Skip to content

2
2023-029
Commit b677141b authored by Sumudu-Himasha-Ranaweera's avatar Sumudu-Himasha-Ranaweera
Browse files
Options

fix:update

parent ff3f7356
Expand all Hide whitespace changes
Inline Side-by-side
Showing with 34561 additions and 0 deletions
Project/Backend/ML_Models/Emotion_Detection_Model/emotion_model.json 0 → 100644
View file @ b677141b
{"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 48, 48, 1], "dtype": "float32", "sparse": false, "ragged": false, "name": "conv2d_input"}}, {"class_name": "Conv2D", "config": {"name": "conv2d", "trainable": true, "batch_input_shape": [null, 48, 48, 1], "dtype": "float32", "filters": 32, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_1", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}}, {"class_name": "Dropout", "config": {"name": "dropout", "trainable": true, "dtype": "float32", "rate": 0.25, "noise_shape": null, "seed": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_2", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}}, {"class_name": "Conv2D", "config": {"name": "conv2d_3", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_2", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "dtype": "float32", "rate": 0.25, "noise_shape": null, "seed": null}}, {"class_name": "Flatten", "config": {"name": "flatten", "trainable": true, "dtype": "float32", "data_format": "channels_last"}}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 1024, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dropout", "config": {"name": "dropout_2", "trainable": true, "dtype": "float32", "rate": 0.5, "noise_shape": null, "seed": null}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 7, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "keras_version": "2.4.0", "backend": "tensorflow"}
\ No newline at end of file
Project/Backend/Server_Python/controllers/audio_detect_controler.py 0 → 100644
View file @ b677141b
from fastapi import APIRouter,FastAPI, UploadFile ,File,HTTPException
from fastapi.responses import FileResponse
import os
from core.logger import setup_logger
from services.audio_detect_service import EmotionPredictionService
import tensorflow as tf
app = FastAPI()
router = APIRouter()
audio: UploadFile
logger = setup_logger()
model = tf.keras.models.load_model('../ML_Models/Emotion_Detection_Model/mymodel.h5')
prediction_service = EmotionPredictionService(model)
@router.post("/upload_emotion/audio", tags=["Emotion Detection"])
async def upload_audio(audio: UploadFile = File(...)):
try:
file_location = f"files/emotion/audio/{audio.filename}"
with open(file_location, "wb") as file:
file.write(audio.file.read())
return {"text": "OK"}
except Exception as e:
logger.info(f"Failed to upload file. {e}")
raise HTTPException(
status_code=500,
detail="Failed to upload the audio"
)
@router.post('/predict_emotion/audio', tags=["Emotion Detection"])
def predict_using_audio(audio_request: UploadFile = File(...)):
try:
return prediction_service.predict_emotion_detection_audio_new(audio_request)
except Exception as e:
logger.info(f"Error. {e}")
raise HTTPException(
status_code=500,
detail="Request Failed."
)
Project/Backend/Server_Python/controllers/video_detect_controler.py 0 → 100644
View file @ b677141b
from fastapi import APIRouter,FastAPI, UploadFile ,File,HTTPException
from fastapi.responses import FileResponse
from keras.models import model_from_json
import os
from core.logger import setup_logger
from services.video_detection_service import EmotionPredictionService
import tensorflow as tf
import os
# Get the absolute path to the 'model' directory
model_directory = os.path.abspath('model')
# Construct the absolute path to 'emotion_model.json'
json_file_path = os.path.join(model_directory, 'emotion_model.json')
# Open the JSON file
# json_file = open(json_file_path, 'r')
app = FastAPI()
router = APIRouter()
video: UploadFile
logger = setup_logger()
# Load emotion detection model
json_file = open('../ML_Models/Emotion_Detection_Model/emotion_model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
emotion_model = model_from_json(loaded_model_json)
emotion_model.load_weights("../ML_Models/Emotion_Detection_Model/emotion_model.h5")
prediction_service = EmotionPredictionService(emotion_model)
@router.post("/upload_emotion/video", tags=["Emotion Detection"])
async def upload_video(video: UploadFile = File(...)):
try:
file_location = f"files/emotion/video/{video.filename}"
with open(file_location, "wb") as file:
file.write(video.file.read())
return {"text": "OK2"}
except Exception as e:
logger.info(f"Failed to upload file. {e}")
raise HTTPException(
status_code=500,
detail="Failed to upload the video"
)
@router.post('/predict_emotion/video', tags=["Emotion Detection"])
def predict_using_video(video_request: UploadFile = File(...)):
try:
return prediction_service.predict_emotion_detection_video_new(video_request= video_request)
return {"text": "OK5"}
except Exception as e:
logger.info(f"Error. {e}")
raise HTTPException(
status_code=500,
detail="Request Failed."
)
Project/Backend/Server_Python/node_modules/.yarn-integrity 0 → 100644
View file @ b677141b
{
"systemParams": "win32-x64-93",
"modulesFolders": [],
"flags": [],
"linkedModules": [],
"topLevelPatterns": [],
"lockfileEntries": {},
"files": [],
"artifacts": {}
}
\ No newline at end of file
Project/Backend/Server_Python/services/audio_detect_service.py 0 → 100644
View file @ b677141b
# from fastapi.types import ModelNameMap
# from sklearn import model_selection
# import tensorflow as tf
import numpy as np
import librosa
from fastapi import HTTPException, UploadFile
from typing import Dict
import os
from core.logger import setup_logger
logger = setup_logger()
class EmotionPredictionService:
def __init__(self, model):
self.model = model
def predict_emotion_detection_audio(self,audio_request: UploadFile) -> Dict[str, str]:
try:
# Create a temporary file to save the audio
audio_location = f"files/emotion/audio/{audio_request.filename}"
with open(audio_location, "wb") as file:
file.write(audio_request.file.read())
# Load the audio data from the saved file
y, sr = librosa.load(audio_location)
mfccs = np.mean(librosa.feature.mfcc(y=y, sr=sr, n_mfcc=40).T, axis=0)
test_point = np.reshape(mfccs, newshape=(1, 40, 1))
predictions = self.model.predict(test_point)
emotions = {
1: 'neutral', 2: 'calm', 3: 'happy', 4: 'sad',
5: 'angry', 6: 'fearful', 7: 'disgust', 8: 'surprised'
}
predicted_emotion = emotions[np.argmax(predictions[0]) + 1]
return {"predicted_emotion": predicted_emotion}
except Exception as e:
logger.error(f"Failed to make predictions. {str(e)}")
raise HTTPException(
status_code=500,
detail=f"Failed to make predictions. Error: {str(e)}"
)
def predict_emotion_detection_audio_new(self, audio_request: UploadFile) -> Dict[str, str]:
try:
# Create a temporary file to save the audio
audio_location = f"files/emotion/audio/{audio_request.filename}"
with open(audio_location, "wb") as file:
file.write(audio_request.file.read())
# Load the audio data from the saved file
y, sr = librosa.load(audio_location)
mfccs = np.mean(librosa.feature.mfcc(y=y, sr=sr, n_mfcc=40).T, axis=0)
test_point = np.reshape(mfccs, newshape=(1, 40, 1))
predictions = self.model.predict(test_point)
emotions = {
1: 'neutral', 2: 'calm', 3: 'happy', 4: 'sad',
5: 'angry', 6: 'fearful', 7: 'disgust', 8: 'surprised'
}
predicted_emotion = emotions[np.argmax(predictions[0]) + 1]
return {"predicted_emotion": predicted_emotion}
except Exception as e:
logger.error(f"Failed to make predictions. {str(e)}")
raise HTTPException(
status_code=500,
detail=f"Failed to make predictions. Error: {str(e)}"
)
Project/Backend/Server_Python/services/video_detection_service.py 0 → 100644
View file @ b677141b
from fastapi import FastAPI, UploadFile, HTTPException
from typing import Dict
import cv2
import numpy as np
from keras.models import model_from_json
import os
app = FastAPI()
from core.logger import setup_logger
logger = setup_logger()
# Define the emotion labels
emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
# Load the emotion detection model
json_file = open('../ML_Models/Emotion_Detection_Model/emotion_model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
emotion_model = model_from_json(loaded_model_json)
emotion_model.load_weights("../ML_Models/Emotion_Detection_Model/emotion_model.h5")
class EmotionPredictionService:
def __init__(self, model):
self.model = model
def predict_emotion_detection_video(video_request: UploadFile) -> Dict[str, str]:
try:
# Create a temporary file to save the video
video_location = f"files/emotion/video/{video_request.filename}"
with open(video_location, "wb") as file:
file.write(video_request.file.read())
# Initialize video capture
cap = cv2.VideoCapture(video_location)
if not cap.isOpened():
raise HTTPException(
status_code=400,
detail="Failed to open video file."
)
predicted_emotions = []
while True:
ret, frame = cap.read()
if not ret:
break
emotions = predict_emotion_from_frame(frame)
predicted_emotions.extend(emotions)
cap.release()
os.remove(video_location)
return {"predicted_emotions": predicted_emotions}
except Exception as e:
logger.error(f"Failed to make predictions. {str(e)}")
raise HTTPException(
status_code=500,
detail=f"Failed to make predictions. Error: {str(e)}"
)
def predict_emotion_detection_video_new(self,video_request: UploadFile) -> Dict[str, str]:
try:
# Create a temporary file to save the video
video_location = f"files/emotion/video/{video_request.filename}"
with open(video_location, "wb") as file:
file.write(video_request.file.read())
# Initialize video capture
cap = cv2.VideoCapture(video_location)
if not cap.isOpened():
raise HTTPException(
status_code=400,
detail="Failed to open video file."
)
predicted_emotions = []
while True:
ret, frame = cap.read()
if not ret:
break
emotions = predict_emotion_from_frame(frame)
predicted_emotions.extend(emotions)
cap.release()
os.remove(video_location)
return {"predicted_emotions": predicted_emotions}
except Exception as e:
logger.error(f"Failed to make predictions. {str(e)}")
raise HTTPException(
status_code=500,
detail=f"Failed to make predictions. Error: {str(e)}"
)
# Function to predict emotion from a video frame
def predict_emotion_from_frame(frame):
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
face_detector = cv2.CascadeClassifier('../ML_Models/Emotion_Detection_Model/haarcascade_frontalface_default.xml')
num_faces = face_detector.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5)
emotions = []
for (x, y, w, h) in num_faces:
roi_gray_frame = gray_frame[y:y + h, x:x + w]
cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, (48, 48)), -1), 0)
emotion_prediction = emotion_model.predict(cropped_img)
maxindex = int(np.argmax(emotion_prediction))
emotions.append(emotion_dict[maxindex])
return emotions
Project/Backend/Server_Python/yarn.lock 0 → 100644
View file @ b677141b
# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
# yarn lockfile v1
Project/Frontend/SignConnectPlus/src/store/reducers/curriculum.ts 0 → 100644
View file @ b677141b
// third-party
import { createSlice } from '@reduxjs/toolkit';
// project imports
import { axiosServices } from 'utils/axios';
import { dispatch } from '../index';
// types
import { Curriculum, DefaultRootStateProps } from 'types/curriculum';
// ----------------------------------------------------------------------
const initialState: DefaultRootStateProps['curriculum'] = {
error: null,
success: null,
curriculums: [],
curriculum: null,
isLoading: false
};
const slice = createSlice({
name: 'curriculum',
initialState,
reducers: {
// TO INITIAL STATE
hasInitialState(state) {
state.error = null;
state.success = null;
state.isLoading = false;
},
// HAS ERROR
hasError(state, action) {
state.error = action.payload;
},
startLoading(state) {
state.isLoading = true;
},
finishLoading(state) {
state.isLoading = false;
},
// POST CURRICULUM
addCurriculumSuccess(state, action) {
state.curriculums.push(action.payload);
state.success = "Curriculum created successfully."
},
// GET CURRICULUM
fetchCurriculumSuccess(state, action) {
state.curriculum = action.payload;
state.success = null
},
// GET ALL CURRICULUM
fetchCurriculumsSuccess(state, action) {
state.curriculums = action.payload;
state.success = null
},
// UPDATE CURRICULUM
updateCurriculumSuccess(state, action) {
const updatedCurriculumIndex = state.curriculums.findIndex(curriculum => curriculum._id === action.payload._id);
if (updatedCurriculumIndex !== -1) {
state.curriculums[updatedCurriculumIndex] = action.payload;
}
state.success = "Curriculum updated successfully."
},
// DELETE CURRICULUM
deleteCurriculumSuccess(state, action) {
state.curriculums = state.curriculums.filter(curriculum => curriculum._id !== action.payload);
state.success = "Curriculum deleted successfully."
},
}
});
// Reducer
export default slice.reducer;
// ----------------------------------------------------------------------
/**
* TO INITIAL STATE
* @returns
*/
export function toInitialState() {
return async () => {
dispatch(slice.actions.hasInitialState())
}
}
/**
* POST CURRICULUM
* @param newCurriculum
* @returns
*/
export function addCurriculum(newCurriculum: Curriculum) {
return async () => {
dispatch(slice.actions.startLoading());
try {
const response = await axiosServices.post('/rest_node/curriculum', newCurriculum);
dispatch(slice.actions.addCurriculumSuccess(response.data));
} catch (error) {
dispatch(slice.actions.hasError(error));
} finally {
dispatch(slice.actions.finishLoading());
}
};
}
/**
* GET CURRICULUM
* @param id
* @returns
*/
export function fetchCurriculum(id: number) {
return async () => {
dispatch(slice.actions.startLoading());
try {
const response = await axiosServices.get(`/rest_node/curriculum/${id}`);
dispatch(slice.actions.fetchCurriculumSuccess(response.data));
} catch (error) {
dispatch(slice.actions.hasError(error));
} finally {
dispatch(slice.actions.finishLoading());
}
};
}
/**
* GET ALL CURRICULUMS
* @returns
*/
export function fetchCurriculums() {
return async () => {
dispatch(slice.actions.startLoading());
try {
const response = await axiosServices.get('/rest_node/curriculum');
dispatch(slice.actions.fetchCurriculumsSuccess(response.data));
} catch (error) {
dispatch(slice.actions.hasError(error));
} finally {
dispatch(slice.actions.finishLoading());
}
};
}
/**
* UPDATE CURRICULUM
* @param updatedCurriculum
* @returns
*/
export function updateCurriculum(updatedCurriculum: Curriculum) {
return async () => {
dispatch(slice.actions.startLoading());
try {
const response = await axiosServices.put(`/rest_node/curriculum/${updatedCurriculum._id}`, updatedCurriculum);
dispatch(slice.actions.updateCurriculumSuccess(response.data));
} catch (error) {
dispatch(slice.actions.hasError(error));
} finally {
dispatch(slice.actions.finishLoading());
}
};
}
/**
* DELETE CURRICULUM
* @param curriculumId
* @returns
*/
export function deleteCurriculum(curriculumId: string) {
return async () => {
dispatch(slice.actions.startLoading());
try {
await axiosServices.delete(`/rest_node/curriculum/${curriculumId}`);
dispatch(slice.actions.deleteCurriculumSuccess(curriculumId));
} catch (error) {
dispatch(slice.actions.hasError(error));
} finally {
dispatch(slice.actions.finishLoading());
}
};
}
Project/Frontend/SignConnectPlus/src/store/reducers/marksCalculator.ts 0 → 100644
View file @ b677141b
// third-party
import { createSlice } from '@reduxjs/toolkit';
// project imports
import { axiosServices } from 'utils/axios';
import { dispatch } from '../index';
// types
import { DefaultRootStateProps } from 'types/marksCalculator';
// ----------------------------------------------------------------------
const initialState: DefaultRootStateProps['marksCalculator'] = {
error: null,
success: null,
marksCalculator: null,
isLoading: false
};
const slice = createSlice({
name: 'marksCalculator',
initialState,
reducers: {
// TO INITIAL STATE
hasInitialState(state) {
state.error = null;
state.success = null;
state.isLoading = false;
},
// HAS ERROR
hasError(state, action) {
state.error = action.payload;
},
startLoading(state) {
state.isLoading = true;
},
finishLoading(state) {
state.isLoading = false;
},
// POST USER
marksCalculatorSuccess(state, action) {
state.marksCalculator = action.payload.result;
state.success = "Marks Calculated Successfully."
},
}
});
// Reducer
export default slice.reducer;
// ----------------------------------------------------------------------
/**
* TO INITIAL STATE
* @returns
*/
export function toInitialState() {
return async () => {
dispatch(slice.actions.hasInitialState())
}
}
/**
* POST Marks Calculator
* @param curriculumIndex
* @param tutorialIndex
* @param imageData
* @param targetClass
* @returns
*/
export function CalculateMarks(curriculumIndex: number, tutorialIndex: number, imageData: any, targetClass: string) {
return async () => {
dispatch(slice.actions.startLoading());
try {
// Construct the request body as needed (e.g., for formData)
const formData = new FormData();
formData.append('image', imageData);
formData.append('class', targetClass);
const response = await axiosServices.post(`/rest_node/marks-calculator/curriculum/${curriculumIndex}/tutorial/${tutorialIndex}`, formData);
dispatch(slice.actions.marksCalculatorSuccess(response.data));
} catch (error) {
dispatch(slice.actions.hasError(error));
} finally {
dispatch(slice.actions.finishLoading());
}
};
};
Project/Frontend/SignConnectPlus/src/store/reducers/tutorial.ts 0 → 100644
View file @ b677141b
// third-party
import { createSlice } from '@reduxjs/toolkit';
// project imports
import { axiosServices } from 'utils/axios';
import { dispatch } from '../index';
// types
import { DefaultRootStateProps, Tutorial } from 'types/tutorial';
// ----------------------------------------------------------------------
const initialState: DefaultRootStateProps['tutorial'] = {
error: null,
success: null,
tutorials: [],
tutorial: null,
isLoading: false
};
const slice = createSlice({
name: 'tutorial',
initialState,
reducers: {
// TO INITIAL STATE
hasInitialState(state) {
state.error = null;
state.success = null;
state.isLoading = false;
},
// HAS ERROR
hasError(state, action) {
state.error = action.payload;
},
startLoading(state) {
state.isLoading = true;
},
finishLoading(state) {
state.isLoading = false;
},
// POST TUTORIAL
addTutorialSuccess(state, action) {
state.tutorials.push(action.payload);
state.success = "Tutorial created successfully."
},
// GET TUTORIAL
fetchTutorialSuccess(state, action) {
state.tutorial = action.payload;
state.success = null
},
// GET ALL TUTORIAL
fetchTutorialsSuccess(state, action) {
state.tutorials = action.payload;
state.success = null
},
// UPDATE TUTORIAL
updateTutorialSuccess(state, action) {
const updatedTutorialIndex = state.tutorials.findIndex(tutorial => tutorial._id === action.payload._id);
if (updatedTutorialIndex !== -1) {
state.tutorials[updatedTutorialIndex] = action.payload;
}
state.success = "Tutorial updated successfully."
},
// DELETE TUTORIAL
deleteTutorialSuccess(state, action) {
state.tutorials = state.tutorials.filter(tutorial => tutorial._id !== action.payload);
state.success = "Tutorial deleted successfully."
},
}
});
// Reducer
export default slice.reducer;
// ----------------------------------------------------------------------
/**
* TO INITIAL STATE
* @returns
*/
export function toInitialState() {
return async () => {
dispatch(slice.actions.hasInitialState())
}
}
/**
* POST TUTORIAL
* @param newTutorial
* @returns
*/
export function addTutorial(newTutorial: Tutorial) {
return async () => {
dispatch(slice.actions.startLoading());
try {
const response = await axiosServices.post('/rest_node/tutorial', newTutorial);
dispatch(slice.actions.addTutorialSuccess(response.data));
} catch (error) {
dispatch(slice.actions.hasError(error));
} finally {
dispatch(slice.actions.finishLoading());
}
};
}
/**
* GET TUTORIAL
* @param id
* @returns
*/
export function fetchTutorial(id: number) {
return async () => {
dispatch(slice.actions.startLoading());
try {
const response = await axiosServices.get(`/rest_node/tutorial/${id}`);
dispatch(slice.actions.fetchTutorialSuccess(response.data));
} catch (error) {
dispatch(slice.actions.hasError(error));
} finally {
dispatch(slice.actions.finishLoading());
}
};
}
/**
* GET ALL TUTORIALS
* @returns
*/
export function fetchTutorials() {
return async () => {
dispatch(slice.actions.startLoading());
try {
const response = await axiosServices.get('/rest_node/tutorial');
dispatch(slice.actions.fetchTutorialsSuccess(response.data));
} catch (error) {
dispatch(slice.actions.hasError(error));
} finally {
dispatch(slice.actions.finishLoading());
}
};
}
/**
* UPDATE TUTORIAL
* @param updatedTutorial
* @returns
*/
export function updateTutorial(updatedTutorial: Tutorial) {
return async () => {
dispatch(slice.actions.startLoading());
try {
const response = await axiosServices.put(`/rest_node/tutorial/${updatedTutorial._id}`, updatedTutorial);
dispatch(slice.actions.updateTutorialSuccess(response.data));
} catch (error) {
dispatch(slice.actions.hasError(error));
} finally {
dispatch(slice.actions.finishLoading());
}
};
}
/**
* DELETE TUTORIAL
* @param tutorialId
* @returns
*/
export function deleteTutorial(tutorialId: string) {
return async () => {
dispatch(slice.actions.startLoading());
try {
await axiosServices.delete(`/rest_node/tutorial/${tutorialId}`);
dispatch(slice.actions.deleteTutorialSuccess(tutorialId));
} catch (error) {
dispatch(slice.actions.hasError(error));
} finally {
dispatch(slice.actions.finishLoading());
}
};
}
Project/Frontend/SignConnectPlus/src/store/reducers/userProgress.ts 0 → 100644
View file @ b677141b
// third-party
import { createSlice } from '@reduxjs/toolkit';
// project imports
import { axiosServices } from 'utils/axios';
import { dispatch } from '../index';
// types
import { DefaultRootStateProps, curriculumTypeUserProgress } from 'types/userProgress';
// ----------------------------------------------------------------------
const initialState: DefaultRootStateProps['userProgress'] = {
error: null,
success: null,
userProgresses: [],
userProgress: null,
isLoading: false
};
const slice = createSlice({
name: 'userProgress',
initialState,
reducers: {
// TO INITIAL STATE
hasInitialState(state) {
state.error = null;
state.success = null;
state.isLoading = false;
},
// HAS ERROR
hasError(state, action) {
state.error = action.payload;
},
startLoading(state) {
state.isLoading = true;
},
finishLoading(state) {
state.isLoading = false;
},
// POST USER_PROGRESS
addUpdateUserProgressSuccess(state, action) {
// state.userProgresses.push(action.payload);
state.success = "User Progress created or updated successfully."
},
// GET USER_PROGRESS
fetchUserProgressSuccess(state, action) {
state.userProgress = action.payload.userProgress;
state.success = null
},
// UPDATE USER_PROGRESS
updateUserProgressSuccess(state, action) {
// const updatedUserProgressIndex = state.userProgresses.findIndex(userProgress => userProgress._id === action.payload._id);
// if (updatedUserProgressIndex !== -1) {
// state.userProgresses[updatedUserProgressIndex] = action.payload;
// }
state.success = "UserProgress updated successfully."
},
}
});
// Reducer
export default slice.reducer;
// ----------------------------------------------------------------------
/**
* TO INITIAL STATE
* @returns
*/
export function toInitialState() {
return async () => {
dispatch(slice.actions.hasInitialState())
}
}
/**
* POST USER_PROGRESS
* @param newUserProgress
* @returns
*/
export function addUserProgress(userId: string, curriculum: curriculumTypeUserProgress) {
return async () => {
dispatch(slice.actions.startLoading());
try {
const response = await axiosServices.post('/rest_node/user-progress/subscribe-curriculum', { userId, curriculum });
dispatch(slice.actions.addUpdateUserProgressSuccess(response.data));
} catch (error) {
dispatch(slice.actions.hasError(error));
} finally {
dispatch(slice.actions.finishLoading());
}
};
}
/**
* GET USER_PROGRESS
* @param userId
* @returns
*/
export function fetchUserProgress(userId: string) {
return async () => {
dispatch(slice.actions.startLoading());
try {
const response = await axiosServices.get(`/rest_node/user-progress/${userId}`);
dispatch(slice.actions.fetchUserProgressSuccess(response.data));
} catch (error) {
dispatch(slice.actions.hasError(error));
} finally {
dispatch(slice.actions.finishLoading());
}
};
}
/**
* UPDATE USER_PROGRESS
* @param updatedUserProgress
* @returns
*/
export function updateUserProgress(userId: string, curriculumCode: string, tutorialCode: string, taskItemTitle: string, taskItemMarkUser: number, taskItemSpentTime: number) {
return async () => {
dispatch(slice.actions.startLoading());
try {
const response = await axiosServices.put(`/rest_node/user-progress/update-task-item-progress`, { userId, curriculumCode, tutorialCode, taskItemTitle, taskItemMarkUser, taskItemSpentTime });
dispatch(slice.actions.updateUserProgressSuccess(response.data));
} catch (error) {
dispatch(slice.actions.hasError(error));
} finally {
dispatch(slice.actions.finishLoading());
}
};
}
Project/Frontend/SignConnectPlus/src/types/marksCalculator.ts 0 → 100644
View file @ b677141b
// Marks Calculator Type
export type MarksCalculator = {
predicted_class_name: string,
confidence: string,
status: string
};
export interface MarksCalculatorProps {
marksCalculator: MarksCalculator | null;
error: object | string | null;
success: object | string | null;
isLoading: boolean
}
export interface DefaultRootStateProps {
marksCalculator: MarksCalculatorProps;
}
\ No newline at end of file
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment