minor changes

MobileApp/api/api.js

 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

 // 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

@@ -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)
@@ -110,8 +124,8 @@ export default function ChatScreen({ navigation }) {
       setInput('')
       loadChats()
     }
-  catch(e){
-    Toast.show({title: 'Error sending message. try again!', ...ERROR_TOAST_PROPS})
+    catch (e) {
+      Toast.show({ title: 'Error sending message. try again!', ...ERROR_TOAST_PROPS })
     }
   }
 
@@ -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

-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
+#     return result
\ No newline at end of file

backend/backend/cms/model/train.py

-import pickle
-from matplotlib import pyplot
-import os
-import librosa
-import IPython.display as ipd
-import matplotlib.pyplot as plt
-import numpy as np
-from scipy.io import wavfile
-import warnings
+# 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']
 
-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)
+# def train():
 
-    samples = librosa.resample(samples, sample_rate, 8000)
-    ipd.Audio(samples, rate=8000)
+#     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)
 
-    labels = os.listdir(train_audio_path)
+#     ipd.Audio(samples, rate=sample_rate)
 
-    # 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(sample_rate)
 
-    # 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()
+#     samples = librosa.resample(samples, sample_rate, 8000)
+#     ipd.Audio(samples, rate=8000)
 
-    print('2')
+#     labels = os.listdir(train_audio_path)
 
-    labels = ["yes", "no", "up", "down", "left",
-              "right", "on", "off", "stop", "go"]
+#     # 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_file = open('./labels_file.bin', 'wb+')
-    # pickle.dump(obj=labels, file=labels_file)
-    # labels_file.close()
+#     # 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()
 
-    # # file = open('./labels_file.bin', 'rb')
-    # # dict = pickle.load(file)
-    # # print('loaded')
-    # # print(dict)
-    # # print('fdnasf')
+#     print('2')
 
-    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))
+#     labels = ["yes", "no", "up", "down", "left",
+#               "right", "on", "off", "stop", "go"]
 
-    plt.hist(np.array(duration_of_recordings))
+#     # labels_file = open('./labels_file.bin', 'wb+')
+#     # pickle.dump(obj=labels, file=labels_file)
+#     # labels_file.close()
 
-    train_audio_path = r'./backend/data/train/train/audio/'
+#     # # file = open('./labels_file.bin', 'rb')
+#     # # dict = pickle.load(file)
+#     # # print('loaded')
+#     # # print(dict)
+#     # # print('fdnasf')
 
-    all_wave = []
-    all_label = []
+#     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))
 
-    f1 = open('all_label.txt', 'rb')
-    all_label = pickle.load(f1)
+#     plt.hist(np.array(duration_of_recordings))
 
-    f2 = open('all_waves_file.txt', 'rb')
-    all_wave = pickle.load(f2)
+#     train_audio_path = r'./backend/data/train/train/audio/'
 
-    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_wave = []
+#     all_label = []
 
-        # print('3')
+#     f1 = open('all_label.txt', 'rb')
+#     all_label = pickle.load(f1)
 
-        all_labels_file = open('all_label.txt', 'wb+')
-        pickle.dump(file=all_labels_file, obj=all_label)
-        all_labels_file.close()
+#     f2 = open('all_waves_file.txt', 'rb')
+#     all_wave = pickle.load(f2)
 
-        return False
+#     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_waves_file = open('all_waves_file.txt', 'wb+')
-        pickle.dump(file=all_waves_file, obj=all_wave)
-        all_waves_file.close()
+#         # print('3')
 
-        print('Done: creating labels and waves files')
+#         all_labels_file = open('all_label.txt', 'wb+')
+#         pickle.dump(file=all_labels_file, obj=all_label)
+#         all_labels_file.close()
 
-    return False
-    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_waves_file = open('all_waves_file.txt', 'wb+')
+#         pickle.dump(file=all_waves_file, obj=all_wave)
+#         all_waves_file.close()
 
-    all_wave = np.array(all_wave).reshape(-1, 8000, 1)
+#         print('Done: creating labels and waves files')
 
-    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()
+#     le = LabelEncoder()
+#     y = le.fit_transform(all_label)
+#     classes = list(le.classes_)
 
-    inputs = Input(shape=(8000, 1))
+#     print('4')
 
-    # First Conv1D layer
-    conv = Conv1D(8, 13, padding='valid', activation='relu', strides=1)(inputs)
-    conv = MaxPooling1D(3)(conv)
-    conv = Dropout(0.3)(conv)
+#     y = np_utils.to_categorical(y, num_classes=len(labels))
 
-    # Second Conv1D layer
-    conv = Conv1D(16, 11, padding='valid', activation='relu', strides=1)(conv)
-    conv = MaxPooling1D(3)(conv)
-    conv = Dropout(0.3)(conv)
+#     all_wave = np.array(all_wave).reshape(-1, 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)
+#     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)
 
-    # Fourth Conv1D layer
-    conv = Conv1D(64, 7, padding='valid', activation='relu', strides=1)(conv)
-    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()
 
-    # Flatten layer
-    conv = Flatten()(conv)
+#     inputs = Input(shape=(8000, 1))
 
-    # Dense Layer 1
-    conv = Dense(256, activation='relu')(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)
 
-    # Dense Layer 2
-    conv = Dense(128, activation='relu')(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)
 
-    outputs = Dense(len(labels), activation='softmax')(conv)
+#     # Third Conv1D layer
+#     conv = Conv1D(32, 9, padding='valid', activation='relu', strides=1)(conv)
+#     conv = MaxPooling1D(3)(conv)
+#     conv = Dropout(0.3)(conv)
 
-    model = Model(inputs, outputs)
-    model.summary()
+#     # Fourth Conv1D layer
+#     conv = Conv1D(64, 7, padding='valid', activation='relu', strides=1)(conv)
+#     conv = MaxPooling1D(3)(conv)
+#     conv = Dropout(0.3)(conv)
 
-    model.compile(loss='categorical_crossentropy',
-                  optimizer='adam', metrics=['accuracy'])
+#     # Flatten layer
+#     conv = Flatten()(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')
+#     # Dense Layer 1
+#     conv = Dense(256, activation='relu')(conv)
+#     conv = Dropout(0.3)(conv)
 
-    history = model.fit(x_tr, y_tr, epochs=100, callbacks=[
-                        es, mc], batch_size=32, validation_data=(x_val, y_val))
+#     # Dense Layer 2
+#     conv = Dense(128, activation='relu')(conv)
+#     conv = Dropout(0.3)(conv)
 
-    # pyplot.plot(history.history['loss'], label='train')
-    # pyplot.plot(history.history['val_loss'], label='test')
-    # pyplot.legend()
+#     outputs = Dense(len(labels), activation='softmax')(conv)
 
-    # pyplot.show()
+#     model = Model(inputs, outputs)
+#     model.summary()
 
-    return history
+#     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/serializers.py

@@ -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

-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

@@ -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

@@ -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()),