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Commit 16228ea8 authored by Malsha Rathnasiri's avatar Malsha Rathnasiri
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minor changes

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MobileApp/api/api.js
View file @ 16228ea8
import { BACKEND_URL } from "./constants"
import AsyncStorage from '@react-native-async-storage/async-storage';
import { useNavigation } from "@react-navigation/native";
import axios from "axios";
//"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJ0b2tlbl90eXBlIjoiYWNjZXNzIiwiZXhwIjoxNjYxMzU2NDA5LCJpYXQiOjE2NjAxNDY4MDksImp0aSI6ImFhMTljMmY2ZDNkMzRiNDdhZmZmM2FjMzVjNzI4MWJhIiwidXNlcl9pZCI6MX0.IVzibo_Rf2xzoT1J5o1L3zwu3mco6ODcNPC-7imu3Lo"
......@@ -8,21 +11,41 @@ import { useNavigation } from "@react-navigation/native";
const getHeaders = async () => {
var token = await AsyncStorage.getItem('access_token')
return new Headers({ authorization: `Bearer ${token}`, 'Content-Type': 'application/json' })
return {
Authorization: `JWT ${token}`, 'Content-Type': 'application/json',
}
}
export const create = async (resource, values) => {
const headers = await getHeaders()
console.log({headers})
const headers = getHeaders()
console.log({ headers }, 'create')
try {
return fetch(`${BACKEND_URL}/${resource}/`, { method: 'POST', body: JSON.stringify(values), headers })
// return Axios.post(`${BACKEND_URL}/${resource}/`, values)
}
catch (e) {
console.log(e)
}
}
export const getList = async (resource) => {
export const getList = async (resource, params) => {
const url = new URL(`${BACKEND_URL}/${resource}/`)
if (params) {
Object.keys(params).map(key => {
url.searchParams.append(key, params[key])
})
}
const headers = await getHeaders()
console.log(headers, 'getList')
return fetch(url, { method: 'GET', headers: headers })
return axios.get(url.toString(), null, { headers })
}
export const getOne = async (resource, id) => {
const url = new URL(`${BACKEND_URL}/${resource}/${id}/`)
const headers = await getHeaders()
return fetch(`${BACKEND_URL}/${resource}/`, { method: 'GET', headers: headers })
console.log(headers, 'getONe')
return fetch(url, { method: "GET", headers: headers })
}
\ No newline at end of file
MobileApp/api/constants.js
View file @ 16228ea8
// export const BACKEND_URL = "http://192.168.8.103:8000"
export const BACKEND_ADDRESS = "dcf1-2402-d000-a500-101a-cd9c-120b-91b8-7774.in.ngrok.io"
import { Platform } from 'react-native'
export const BACKEND_ADDRESS = Platform.OS == 'web' ? "127.0.0.1:8000" : "93e8-2401-dd00-10-20-e170-748c-b618-e8f5.in.ngrok.io"
export const BACKEND_URL = `http://${BACKEND_ADDRESS}`
MobileApp/screens/TabOneScreen.jsx
View file @ 16228ea8
......@@ -6,7 +6,7 @@ import _ from 'lodash'
import EditScreenInfo from '../components/EditScreenInfo';
// import { Text, View } from '../components/Themed';
import { RootTabScreenProps } from '../types';
import { create, getList } from '../api/api';
import { create, getList, getOne } from '../api/api';
import { BACKEND_ADDRESS } from '../api/constants';
import Ionicons from '@expo/vector-icons/Ionicons';
import { CONVO_DEFAULT_ICON_COLOR, styles } from '../util/styles';
......@@ -37,7 +37,8 @@ export default function ChatScreen({ navigation }) {
const [detectedText, setDetectedText] = useState("")
const [playinId, setPlayingId] = useState(3)
const [chatDetails, setChatDetails] = useState({ from_user: 1, to_user: 2, conversation_id: 1 })
const [chatDetails, setChatDetails] = useState()
const [chats, setChats] = useState([])
const [input, setInput] = useState('test')
const [loading, setLoading] = useState(true)
......@@ -59,12 +60,15 @@ export default function ChatScreen({ navigation }) {
console.log({ chats })
}
useEffect(() => { if (chatDetails) { loadChats(); setLoading(false) } }, [chatDetails])
// const ws = new WebSocket(`ws://${BACKEND_ADDRESS}/chatSocket/`)
useEffect(() => {
loadChats()
loadChatDetails()
// startWebsocket()
// loadSampleChats()
setLoading(false)
}, [])
// const startWebsocket = () => {
......@@ -87,12 +91,22 @@ export default function ChatScreen({ navigation }) {
// }
const loadChatDetails = async () => {
await getOne('conversations', 1).then(res => {
return res.json()
}).then(res => {
console.log(res)
setChatDetails(res)
})
}
const loadChats = async () => {
await getList('chats').then(res => {
return res.json()
}).then(res => {
// console.log(res)
const chats = res.results
console.log("load chats")
const chats = res.results || []
const sections = [...new Set(chats.map(chat => new Date(chat.timestamp).setHours(0, 0, 0, 0)))];
const sectionChats = sections.map(section => ({ title: section, data: chats.filter(chat => new Date(chat.timestamp).setHours(0, 0, 0, 0) == section) }))
setChats(sectionChats)
......@@ -103,16 +117,16 @@ export default function ChatScreen({ navigation }) {
const onSendPress = () => {
try {
create('chats', { message: input, from_user: chatDetails.from_user, to_user: chatDetails.to_user, conversation: chatDetails.conversation_id }).then(response => {
// console.log(response)
})
setLoading(true)
setInput('')
loadChats()
}
catch(e){
Toast.show({title: 'Error sending message. try again!', ...ERROR_TOAST_PROPS})
}
create('chats', { message: input, from_user: chatDetails.from_user, to_user: chatDetails.to_user, conversation: chatDetails.conversation_id }).then(response => {
// console.log(response)
})
setLoading(true)
setInput('')
loadChats()
}
catch (e) {
Toast.show({ title: 'Error sending message. try again!', ...ERROR_TOAST_PROPS })
}
}
return (
......@@ -130,12 +144,18 @@ export default function ChatScreen({ navigation }) {
{loading ? <ActivityIndicator /> :
<SectionList
refreshing={loading}
onRefresh={() => loadChats()}
inverted={true}
onRefresh={() => {
// loadChatDetails() //remove
loadChats()
}}
inverted
sections={chats}
keyExtractor={(item, index) => item + index}
renderItem={({ item }) => {
// console.log({ item })
const timeString = new Date(item.timestamp).toLocaleTimeString()
const time = timeString.slice(-11, -6) + " " + timeString.slice(-2)
return (
<View style={{ margin: 5, padding: 5 }}><Text
style={[{ textAlign: chatDetails.from_user == item.from_user ? 'right' : 'left', backgroundColor: chatDetails.from_user == item.from_user ? 'lightgray' : '#FFDE03', borderRadius: 5, padding: 5 },
......@@ -143,8 +163,8 @@ export default function ChatScreen({ navigation }) {
]}
key={item.timestamp}>{item.message}</Text>
<View style={{ flex: 1, flexDirection: chatDetails.from_user == item.from_user ? 'row-reverse' : 'row', textAlign: chatDetails.from_user == item.from_user ? 'right' : 'left' }}>
<Text style={{ textAlign: chatDetails.from_user == item.from_user ? 'right' : 'left', color: 'gray', fontSize: 11 }}>{new Date(item.timestamp).toLocaleTimeString()}</Text>
{item.is_detected && <Ionicons name="mic" size={15} color={CONVO_DEFAULT_ICON_COLOR}/>}
<Text style={{ textAlign: chatDetails.from_user == item.from_user ? 'right' : 'left', color: 'gray', fontSize: 11 }}>{time}</Text>
{item.is_detected && <Ionicons name="mic" size={15} color={CONVO_DEFAULT_ICON_COLOR} />}
{/* {chatDetails.to_user == item.from_user && item.id != playinId && <Ionicons name="play" size={15} style={styles.chatIcon} />}
{chatDetails.to_user == item.from_user && item.id == playinId && <Ionicons name="pause" size={15} style={styles.chatIcon} />} */}
{chatDetails.to_user == item.from_user && <PlayMessage message={item.message} />}
......@@ -200,11 +220,11 @@ export default function ChatScreen({ navigation }) {
</View>
<View style={{ flex: 0.075, padding:0, backgroundColor: 'white'}}>
<View style={{ flex: 0.075, padding: 0, backgroundColor: 'white' }}>
<View style={{ flexDirection: 'row', display: 'flex', height: '100%' }}>
<View style={{ flex: 0.8, height: '100%', flexDirection: 'column-reverse' }}>
<TextInput
style={{ borderWidth: 2, borderColor: 'gray', color: 'black', marginHorizontal: 5,paddingHorizontal: 10, borderRadius: 5 }}
style={{ borderWidth: 2, borderColor: 'gray', color: 'black', marginHorizontal: 5, paddingHorizontal: 10, borderRadius: 5 }}
defaultValue={input}
onChange={(e) => setInput(e.target.value)}></TextInput>
</View>
......
backend/backend/cms/model/predict.py
View file @ 16228ea8
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 @ 16228ea8
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)
print(sample_rate)
# ipd.Audio(samples, rate=sample_rate)
samples = librosa.resample(samples, sample_rate, 8000)
ipd.Audio(samples, rate=8000)
# print(sample_rate)
labels = os.listdir(train_audio_path)
# samples = librosa.resample(samples, sample_rate, 8000)
# ipd.Audio(samples, rate=8000)
# 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))
# labels = os.listdir(train_audio_path)
# 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()
# # 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))
print('2')
# # 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()
labels = ["yes", "no", "up", "down", "left",
"right", "on", "off", "stop", "go"]
# print('2')
# labels_file = open('./labels_file.bin', 'wb+')
# pickle.dump(obj=labels, file=labels_file)
# labels_file.close()
# labels = ["yes", "no", "up", "down", "left",
# "right", "on", "off", "stop", "go"]
# # file = open('./labels_file.bin', 'rb')
# # dict = pickle.load(file)
# # print('loaded')
# # print(dict)
# # print('fdnasf')
# # labels_file = open('./labels_file.bin', 'wb+')
# # pickle.dump(obj=labels, file=labels_file)
# # labels_file.close()
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))
# # # file = open('./labels_file.bin', 'rb')
# # # dict = pickle.load(file)
# # # print('loaded')
# # # print(dict)
# # # print('fdnasf')
plt.hist(np.array(duration_of_recordings))
# 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))
train_audio_path = r'./backend/data/train/train/audio/'
# plt.hist(np.array(duration_of_recordings))
all_wave = []
all_label = []
# train_audio_path = r'./backend/data/train/train/audio/'
f1 = open('all_label.txt', 'rb')
all_label = pickle.load(f1)
# all_wave = []
# all_label = []
f2 = open('all_waves_file.txt', 'rb')
all_wave = pickle.load(f2)
# f1 = open('all_label.txt', 'rb')
# all_label = pickle.load(f1)
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)
# f2 = open('all_waves_file.txt', 'rb')
# all_wave = pickle.load(f2)
# print('3')
# 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)
all_labels_file = open('all_label.txt', 'wb+')
pickle.dump(file=all_labels_file, obj=all_label)
all_labels_file.close()
# # print('3')
return False
# 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')
# all_waves_file = open('all_waves_file.txt', 'wb+')
# pickle.dump(file=all_waves_file, obj=all_wave)
# all_waves_file.close()
return False
le = LabelEncoder()
y = le.fit_transform(all_label)
classes = list(le.classes_)
# print('Done: creating labels and waves files')
print('4')
y = np_utils.to_categorical(y, num_classes=len(labels))
# le = LabelEncoder()
# y = le.fit_transform(all_label)
# classes = list(le.classes_)
all_wave = np.array(all_wave).reshape(-1, 8000, 1)
# print('4')
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)
# y = np_utils.to_categorical(y, num_classes=len(labels))
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()
# all_wave = np.array(all_wave).reshape(-1, 8000, 1)
inputs = Input(shape=(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)
# First Conv1D layer
conv = Conv1D(8, 13, padding='valid', activation='relu', strides=1)(inputs)
conv = MaxPooling1D(3)(conv)
conv = Dropout(0.3)(conv)
# 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()
# Second Conv1D layer
conv = Conv1D(16, 11, padding='valid', activation='relu', strides=1)(conv)
conv = MaxPooling1D(3)(conv)
conv = Dropout(0.3)(conv)
# inputs = Input(shape=(8000, 1))
# Third Conv1D layer
conv = Conv1D(32, 9, padding='valid', activation='relu', strides=1)(conv)
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)
# Fourth Conv1D layer
conv = Conv1D(64, 7, 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)
# Flatten layer
conv = Flatten()(conv)
# # Third Conv1D layer
# conv = Conv1D(32, 9, padding='valid', activation='relu', strides=1)(conv)
# conv = MaxPooling1D(3)(conv)
# conv = Dropout(0.3)(conv)
# Dense Layer 1
conv = Dense(256, activation='relu')(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)
# Dense Layer 2
conv = Dense(128, activation='relu')(conv)
conv = Dropout(0.3)(conv)
# # Flatten layer
# conv = Flatten()(conv)
outputs = Dense(len(labels), activation='softmax')(conv)
# # Dense Layer 1
# conv = Dense(256, activation='relu')(conv)
# conv = Dropout(0.3)(conv)
model = Model(inputs, outputs)
model.summary()
# # Dense Layer 2
# conv = Dense(128, activation='relu')(conv)
# conv = Dropout(0.3)(conv)
model.compile(loss='categorical_crossentropy',
optimizer='adam', metrics=['accuracy'])
# outputs = Dense(len(labels), activation='softmax')(conv)
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')
# model = Model(inputs, outputs)
# model.summary()
history = model.fit(x_tr, y_tr, epochs=100, callbacks=[
es, mc], batch_size=32, validation_data=(x_val, y_val))
# model.compile(loss='categorical_crossentropy',
# optimizer='adam', metrics=['accuracy'])
# pyplot.plot(history.history['loss'], label='train')
# pyplot.plot(history.history['val_loss'], label='test')
# pyplot.legend()
# 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')
# pyplot.show()
# history = model.fit(x_tr, y_tr, epochs=100, callbacks=[
# es, mc], batch_size=32, validation_data=(x_val, y_val))
return history
# # pyplot.plot(history.history['loss'], label='train')
# # pyplot.plot(history.history['val_loss'], label='test')
# # pyplot.legend()
# # pyplot.show()
# return history
backend/backend/cms/serializers.py
View file @ 16228ea8
......@@ -46,7 +46,7 @@ class MlModelSerializer(serializers.ModelSerializer):
'__all__'
)
class ChatSerialier(serializers.ModelSerializer):
class ChatSerializer(serializers.ModelSerializer):
class Meta:
model = Chat
fields = (
......
backend/backend/cms/views.py
View file @ 16228ea8
from http.client import HTTPResponse
from lib2to3.pytree import convert
from pyexpat import model
from django.contrib.auth.models import User, Group
from rest_framework import viewsets
from rest_framework import permissions
from backend.cms.serializers import MyTokenObtainPairSerializer
from backend.cms.serializers import MlModelSerializer, ChatSerialier, ConversationSerializer
from backend.cms.serializers import MlModelSerializer, ChatSerializer, ConversationSerializer
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
from datetime import datetime, timedelta
import librosa
from django.db.models import Q
from django.core.files.storage import FileSystemStorage
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
......@@ -73,7 +71,7 @@ class MlModelViewSet(viewsets.ViewSet):
parser_classes = [MultiPartParser]
@action(detail=False)
def runAction(*args, **kwargs):
def addChats(*args, **kwargs):
admin = User.objects.get(username='admin')
user2 = User.objects.get(username='user2')
......@@ -172,16 +170,47 @@ class MlModelViewSet(viewsets.ViewSet):
# track.file.name = mp3_filename
# track.save()
results = predict(samples)
results = {} # predict(samples)
print(results)
return Response({'success': True, 'result': results})
class ChatViewSet(viewsets.ModelViewSet):
queryset = Chat.objects.all().order_by('-timestamp')
serializer_class = ChatSerialier
serializer_class = ChatSerializer
permission_classes = [permissions.IsAuthenticated]
@action(methods='POST', detail=True)
def getChats(self, request, *args, **kwargs):
chats = Chat.objects.filter(Q(from_user__user_id=request.user.id) | Q(
to_user__user_id=request.user.id)).order_by('-timestamp').values()
return Response({chats})
def list(self, request, pk=None):
if pk == None:
chats = Chat.objects.filter(Q(from_user_id=request.user.id) | Q(
to_user_id=request.user.id))
else:
chats = Chat.objects.get(id=pk)
page = self.paginate_queryset(chats)
if page is not None:
serializer = self.get_serializer(page, many=True)
return self.get_paginated_response(serializer.data)
serializer = self.get_serializer(page, many=True)
result_set = serializer.data
return Response(result_set)
def get_result_set(self, chats):
result_set = ChatSerializer(chats, many=True).data
return result_set
class ConversationViewSet(viewsets.ModelViewSet):
queryset = Conversation.objects.all().order_by('id')
......
backend/backend/settings.py
View file @ 16228ea8
......@@ -46,14 +46,16 @@ INSTALLED_APPS = [
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'corsheaders.middleware.CorsMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
'corsheaders.middleware.CorsMiddleware',
]
ROOT_URLCONF = 'backend.urls'
......@@ -92,18 +94,18 @@ DATABASES = {
# 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',
},
# {
# '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',
# },
]
......@@ -132,10 +134,10 @@ DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'
REST_FRAMEWORK = {
'DEFAULT_PAGINATION_CLASS': 'rest_framework.pagination.PageNumberPagination',
'PAGE_SIZE': 10,
'DEFAULT_AUTHENTICATION_CLASSES': (
'DEFAULT_AUTHENTICATION_CLASSES': [
'rest_framework_simplejwt.authentication.JWTAuthentication',
'rest_framework.authentication.SessionAuthentication',
)
]
}
SIMPLE_JWT = {
......@@ -148,7 +150,7 @@ SIMPLE_JWT = {
'SIGNING_KEY': SECRET_KEY,
'VERIFYING_KEY': None,
'AUTH_HEADER_TYPES': ('Bearer',),
'AUTH_HEADER_TYPES': ('JWT',),
'USER_ID_FIELD': 'id',
'USER_ID_CLAIM': 'user_id',
......@@ -156,10 +158,11 @@ SIMPLE_JWT = {
'TOKEN_TYPE_CLAIM': 'token_type',
'SLIDING_TOKEN_REFRESH_EXP_CLAIM': 'refresh_exp',
'SLIDING_TOKEN_LIFETIME': datetime.timedelta(minutes=5),
'SLIDING_TOKEN_LIFETIME': datetime.timedelta(minutes=60),
'SLIDING_TOKEN_REFRESH_LIFETIME': datetime.timedelta(days=1),
}
CORS_ORIGIN_ALLOW_ALL = True
CORS_ALLOW_ALL_ORIGINS = True # If this is used then `CORS_ALLOWED_ORIGINS` will not have any effect
CORS_ALLOW_CREDENTIALS = True
ASGI_APPLICATION = "backend.asgi.application"
\ No newline at end of file
backend/backend/urls.py
View file @ 16228ea8
......@@ -5,6 +5,8 @@ from backend.cms import views
from rest_framework_simplejwt.views import TokenObtainPairView, TokenRefreshView
from rest_framework.authtoken.views import obtain_auth_token
from django.contrib import admin
router = routers.DefaultRouter()
router.register(r'users', views.UserViewSet)
router.register(r'groups', views.GroupViewSet)
......@@ -16,6 +18,7 @@ router.register(r'conversations', views.ConversationViewSet)
# Additionally, we include login URLs for the browsable API.
urlpatterns = [
path('', include(router.urls)),
path('admin/', admin.site.urls),
path('api-auth/', include('rest_framework.urls', namespace='rest_framework')),
re_path(r'^api/auth/token/obtain/$', ObtainTokenPairWithUserView.as_view()),
re_path(r'^api/auth/token/refresh/$', TokenRefreshView.as_view()),
......
backend/db.sqlite3
View file @ 16228ea8
No preview for this file type
backend/output.m4a
View file @ 16228ea8
No preview for this file type
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