#2c163b3fafb1b606d9d17928f05c2a1c38e07677"
......@@ -5032,6 +5044,13 @@ md5-file@^3.2.3:
dependencies:
buffer-alloc "^1.1.0"
merge-options@^3.0.4:
version "3.0.4"
resolved "https://registry.yarnpkg.com/merge-options/-/merge-options-3.0.4.tgz#84709c2aa2a4b24c1981f66c179fe5565cc6dbb7"
integrity sha512-2Sug1+knBjkaMsMgf1ctR1Ujx+Ayku4EdJN4Z+C2+JzoeF7A3OZ9KM2GY0CpQS51NR61LTurMJrRKPhSs3ZRTQ==
dependencies:
is-plain-obj "^2.1.0"
merge-stream@^2.0.0:
version "2.0.0"
resolved "https://registry.yarnpkg.com/merge-stream/-/merge-stream-2.0.0.tgz#52823629a14dd00c9770fb6ad47dc6310f2c1f60"
......
backend/backend/cms/model/predict.py
View file @ 7cca654f
import pickle
from keras.models import load_model
# import pickle
# from keras.models import load_model
import numpy as np
# import numpy as np
import IPython.display as ipd
# import IPython.display as ipd
import random
# import random
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
# from sklearn.model_selection import train_test_split
# from sklearn.preprocessing import LabelEncoder
def predict(samples):
model=load_model(r'./best_model_final.hdf5')
# 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')
# 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')
# # 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_)
# 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()
# # 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]
# 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)
# # 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)
# result = predictSamples(samples)
print("Text:",result)
# print("Text:",result)
return result
\ No newline at end of file
# return result
\ No newline at end of file
backend/backend/cms/model/train.py
View file @ 7cca654f
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
# 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 sklearn.preprocessing import LabelEncoder
from keras.utils import np_utils
# 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)
# 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)
# print(sample_rate)
samples = librosa.resample(samples, sample_rate, 8000)
ipd.Audio(samples, rate=8000)
# samples = librosa.resample(samples, sample_rate, 8000)
# ipd.Audio(samples, rate=8000)
labels = os.listdir(train_audio_path)
# 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))
# # 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()
# # 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')
# print('2')
labels = ["yes", "no", "up", "down", "left",
"right", "on", "off", "stop", "go"]
# 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()
# # 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')
# # # 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))
# 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))
# plt.hist(np.array(duration_of_recordings))
train_audio_path = r'./backend/data/train/train/audio/'
# train_audio_path = r'./backend/data/train/train/audio/'
all_wave = []
all_label = []
# all_wave = []
# all_label = []
f1 = open('all_label.txt', 'rb')
all_label = pickle.load(f1)
# f1 = open('all_label.txt', 'rb')
# all_label = pickle.load(f1)
f2 = open('all_waves_file.txt', 'rb')
all_wave = pickle.load(f2)
# 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)
# 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')
# # print('3')
all_labels_file = open('all_label.txt', 'wb+')
pickle.dump(file=all_labels_file, obj=all_label)
all_labels_file.close()
# all_labels_file = open('all_label.txt', 'wb+')
# pickle.dump(file=all_labels_file, obj=all_label)
# all_labels_file.close()
return False
# return False
all_waves_file = open('all_waves_file.txt', 'wb+')
pickle.dump(file=all_waves_file, obj=all_wave)
all_waves_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')
# print('Done: creating labels and waves files')
return False
le = LabelEncoder()
y = le.fit_transform(all_label)
classes = list(le.classes_)
# return False
# le = LabelEncoder()
# y = le.fit_transform(all_label)
# classes = list(le.classes_)
print('4')
# print('4')
y = np_utils.to_categorical(y, num_classes=len(labels))
# y = np_utils.to_categorical(y, num_classes=len(labels))
all_wave = np.array(all_wave).reshape(-1, 8000, 1)
# 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)
# 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()
# 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))
# 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)
# # 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)
# # 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)
# # 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)
# # 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)
# # Flatten layer
# conv = Flatten()(conv)
# Dense Layer 1
conv = Dense(256, activation='relu')(conv)
conv = Dropout(0.3)(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)
# # Dense Layer 2
# conv = Dense(128, activation='relu')(conv)
# conv = Dropout(0.3)(conv)
outputs = Dense(len(labels), activation='softmax')(conv)
# outputs = Dense(len(labels), activation='softmax')(conv)
model = Model(inputs, outputs)
model.summary()
# model = Model(inputs, outputs)
# model.summary()
model.compile(loss='categorical_crossentropy',
optimizer='adam', metrics=['accuracy'])
# 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')
# 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))
# 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.plot(history.history['loss'], label='train')
# # pyplot.plot(history.history['val_loss'], label='test')
# # pyplot.legend()
# pyplot.show()
# # pyplot.show()
return history
# return history
backend/backend/cms/models.py
View file @ 7cca654f
from argparse import ONE_OR_MORE
from operator import truediv
from sqlite3 import Timestamp
from django.db import models
from django.contrib.auth.models import User
# 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()
class Conversation(models.Model):
id = models.AutoField(primary_key=True)
from_user = models.ForeignKey(
User, related_name='from_chat', on_delete=models.PROTECT)
to_user = models.ForeignKey(
User, related_name='to_chat', on_delete=models.PROTECT)
timestamp = models.DateTimeField(auto_now=True)
class Chat(models.Model):
id = models.AutoField(primary_key=True)
conversation = models.ForeignKey(Conversation, on_delete=models.PROTECT)
from_user = models.ForeignKey(
User, related_name='from_convo', on_delete=models.PROTECT)
to_user = models.ForeignKey(
User, related_name='to_convo', on_delete=models.PROTECT)
message = models.TextField()
timestamp = models.DateTimeField(auto_now=True)
backend/backend/cms/serializers.py
View file @ 7cca654f
from django.contrib.auth.models import User, Group
from .models import MlModel
from .models import Chat, Conversation, MlModel
from rest_framework import serializers
from rest_framework_simplejwt.serializers import TokenObtainPairSerializer
from django.forms.models import model_to_dict
class MyTokenObtainPairSerializer(TokenObtainPairSerializer):
@classmethod
def get_token(cls, user):
token = super(MyTokenObtainPairSerializer, cls).get_token(user)
# Add custom claims
return token
def validate(self, attrs):
data = super().validate(attrs)
refresh = self.get_token(self.user)
data['refresh'] = str(refresh)
data['access'] = str(refresh.access_token)
# Add extra responses here
user = self.user
print(user.id)
user_obj = User.objects.filter(id=user.id).values('username', 'email', 'id').first()
print(user_obj)
data['user'] = user_obj
return data
class UserSerializer(serializers.HyperlinkedModelSerializer):
class Meta:
......@@ -17,6 +42,20 @@ class GroupSerializer(serializers.HyperlinkedModelSerializer):
class MlModelSerializer(serializers.ModelSerializer):
class Meta:
model = MlModel
fields = (
'__all__'
)
class ChatSerialier(serializers.ModelSerializer):
class Meta:
model = Chat
fields = (
'__all__'
)
class ConversationSerializer(serializers.Serializer):
class Meta:
model = Conversation
fields = (
'__all__'
)
\ No newline at end of file
backend/backend/cms/views.py
View file @ 7cca654f
......@@ -2,7 +2,8 @@ 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 MyTokenObtainPairSerializer
from backend.cms.serializers import MlModelSerializer, ChatSerialier, ConversationSerializer
from backend.cms.serializers import UserSerializer, GroupSerializer
from rest_framework.decorators import action
from rest_framework.response import Response
......@@ -16,16 +17,23 @@ import librosa
from django.core.files.storage import FileSystemStorage
from .models import MlModel
from .models import Chat, Conversation, MlModel
from rest_framework_simplejwt.views import TokenObtainPairView
from .model.train import train
# from .model.train import train
from .model.predict import predict
# from .model.predict import predict
from pydub import AudioSegment
import numpy as np
class ObtainTokenPairWithUserView(TokenObtainPairView):
permission_classes = (permissions.AllowAny,)
serializer_class = MyTokenObtainPairSerializer
class UserViewSet(viewsets.ModelViewSet):
"""
API endpoint that allows users to be viewed or edited.
......@@ -39,7 +47,7 @@ class UserViewSet(viewsets.ModelViewSet):
print('Function ran')
results = train()
results = {} # train() CHANGE HERE
print(results)
return Response({'success': True})
......@@ -130,10 +138,17 @@ class MlModelViewSet(viewsets.ViewSet):
results = predict(samples)
results = {} #predict(samples) CHANGE HERE
print(results)
return Response({'success': True, 'result': results})
class ChatViewSet(viewsets.ViewSet):
queryset = Chat.objects.all().order_by('-timestamp')
serializer_class = ChatSerialier
permission_classes = [permissions.IsAuthenticated]
class ConversationViewSet(viewsets.ViewSet):
queryset = Conversation.objects.all
serializer_class = ConversationSerializer
permission_classes = [permissions.IsAuthenticated]
\ No newline at end of file
backend/backend/settings.py
View file @ 7cca654f
......@@ -40,7 +40,8 @@ INSTALLED_APPS = [
'django.contrib.messages',
'django.contrib.staticfiles',
'rest_framework',
'backend.cms'
'backend.cms',
'corsheaders',
]
MIDDLEWARE = [
......@@ -51,6 +52,7 @@ MIDDLEWARE = [
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
'corsheaders.middleware.CorsMiddleware',
]
ROOT_URLCONF = 'backend.urls'
......@@ -155,4 +157,6 @@ SIMPLE_JWT = {
'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
}
CORS_ORIGIN_ALLOW_ALL = True
\ No newline at end of file
backend/backend/urls.py
View file @ 7cca654f
from django.urls import include, path, re_path
from rest_framework import routers
from backend.cms.views import ObtainTokenPairWithUserView
from backend.cms import views
from rest_framework_simplejwt.views import TokenObtainPairView, TokenRefreshView
from rest_framework.authtoken.views import obtain_auth_token
router = routers.DefaultRouter()
router.register(r'users', views.UserViewSet)
......@@ -13,6 +15,10 @@ router.register(r'mlmodels', views.MlModelViewSet)
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())
re_path(r'^api/auth/token/obtain/$', ObtainTokenPairWithUserView.as_view()),
re_path(r'^api/auth/token/refresh/$', TokenRefreshView.as_view()),
# url(r'^auth/login/$',
# obtain_auth_token,
# name='auth_user_login'),
]
\ No newline at end of file
backend/db.sqlite3
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