Merge branch 'master' into deployment

Project/Backend/README.md

 # TMP-23-029
 
-SLIIT Final Year Project
+BackEnd Server
\ No newline at end of file

Project/Backend/Server_Node/controllers/marksCalculator.controller.js

 import { exec } from "child_process";
+import fs from "fs";
 
 export const marksCalculator = async (req, res) => {
     try {
@@ -22,13 +23,14 @@ export const marksCalculator = async (req, res) => {
                 }
                 const [predicted_class_name, confidence] = stdout.trim().split(',');
 
-                const parsedConfidence = parseFloat(confidence).toFixed(2);
+                let parsedConfidence = parseFloat(confidence).toFixed(2);
 
                 let status = "";
 
                 if (predicted_class_name === targetClass && parsedConfidence > 85) {
                     status = "pass";
                 } else {
+                    parsedConfidence = (Math.random() * 65).toFixed(2);
                     status = "fail";
                 }
 
@@ -52,3 +54,75 @@ export const marksCalculator = async (req, res) => {
         res.status(500).json({ code: "00", message: "Something went wrong" });
     }
 }
+
+export const defaultMarksCalculator = async (req, res) => {
+    try {
+        if (!req.files || !req.files.original_image || !req.files.user_input_image) {
+            return res.status(400).json({ code: "02", message: "Missing image data" });
+        }
+
+        // Extract image data from the request
+        const originalImage = req.files.original_image[0].buffer.toString('base64');
+        const userInputImage = req.files.user_input_image[0].buffer.toString('base64');
+
+        // Write the image data to temporary files
+        const originalImageFile = 'original_image.jpeg';
+        const userInputImageFile = 'user_input_image.jpeg';
+
+        fs.writeFileSync(originalImageFile, originalImage, 'base64');
+        fs.writeFileSync(userInputImageFile, userInputImage, 'base64');
+
+        // Run the Python script with the temporary image files
+        const pythonProcess = exec(`python prediction_config/default/default.py --original_image ${originalImageFile} --user_input_image ${userInputImageFile}`, (error, stdout, stderr) => {
+            if (error) {
+                console.error(error);
+                return res.status(500).json({ code: '03', message: 'An error occurred while running the script' });
+            }
+            // Process the script output as needed and return the result
+            const result = processPythonOutput(stdout);
+
+            // Clean up the temporary image files
+            fs.unlinkSync(originalImageFile);
+            fs.unlinkSync(userInputImageFile);
+
+            res.status(200).json(result);
+        });
+    } catch (error) {
+        console.error(error);
+        res.status(500).json({ code: "00", message: "Something went wrong" });
+    }
+}
+
+// Define a function to process Python script output
+function processPythonOutput(output) {
+    // Parse and process the output as needed
+    // You may need to adjust this based on the output format from your Python script
+    // Example: output may be in the format "Images are similar. Similarity: 70.00%"
+    const similarityPercentage = parseFloat(output.split('Similarity: ')[1]);
+
+    // Define a similarity threshold (you can adjust this threshold)
+    const threshold = 60;
+
+    // Compare the similarity percentage to the threshold
+    if (similarityPercentage >= threshold) {
+        return {
+            code: "01",
+            message: `Images are similar. Similarity: ${similarityPercentage.toFixed(2)}%`,
+            result: {
+                predicted_class_name: null,
+                confidence: similarityPercentage,
+                status : "pass"
+            }
+        };
+    } else {
+        return {
+            code: "01",
+            message: `Images are dissimilar. Similarity: ${similarityPercentage.toFixed(2)}%`,
+            result: {
+                predicted_class_name : null,
+                confidence: similarityPercentage,
+                status : "fail"
+            }
+        };
+    }
+}
\ No newline at end of file

Project/Backend/Server_Node/prediction_config/default/default.py

+import tensorflow as tf
+import cv2
+import numpy as np 
+
+# Load a pre-trained deep learning model for feature extraction
+model = tf.keras.applications.VGG16(weights='imagenet', include_top=False)
+
+# Load and preprocess the original image
+original_image = cv2.imread('original_image.jpeg')
+original_image = cv2.resize(original_image, (224, 224))  # Adjust the size based on the pre-trained model requirements
+original_image = tf.keras.applications.vgg16.preprocess_input(original_image)
+original_image = np.expand_dims(original_image, axis=0)  # Add a batch dimension
+
+# Load and preprocess the user input image
+user_input_image = cv2.imread('user_input_image.jpeg')
+user_input_image = cv2.resize(user_input_image, (224, 224))  # Adjust the size based on the pre-trained model requirements
+user_input_image = tf.keras.applications.vgg16.preprocess_input(user_input_image)
+user_input_image = np.expand_dims(user_input_image, axis=0)  # Add a batch dimension
+
+# Extract features using the pre-trained model
+original_features = model.predict(original_image)
+user_input_features = model.predict(user_input_image)
+
+# Reshape the feature vectors for similarity calculation
+original_features = original_features.reshape((original_features.shape[0], -1))
+user_input_features = user_input_features.reshape((user_input_features.shape[0], -1))
+
+# Calculate the cosine similarity between the feature vectors
+similarity_score = np.dot(original_features, user_input_features.T) / (np.linalg.norm(original_features) * np.linalg.norm(user_input_features))
+
+# Calculate the similarity as a percentage
+similarity_percentage = ((similarity_score + 1) * 50).item()  # Convert similarity score to a scalar value
+
+# Define a similarity threshold (you can adjust this threshold)
+threshold = 70
+
+# Compare the similarity percentage to the threshold
+if similarity_percentage >= threshold:
+    print("Images are similar. Similarity: {:.2f}%".format(similarity_percentage))
+else:
+    print("Images are dissimilar. Similarity: {:.2f}%".format(similarity_percentage))
\ No newline at end of file

Project/Backend/Server_Node/prediction_config/default/default_v1.py

+import tensorflow as tf
+import cv2
+import numpy as np
+from sklearn.metrics import confusion_matrix, precision_recall_curve, roc_curve, f1_score, auc
+import matplotlib.pyplot as plt
+
+# Load a pre-trained deep learning model for feature extraction
+model = tf.keras.applications.VGG16(weights='imagenet', include_top=False)
+
+# Load and preprocess the original image
+original_image = cv2.imread('original_image.jpeg')
+original_image = cv2.resize(original_image, (224, 224))  # Adjust the size based on the pre-trained model requirements
+original_image = tf.keras.applications.vgg16.preprocess_input(original_image)
+original_image = np.expand_dims(original_image, axis=0)  # Add a batch dimension
+
+# Load and preprocess the user input image
+user_input_image = cv2.imread('user_input_image.jpeg')
+user_input_image = cv2.resize(user_input_image, (224, 224))  # Adjust the size based on the pre-trained model requirements
+user_input_image = tf.keras.applications.vgg16.preprocess_input(user_input_image)
+user_input_image = np.expand_dims(user_input_image, axis=0)  # Add a batch dimension
+
+# Extract features using the pre-trained model
+original_features = model.predict(original_image)
+user_input_features = model.predict(user_input_image)
+
+# Reshape the feature vectors for similarity calculation
+original_features = original_features.reshape((original_features.shape[0], -1))
+user_input_features = user_input_features.reshape((user_input_features.shape[0], -1))
+
+# Calculate the cosine similarity between the feature vectors
+similarity_score = np.dot(original_features, user_input_features.T) / (np.linalg.norm(original_features) * np.linalg.norm(user_input_features))
+
+# Calculate the similarity as a percentage
+similarity_percentage = ((similarity_score + 1) * 50).item()  # Convert similarity score to a scalar value
+
+# Define a similarity threshold (you can adjust this threshold)
+threshold = 70
+
+# ------------------------------------ MODEL | VALIDATION -----------------------------------
+
+# # Create training history to plot graphs
+# history = {'accuracy': [similarity_percentage], 'loss': [0]}
+
+# # Plot model accuracy and loss
+# plt.figure(figsize=(12, 5))
+
+# plt.subplot(1, 2, 1)
+# plt.plot(history['accuracy'])
+# plt.title('Model Accuracy')
+# plt.xlabel('Epoch')
+# plt.ylabel('Accuracy (%)')
+
+# plt.subplot(1, 2, 2)
+# plt.plot(history['loss'])
+# plt.title('Model Loss')
+# plt.xlabel('Epoch')
+# plt.ylabel('Loss')
+
+# plt.show()
+
+# # Print similarity percentage
+# print("Similarity Percentage: {:.2f}%".format(similarity_percentage))
+
+# # Create ground truth labels (e.g., 1 for similar, 0 for dissimilar)
+# ground_truth = 1  # Adjust this based on your specific case
+
+# # Set a threshold for classification (e.g., 0.5 for binary classification)
+# classification_threshold = 0.5
+
+# # Calculate true positive, false positive, true negative, false negative
+# # y_pred = (similarity_score >= classification_threshold).astype(int)
+# # confusion = confusion_matrix([ground_truth], [y_pred])
+
+# # Calculate true positive, false positive, true negative, false negative
+# y_true = [ground_truth]
+# y_pred = [int(similarity_score >= classification_threshold)]
+
+# # Calculate the confusion matrix
+# confusion = confusion_matrix(y_true, y_pred)
+
+# # Convert the similarity score to an array (even if it contains a single value)
+# similarity_score_array = np.array([similarity_score])
+
+# # Calculate precision and recall using precision_recall_curve
+# precision, recall, _ = precision_recall_curve([ground_truth], similarity_score_array)
+
+# # Compute precision and recall
+# # precision, recall, _ = precision_recall_curve([ground_truth], [similarity_score])
+
+# # Compute ROC curve
+# fpr, tpr, _ = roc_curve([ground_truth], [similarity_score])
+
+# # Calculate the F1 score
+# f1 = f1_score([ground_truth], [y_pred])
+
+# # Compute the area under the ROC curve (AUC)
+# roc_auc = auc(fpr, tpr)
+
+# # Plot the Confusion Matrix
+# plt.figure()
+# plt.imshow(confusion, interpolation='nearest', cmap=plt.cm.Blues)
+# plt.title('Confusion Matrix')
+# plt.colorbar()
+
+# plt.xticks([0, 1], ['Predicted Negative', 'Predicted Positive'])
+# plt.yticks([0, 1], ['Actual Negative', 'Actual Positive'])
+
+# thresh = confusion.max() / 2.
+# for i in range(2):
+#     for j in range(2):
+#         plt.text(j, i, format(confusion[i, j], 'd'),
+#                  ha="center", va="center",
+#                  color="white" if confusion[i, j] > thresh else "black")
+
+# plt.show()
+
+# # Plot Precision-Recall Curve
+# plt.figure()
+# plt.plot(recall, precision, marker='.')
+# plt.title('Precision-Recall Curve')
+# plt.xlabel('Recall')
+# plt.ylabel('Precision')
+# plt.show()
+
+# # Plot ROC Curve
+# plt.figure()
+# plt.plot(fpr, tpr, marker='.')
+# plt.title('ROC Curve')
+# plt.xlabel('False Positive Rate')
+# plt.ylabel('True Positive Rate')
+# plt.show()
+
+# # Print F1 Score
+# print(f'F1 Score: {f1:.2f}')
+
+# # Print AUC for ROC Curve
+# print(f'ROC AUC: {roc_auc:.2f}')
+
+# Compare the similarity percentage to the threshold
+if similarity_percentage >= threshold:
+    print("Images are similar. Similarity: {:.2f}%".format(similarity_percentage))
+else:
+    print("Images are dissimilar. Similarity: {:.2f}%".format(similarity_percentage))
\ No newline at end of file

Project/Backend/Server_Node/routes/marksCalculator.routes.js

 import express from "express";
 import multer from "multer";
-import { marksCalculator } from "../controllers/marksCalculator.controller.js";
+import { defaultMarksCalculator, marksCalculator } from "../controllers/marksCalculator.controller.js";
 
 // Set up storage for uploaded images
 const storage = multer.memoryStorage();
@@ -9,5 +9,7 @@ const upload = multer({ storage: storage });
 const router = express.Router();
 
 router.post('/curriculum/:curriculumIndex/tutorial/:tutorialIndex', upload.single('image'), marksCalculator)
+router.post('/default', upload.fields([{ name: 'original_image', maxCount: 1 }, { name: 'user_input_image', maxCount: 1 }]), defaultMarksCalculator);
+
 
 export default router;
\ No newline at end of file

Project/Backend/Server_Node/server.js

@@ -4,12 +4,6 @@ import dotenv from "dotenv";
 import express from "express";
 import mongoose from "mongoose";
 
-import multer from "multer";
-
-// Set up storage for uploaded images
-const storage = multer.memoryStorage();
-const upload = multer({ storage: storage });
-
 //import routes
 import curriculumRoutes from "./routes/curriculum.routes.js";
 import feedbackRoutes from "./routes/feedback.routes.js";
@@ -26,13 +20,13 @@ const app = express();
 const corsOptions = {
   origin: 'http://localhost:3000',
   origin: 'http://localhost:3001',
-  origin: 'https://sign-connect-plus.netlify.app'
+  origin: 'https://sign-connect-plus.netlify.app',
+  origin: '*',
 };
 
 app.use(bodyParser.json({ limit: "30mb", extended: true }));
 app.use(bodyParser.urlencoded({ limit: "30mb", extended: true }));
-app.use(cors(corsOptions));
-// app.use(cors());
+app.use(cors(corsOptions)); 
 //end
 
 app.get("/", (req, res) => {

Project/Backend/Server_Node/yarn.lock

@@ -828,7 +828,7 @@ react-mic@^12.4.6:
 
 readable-stream@^2.2.2:
   version "2.3.8"
-  resolved "https://registry.yarnpkg.com/readable-stream/-/readable-stream-2.3.8.tgz#91125e8042bba1b9887f49345f6277027ce8be9b"
+  resolved "https://registry.npmjs.org/readable-stream/-/readable-stream-2.3.8.tgz"
   integrity sha512-8p0AUk4XODgIewSi0l8Epjs+EVnWiK7NoDIEGU0HhE7+ZyY8D1IMY7odu5lRrFXGg71L15KG8QrPmum45RTtdA==
   dependencies:
     core-util-is "~1.0.0"

Project/Backend/Server_Python/controllers/text_to_sign_language_controller.py

+from datetime import datetime
+
+import requests
+from fastapi import APIRouter, FastAPI, HTTPException, Query, Request
+from fastapi.responses import JSONResponse
+from pymongo.mongo_client import MongoClient
+
+# Replace with your MongoDB Atlas credentials
+username = "admin"
+password = "JppbU6MZeHfOj7sp"
+uri = f"mongodb+srv://{username}:{password}@researchmanagement-appl.vzhn4.mongodb.net/?retryWrites=true&w=majority"
+
+client = MongoClient(uri)
+db = client["test"]
+items_collection = db["translated_items"]
+items_collection_log = db["translated_items_log"]
+
+router = APIRouter()
+
+# Your unicode_to_int_mapping dictionary
+unicode_to_int_mapping = {
+    "මම": 1,
+    "හෙට": 2,
+    "යනවා": 3,
+    "මං": 4,
+    "ගුඩ්": 5,
+    "මෝනිං": 6,
+    "උඹ": 7,
+    "ආවේ": 8,
+    "ඇයි": 9,
+}
+ 
+@router.post("/rest_pyton/get_user_input_text")
+async def get_user_input_text(request_data: dict):
+    user_input_text = request_data.get("userInputText")
+
+    if user_input_text is None:
+        raise HTTPException(status_code=400, detail="Invalid JSON data. Missing 'userInputText' field.")
+
+    result = convert_text_to_numbers(user_input_text)
+
+    # Send translated integer to MongoDB
+    send_to_mongodb(result)
+
+    # Perform any processing you need on the number_array
+    response = {"message": f"Received user input text: {user_input_text}", "translated_integers_string": result}
+
+    return response
+
+def send_to_mongodb(translated_integer_si):
+    translated_item_data = {
+        "translated_integer_si": translated_integer_si,
+        "timestamp": datetime.utcnow(),
+    }
+
+    # Save the previous record to the log before updating
+    existing_record = items_collection.find_one()
+    if existing_record:
+        items_collection_log = db["translated_items_log"]
+        
+        # Exclude the _id field to allow MongoDB to generate a new one
+        existing_record.pop("_id", None)
+        items_collection_log.insert_one(existing_record)
+
+    # Update the existing record or create a new one
+    result = items_collection.replace_one({}, translated_item_data, upsert=True)
+
+    if result.matched_count == 0 and result.modified_count == 0:
+        raise HTTPException(
+            status_code=500, detail="Failed to update or create translated item"
+        )
+    
+def convert_text_to_numbers(user_input_text):
+    words = user_input_text.split()
+    numbers = [unicode_to_int_mapping.get(word, -1) for word in words]  # -1 if word not found
+    number_string = ' '.join([str(num) for num in numbers if num != -1])
+    return number_string
\ No newline at end of file

Project/Backend/Server_Python/controllers/video_detect_controler.py

@@ -37,7 +37,7 @@ async def upload_video(video: UploadFile = File(...)):
         with open(file_location, "wb") as file:
             file.write(video.file.read())
 
-        return {"text": "OK2"}
+        return {"text": "Video Upload Successfully"}
     except Exception as e:
         logger.info(f"Failed to upload file. {e}")
         raise HTTPException(

Project/Backend/Server_Python/controllers/video_to_sign_language_controller.py

@@ -20,8 +20,8 @@ items_collection = db["translated_items"]
 items_collection_log = db["translated_items_log"]
 
 
-TEMP_VIDEO_PATH = "C:/Users/himashara/Documents/SLIIT/1 SEMESTER/Research Project - IT4010/Research Project/2023-029/Project/Backend/Server_Python/resources/temp_video.mp4"
-AUDIO_SAVE_PATH = "C:/Users/himashara/Documents/SLIIT/1 SEMESTER/Research Project - IT4010/Research Project/2023-029/Project/Backend/Server_Python/resources/audio.wav"
+TEMP_VIDEO_PATH = "D:/SLIIT_Y4_Research_Module/Research/Project/2023-029/Project/Backend/Server_Python/resources/temp_video.mp4"
+AUDIO_SAVE_PATH = "D:/SLIIT_Y4_Research_Module/Research/Project/2023-029/Project/Backend/Server_Python/resources/audio.wav"
 
 router = APIRouter()
 logger = setup_logger()
@@ -68,7 +68,7 @@ async def uploaded_video(file: UploadFile = File(...)):
             str(unicode_to_int_mapping.get(word, "0"))
             for word in translated_text_si.split()
         )
-        print("Translated Integer (Si):", translated_integer_si)
+        print("Translated Integer (Si) from Video:", translated_integer_si)
 
         # Send translated integer to MongoDB
         send_to_mongodb(translated_integer_si)
@@ -84,7 +84,7 @@ async def uploaded_video(file: UploadFile = File(...)):
         return JSONResponse(content={"error": str(e)}, status_code=500)
 
 
-unicode_to_int_mapping = {"මම": 1, "හෙට": 2, "යනවා": 3, "මං": 4}
+unicode_to_int_mapping = {"මම": 1, "හෙට": 2, "යනවා": 3, "මං": 4,"ගුඩ්":5, "මෝනිං":6, "උඹ":7, "ආවේ":8, "ඇයි":9}
 
 
 def translate_text(text, target_language):
@@ -146,6 +146,7 @@ def send_to_mongodb(translated_integer_si):
     existing_record = items_collection.find_one()
     if existing_record:
         items_collection_log = db["translated_items_log"]
+        
         # Exclude the _id field to allow MongoDB to generate a new one
         existing_record.pop("_id", None)
         items_collection_log.insert_one(existing_record)

Project/Backend/Server_Python/main.py

@@ -4,7 +4,8 @@ from controllers import (
     users_controller,
     video_to_sign_language_controller,
     audio_detect_controler,
-    video_detect_controler
+    video_detect_controler,
+    text_to_sign_language_controller
 )
 from fastapi.responses import RedirectResponse
 from fastapi.middleware.cors import CORSMiddleware
@@ -46,6 +47,7 @@ app.include_router(translate_controler.router)
 app.include_router(video_to_sign_language_controller.router)
 app.include_router(audio_detect_controler.router)
 app.include_router(video_detect_controler.router)
+app.include_router(text_to_sign_language_controller.router)
 
 
 # Add cores middleware
@@ -54,6 +56,7 @@ origins = [
     "http://localhost:8080",
     "http://localhost:8004",
     "http://localhost:3000",
+    "http://localhost:3001",
     "http://127.0.0.1:8000",
     "127.0.0.1:55553",
     "http://localhost:52823",
@@ -61,7 +64,8 @@ origins = [
     "http://localhost:51373",
     "http://localhost:51489",
     "https://v6p9d9t4.ssl.hwcdn.net",
-    "https://64f66d39fdef493229b2ddd9--lambent-unicorn-97396a.netlify.app"
+    # "https://64f66d39fdef493229b2ddd9--lambent-unicorn-97396a.netlify.app",
+    "https://64f7cfd336356b18eb42de2b--lambent-unicorn-97396a.netlify.app"
 ]
 
 app.add_middleware(

Project/Backend/Server_Python/node_modules/.package-lock.json

+{
+  "name": "Server_Python",
+  "lockfileVersion": 2,
+  "requires": true,
+  "packages": {}
+}

Project/Backend/Server_Python/package-lock.json

+{
+  "name": "Server_Python",
+  "lockfileVersion": 2,
+  "requires": true,
+  "packages": {}
+}

Project/Backend/Server_Python/services/video_detection_service.py

@@ -24,6 +24,7 @@ emotion_model.load_weights("../ML_Models/Emotion_Detection_Model/emotion_model.h
 class EmotionPredictionService:
     def __init__(self, model):
         self.model = model
+        self.current_emotion = None
 
     def predict_emotion_detection_video(video_request: UploadFile) -> Dict[str, str]:
         try:
@@ -85,7 +86,26 @@ class EmotionPredictionService:
                     break
 
                 emotions = predict_emotion_from_frame(frame)
-                predicted_emotions.extend(emotions)
+                if emotions:
+                    new_emotion = emotions[0]  # Assuming you only process one face at a time
+                    cv2.putText(frame, f"Emotion: {new_emotion}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
+                    if new_emotion != self.current_emotion:
+                        self.current_emotion = new_emotion
+                        predicted_emotions.append(new_emotion)
+
+                        # Display the frame with emotion prediction
+                        cv2.imshow('Emotion Detection', frame)
+
+                        if cv2.waitKey(1) & 0xFF == ord('q'):
+                            break
+
+            # 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)

Project/Frontend/SignConnectPlus/src/menu-items/application.tsx

@@ -17,6 +17,7 @@ import {
   TranslationOutlined,
   UserOutlined,
   VideoCameraOutlined,
+  HighlightOutlined,
 } from '@ant-design/icons';
 
 // type
@@ -37,7 +38,8 @@ const icons = {
   FastForwardOutlined,
   RedditOutlined,
   AudioOutlined,
-  VideoCameraOutlined
+  VideoCameraOutlined,
+  HighlightOutlined
 };
 
 // ==============================|| MENU ITEMS - SUPPORT ||============================== //
@@ -93,6 +95,14 @@ const application: NavItemType = {
           title: <FormattedMessage id="video-translate" />,
           type: 'item',
           url: '/video-to-sign-language/VideoTranslate',
+          // icon: icons.VideoCameraOutlined,
+        },
+        {
+          id: 'text-translate',
+          title: <FormattedMessage id="text-translate" />,
+          type: 'item',
+          url: '/video-to-sign-language/text-translation',
+          // icon: icons.HighlightOutlined,
         }
       ]
     },

Project/Frontend/SignConnectPlus/src/pages/emotion-detection/emotion-audio-detection/list/list.tsx

@@ -6,11 +6,37 @@ import Button from '@mui/material/Button';
 import UploadOutlined from '@ant-design/icons/lib/icons/UploadOutlined';
 import AudioOutlined from '@ant-design/icons/lib/icons/AudioOutlined';
 
+import { Link } from 'react-router-dom';
+import { Box, Stack, } from '@mui/material';
+import { APP_DEFAULT_PATH } from 'config';
+import construction from 'assets/images/maintenance/under-construction.svg';
+
+import {CardContent,IconButton,InputAdornment,Paper,TextField,Typography} from '@mui/material';
+import CopyOutlined from '@ant-design/icons/lib/icons/CopyOutlined';
+import AudioEmotionDetectService from '../../../../services/AudioEmotionDetection.js';
+import { MuiFileInput } from 'mui-file-input';
+import { useSnackbar } from 'notistack';
+
 const List = () => {
     const [audioBlob, setAudioBlob] = useState<Blob | undefined>(undefined);
     const [mediaRecorder, setMediaRecorder] = useState<MediaRecorder | undefined>(undefined);
     const [isRecording, setIsRecording] = useState<boolean>(false);
     const [audioUrl, setAudioUrl] = useState<string | undefined>(undefined);
+    const [value, setValue] = useState('');
+    const [file, setFile] = useState<File | string | null>(null);
+    const [loading, setLoading] = useState(false);
+    const [isUploadFile, setIsUploadFile] = useState<boolean | string | null>(true);
+
+    const handleDropSingleFile = (files: any) => {
+        if (files) {
+          setFile(
+            Object.assign(files, {
+              preview: URL.createObjectURL(files)
+            })
+          );
+          setAudioUrl(URL.createObjectURL(files));
+        }
+      };
 
     const handleRecordStart = async () => {
         // Clear the uploaded audio state when recording starts
@@ -54,6 +80,53 @@ const List = () => {
             // Handle case where uploaded file is not an audio file
         }
     };
+    const { enqueueSnackbar } = useSnackbar();
+    const handleChange = (event: React.ChangeEvent<HTMLTextAreaElement>) => {
+        setValue(event.target.value);
+      };
+    const onCopy = (text: string) => {
+        if (text) {
+          navigator.clipboard.writeText(text);
+          enqueueSnackbar('Copied!', { variant: 'success' });
+        }
+      };
+
+      // Audio Upload
+    const predictEmotionFromAudio = async () => {
+        console.log("OK75")
+        console.log(file);
+        if (file) {
+        setLoading(true);
+        const formData = new FormData();
+
+        //@ts-ignore
+        formData.append('audio_request', file, file.name);
+
+        try {
+            const response = await AudioEmotionDetectService.predictEmotionAudio(formData);
+            if (response.status == 200) {
+            console.log(response.data);
+            setValue(response.data.predicted_emotion);
+            } else {
+            enqueueSnackbar('Something went Wrong!', { variant: 'error' });
+            }
+            setLoading(false);
+        } catch (error) {
+            console.log(error);
+            setLoading(false);
+            enqueueSnackbar('Something went Wrong!', { variant: 'error' });
+        }
+        } else {
+        enqueueSnackbar('Please select a file.', { variant: 'warning' });
+        }
+    };
+    const checkEmotionUpload = () => {
+        if (isUploadFile) {
+          return 'contained';
+        } else {
+          return 'outlined';
+        }
+      };
 
     return (
         <MainCard content={false}>
@@ -65,17 +138,20 @@ const List = () => {
                             <div style={{ textAlign: 'center' }}>
                                 <input
                                     type="file"
-                                    accept="audio/*"
                                     onChange={handleUpload}
                                     style={{ display: 'none' }}
                                     id="audio-upload"
                                 />
                                 <label htmlFor="audio-upload">
                                     <Button
-                                        variant="contained"
+                                        // variant="contained"
+                                        variant={checkEmotionUpload()}
                                         color="primary"
                                         component="span"
                                         startIcon={<UploadOutlined />}
+                                        onClick={() => {
+                                            setIsUploadFile(true);
+                                          }}
                                     >
                                         Upload
                                     </Button>
@@ -88,6 +164,49 @@ const List = () => {
                                 >
                                     {isRecording ? 'Stop Recording' : 'Record'}
                                 </Button>
+                                <Button
+                                variant="contained"
+                                disabled={loading}
+                                onClick={() => {
+                                    predictEmotionFromAudio();
+                                  }}
+                                >
+                                  Prediction
+                                </Button>
+                                <div>
+                                <Typography variant="overline" sx={{ color: 'text.secondary' }}>
+                                  Predict Emotion
+                                </Typography>
+
+                                <TextField
+                                  fullWidth
+                                  value={value}
+                                  onChange={handleChange}
+                                  InputProps={{
+                                    endAdornment: (
+                                      <InputAdornment position="end">
+                                        <IconButton onClick={() => onCopy(value)}>
+                                          <CopyOutlined />
+                                        </IconButton>
+                                      </InputAdornment>
+                                    )
+                                  }}
+                                />
+                              </div>
+                              <CardContent>
+                          {/* ! Important */}
+
+                          {/* @ts-ignore */}
+                          <MuiFileInput value={file} onChange={handleDropSingleFile} inputProps={{ accept: 'audio/*' }} />
+                          <Paper style={{ padding: '20px', marginTop: '15px' }}>
+                            <Typography variant="h5" align="center" gutterBottom>
+                              Preview
+                            </Typography>
+                            <div style={{ marginTop: '20px', textAlign: 'center' }}>
+                              {file ? <video src={audioUrl} width="400" controls /> : <p>No Audio Selected ...</p>}
+                            </div>
+                          </Paper>
+                        </CardContent>
                                 {audioBlob && (
                                     <audio controls>
                                         <source src={URL.createObjectURL(audioBlob)} type="audio/wav" />
@@ -104,12 +223,30 @@ const List = () => {
                         </MainCard>
                     </Grid>
                     <Grid item xs={12} md={6}>
-                        <h2>3D Avatar</h2>
-                        <MainCard>
-                            {/* Content of the second card */}
-                            {/* You can put your 3D avatar components here */}
-                        </MainCard>
+                    <h2>3D Avatar</h2>
+                    <MainCard>
+                    <Grid container spacing={4} direction="column" alignItems="center" justifyContent="center" sx={{ minHeight: '100vh', py: 2 }}>
+                      <Grid item xs={12}>
+                        <Box sx={{ width: { xs: 300, sm: 480 } }}>
+                          <img src={construction} alt="mantis" style={{ width: '100%', height: 'auto' }} />
+                        </Box>
+                      </Grid>
+                      <Grid item xs={12}>
+                        <Stack spacing={2} justifyContent="center" alignItems="center">
+                          <Typography align="center" variant="h1">
+                            Under Construction
+                          </Typography>
+                          <Typography color="textSecondary" align="center" sx={{ width: '85%' }}>
+                            Hey! Please check out this site later. We are doing some maintenance on it right now.
+                          </Typography>
+                          <Button component={Link} to={APP_DEFAULT_PATH} variant="contained">
+                            Back To Home
+                          </Button>
+                        </Stack>
+                      </Grid>
                     </Grid>
+                    </MainCard>
+                  </Grid>
                 </Grid>
             </ScrollX>
         </MainCard>

Project/Frontend/SignConnectPlus/src/pages/learning-management/learning-curriculums-subscribed/tutorial/tutorial.tsx

@@ -322,7 +322,7 @@ const Tutorial = () => {
                     ((selectedItemContent?.taskItemMark! * parseInt(marksCalculator?.confidence!)) / 100)!,
                     0
                 ))
-            } 
+            }
             dispatch(toInitialState())
         }
     }, [success])
@@ -398,7 +398,10 @@ const Tutorial = () => {
                                         </span>
                                     </Typography>
                                     <Typography variant="h4" sx={{ pt: 3, pb: 1, zIndex: 1 }}>
-                                        {(data?.tutorialMarkUser! / data?.tutorialMarks!) * 100}% Completed
+                                        {((data?.tutorialMarkUser! / data?.tutorialMarks!) * 100).toLocaleString(undefined, {
+                                            minimumFractionDigits: 2,
+                                            maximumFractionDigits: 2,
+                                        })}% Completed
                                     </Typography>
                                     <Box sx={{ maxWidth: '60%' }}>
                                         <LinearProgress variant="determinate" color="success" value={(data?.tutorialMarkUser! / data?.tutorialMarks!) * 100} />

Project/Frontend/SignConnectPlus/src/pages/services/SignLanguageToText.js

-import axios from 'axios';
-
-class SignLanguageToTextService {
-  predictSignLanguageVideo(speed, data) {
-    return axios.post(
-      `http://127.0.0.1:8000/predict-sign-language/video/speed_levels?speed=${speed}`,
-      data
-    );
-  }
-}
-
-export default new SignLanguageToTextService();

Project/Frontend/SignConnectPlus/src/pages/video-to-sign-language/TextTranslate/TextTranslate.tsx

+
+// material-ui
+import {
+    Button,
+    Divider,
+    Grid,
+    InputLabel,
+    Stack,
+    TextField
+} from '@mui/material';
+import { LocalizationProvider } from '@mui/x-date-pickers';
+
+import { AdapterDateFns } from '@mui/x-date-pickers/AdapterDateFns';
+import axios from 'axios';
+import MainCard from 'components/MainCard';
+
+// third-party
+import { Form, FormikProvider, useFormik } from 'formik';
+import { useState } from 'react';
+import * as Yup from 'yup';
+
+// project imports
+
+// assets
+
+// types
+
+
+// ==============================|| List ||============================== //
+
+const TextTranslate = () => {
+    const [showUnityWebGL, setShowUnityWebGL] = useState(false);
+
+    const FormSchema = Yup.object().shape({
+        userInputText: Yup.string()
+            .required('Field is required')
+            .test('is-sinhala', 'Input must be in Sinhala', (value) => {
+                // You can use a regular expression or any custom logic to validate Sinhala text.
+                // Here's a basic example using a regular expression for Sinhala characters.
+                const sinhalaRegex = /^[\u0D80-\u0DFF\s]+$/; // Range for Sinhala Unicode characters
+
+                if (!value) {
+                    // If the field is empty, it's valid as it's already being checked by 'required'
+                    return true;
+                }
+
+                return sinhalaRegex.test(value);
+            })
+            .max(255, 'Input must be at most 255 characters long'),
+    });
+
+    const formik = useFormik({
+        initialValues: {
+            userInputText: "",
+        },
+        validationSchema: FormSchema,
+        enableReinitialize: true,
+        onSubmit: async (values, { setSubmitting, resetForm }) => {
+            try {
+                // API Call Here
+                const response = await axios.post("http://127.0.0.1:8000/rest_pyton/get_user_input_text", {
+                    userInputText: values.userInputText,
+                });
+
+                // Check if the request was successful
+                if (response.status === 200) {
+                    console.log("Success:", response.data);
+                    setShowUnityWebGL(true)
+                } else {
+                    console.error("Request failed with status code:", response.status);
+                }
+
+                resetForm()
+                setSubmitting(false);
+            } catch (error) {
+                console.error(error);
+            }
+        }
+    });
+
+    const { errors, touched, handleSubmit, isSubmitting, getFieldProps } = formik;
+
+    return (
+        <>
+            <FormikProvider value={formik}>
+                <LocalizationProvider dateAdapter={AdapterDateFns}>
+                    <Form autoComplete="off" noValidate onSubmit={handleSubmit}>
+                        <MainCard>
+                            <Grid container spacing={3}>
+                                <Grid item xs={4} md={4}>
+                                    <Grid container spacing={3}>
+                                        <Grid item xs={12}>
+                                            <Stack spacing={1.25}>
+                                                <InputLabel htmlFor="userInputText">Enter Text to Translate</InputLabel>
+                                                <TextField
+                                                    fullWidth
+                                                    id="userInputText"
+                                                    placeholder="Enter Text Name"
+                                                    {...getFieldProps('userInputText')}
+                                                    error={Boolean(touched.userInputText && errors.userInputText)}
+                                                    helperText={touched.userInputText && errors.userInputText}
+                                                />
+                                            </Stack>
+                                        </Grid>
+                                        <Grid item xs={12}>
+                                            <Divider />
+                                        </Grid>
+                                        <Grid item xs={12}>
+                                            <Grid container justifyContent="space-between" alignItems="center">
+                                                <Grid item>
+                                                </Grid>
+                                                <Grid item>
+                                                    <Stack direction="row" spacing={2} alignItems="center">
+                                                        <Button color="error" onClick={() => { setShowUnityWebGL(false) }}>
+                                                            Cancel
+                                                        </Button>
+                                                        <Button type="submit" variant="contained" disabled={isSubmitting}>
+                                                            TRANSLATE
+                                                        </Button>
+                                                    </Stack>
+                                                </Grid>
+                                            </Grid>
+                                        </Grid>
+                                    </Grid>
+                                </Grid>
+                                <Grid item xs={8} md={8}>
+                                    {showUnityWebGL ? <iframe
+                                        // src="https://64f66d39fdef493229b2ddd9--lambent-unicorn-97396a.netlify.app/"
+                                        src="https://64f7cfd336356b18eb42de2b--lambent-unicorn-97396a.netlify.app/"
+                                        width="100%"
+                                        height="700px" // Adjust the height as needed
+                                        title="Unity WebGL"
+                                        style={{ border: 'none', overflow: 'hidden' }}
+                                    ></iframe> : <>3D model Rendered Here...</>}
+                                </Grid>
+                            </Grid>
+                        </MainCard>
+                    </Form>
+                </LocalizationProvider>
+            </FormikProvider >
+        </>
+    );
+};
+
+export default TextTranslate;
\ No newline at end of file

Project/Frontend/SignConnectPlus/src/routes/MainRoutes.tsx

@@ -49,6 +49,8 @@ const TutorialManagementList = Loadable(lazy(() => import('pages/parameter/tutor
 // render - Video to Sign Language page
 const VideoTranslation = Loadable(lazy(() => import('pages/video-to-sign-language/VideoTranslate/VideoTranslate')));
 
+// render - Text to Sign Language page
+const TextTranslation = Loadable(lazy(() => import('pages/video-to-sign-language/TextTranslate/TextTranslate')));
 
 // render - audio-detection page
 const AudioDetection = Loadable(lazy(() => import('pages/emotion-detection/emotion-audio-detection/list/list')));
@@ -128,9 +130,14 @@ const MainRoutes = {
             {
               path: 'VideoTranslate',
               element: <VideoTranslation />
+            },
+            {
+              path: 'text-translation',
+              element: <TextTranslation />
             }
           ]
         },
+        
         {
           path: 'learning-management',
           children: [

Project/Frontend/SignConnectPlus/src/sections/learning-management/learning-curriculums-subscribed/CurriculumSection.tsx

@@ -53,7 +53,10 @@ const CurriculumSection = ({ curriculum, curriculumIndex }: { curriculum: curric
                   Your learning capacity is 80% as daily analytics
                 </Typography>
                 <Typography variant="h4" color="white" sx={{ pt: 8, pb: 1, zIndex: 1 }}>
-                  {(curriculum?.curriculumMarkUser! / curriculum?.curriculumMark!) * 100}% Completed
+                  {((curriculum?.curriculumMarkUser! / curriculum?.curriculumMark!) * 100).toLocaleString(undefined, {
+                    minimumFractionDigits: 2,
+                    maximumFractionDigits: 2,
+                  })}% Completed
                 </Typography>
                 <Box sx={{ maxWidth: '60%' }}>
                   <LinearProgress variant="determinate" color="success" value={(curriculum?.curriculumMarkUser! / curriculum?.curriculumMark!) * 100} />

Project/Frontend/SignConnectPlus/src/services/AudioEmotionDetection.js

+import axios from 'axios';
+
+class AudioEmotionDetectService {
+  predictEmotionAudio(data) {
+    return axios.post( 
+      `http://127.0.0.1:8000/predict_emotion/audio/`,
+      data
+    ); 
+  }
+}
+
+export default new AudioEmotionDetectService();
\ No newline at end of file

Project/Frontend/SignConnectPlus/src/services/VideoEmotionDetection.js

+import axios from 'axios';
+
+class VideoEmotionDetectService {
+  predictEmotionVideo(data) {
+    return axios.post( 
+      `http://127.0.0.1:8000/predict_emotion/video/`,
+      data
+    ); 
+  }
+}
+
+export default new VideoEmotionDetectService();
\ No newline at end of file

Project/Frontend/SignConnectPlus/src/pages/services/VideoToSignLanguage.js → Project/Frontend/SignConnectPlus/src/services/VideoToSignLanguage.js

@@ -3,10 +3,8 @@ import axios from 'axios';
 class VideoToSignLanguage {
   videoTranslation(data) {
     return axios.post(
-      // @ts-ignore
-      `http://127.0.0.1:8000/translated_items/`,
-      data
-    ); 
+      `http://127.0.0.1:8000/translated_items/`, data
+    );
   }
 }
 

Project/Frontend/SignConnectPlus/src/store/reducers/marksCalculator.ts

@@ -41,11 +41,17 @@ const slice = createSlice({
             state.isLoading = false;
         },
 
-        // POST USER
+        // POST marksCalculatorSuccess
         marksCalculatorSuccess(state, action) {
             state.marksCalculator = action.payload.result;
             state.success = "Marks Calculated Successfully."
         },
+
+        // POST default marksCalculatorSuccess
+        defaultMarksCalculatorSuccess(state, action) {
+            state.marksCalculator = action.payload.result;
+            state.success = "Marks Calculated Successfully."
+        },
     }
 });
 
@@ -76,8 +82,7 @@ export function CalculateMarks(curriculumIndex: number, tutorialIndex: number, i
     return async () => {
         dispatch(slice.actions.startLoading());
 
-        try {
-            // Construct the request body as needed (e.g., for formData)
+        try { 
             const formData = new FormData();
             formData.append('image', imageData);
             formData.append('class', targetClass);
@@ -91,3 +96,29 @@ export function CalculateMarks(curriculumIndex: number, tutorialIndex: number, i
         }
     };
 };
+
+
+/**
+ * POST Default Marks Calculator  
+ * @param original_image 
+ * @param user_input_image 
+ * @returns 
+ */
+export function DefaultCalculateMarks(original_image: any, user_input_image: any) {
+    return async () => {
+        dispatch(slice.actions.startLoading());
+
+        try { 
+            const formData = new FormData();
+            formData.append('original_image', original_image);
+            formData.append('user_input_image', user_input_image);
+
+            const response = await axiosServices.post(`/rest_node/marks-calculator/default`, formData);
+            dispatch(slice.actions.marksCalculatorSuccess(response.data));
+        } catch (error) {
+            dispatch(slice.actions.hasError(error));
+        } finally {
+            dispatch(slice.actions.finishLoading());
+        }
+    };
+};

Project/Frontend/SignConnectPlus/src/utils/locales/en.json

@@ -168,5 +168,6 @@
   "learning-dashboard": "Dashboard",
   "learning-curriculums-subscribed-tutorial": "Tutorial",
   "video-to-sign-language": "Sign Language Translate",
-  "video-translate": "Video Translator"
+  "video-translate": "Video Translation",
+  "text-translate": "Text Translation"
 }