Merge remote-tracking branch 'origin/IT19041926'

# Conflicts:
#	backend/database.db
#	backend/models/attribute_model.py
#	backend/models/class_component_model.py
#	backend/routes/submission_routes.py
#	backend/services/class_model_detection_service.py

backend/models/attribute_model.py

@@ -3,9 +3,9 @@ from config.database import db
 
 class Attribute(db.Model):
     id = db.Column(db.Integer, primary_key=True)
-    data_type = db.Column(db.String(50), nullable=False)
+    data_type = db.Column(db.String(50))
     name = db.Column(db.String(50), nullable=False)
-    access_spec = db.Column(db.String(50), nullable=False)
+    access_spec = db.Column(db.String(50))
     class_id = db.Column(db.Integer)
 
     def __repr__(self) -> str:

backend/routes/submission_routes.py

@@ -5,7 +5,7 @@ from flask_jwt_extended import jwt_required, get_jwt_identity
 
 from constants.http_status_codes_constant import HTTP_400_BAD_REQUEST, HTTP_200_OK
 
-from services.class_model_detection_service import component_separation
+from services.class_diagram_class_detection_service import component_separation
 from models.actor_and_use_case import ActorANDUseCase
 from services.submission_service import save_submission
 from services.use_case_model_detection_service import model_object_detection

backend/services/class_model_detection_service.py

-import operator
-import os
-import re
-
-import cv2
-import numpy as np
-import pytesseract as ts
-from PIL import Image
-from models.attribute_model import Attribute
-
-from object_detection.utils import label_map_util
-
-import app
-import tensorflow as tf
-import spacy
-
-from config.database import db
-from models.component_model import Component
-from models.method_model import Method
-
-ts.pytesseract.tesseract_cmd = r'C:\Users\DELL\AppData\Local\Programs\Tesseract-OCR\tesseract.exe'
-
-
-def component_separation(filename, class_comp_id):
-    mdl1_path = app.CLASS_SAVED_MODEL_PATH
-    lbl1_path = app.CLASS_SAVED_LABEL_PATH
-    img1_path = app.SUBMISSION_PATH_CLASS + '/' + filename
-    image_nparray = np.array(Image.open(img1_path))
-
-    # print(img1_path)
-    boxes, num_entities, accurate_indexes, num_entities, category_index, class_id = class_object_detection(mdl1_path,
-                                                                                                           lbl1_path,
-                                                                                                           image_nparray)
-
-    # Convert the class id in their name
-    if num_entities > 0:
-        for index in range(0, len(accurate_indexes)):
-            if category_index[class_id[index]]['name'] == 'class':
-                print(filename)
-                _image = crop_image_(image_nparray, boxes, index)
-                _image = cv2.resize(_image, None, fx=2, fy=2)
-                class_details_detection(_image, class_comp_id)
-
-            elif category_index[class_id[index]]['name'] == 'interface':
-                _image = crop_image_(image_nparray, boxes, index)
-                _image = cv2.resize(_image, None, fx=2, fy=2)
-
-
-def class_object_detection(model_path, label_path, image_nparray):
-    detect_fn = tf.saved_model.load(model_path)
-    category_index = label_map_util.create_category_index_from_labelmap(label_path, use_display_name=True)
-    image_np = image_nparray
-    input_tensor = tf.convert_to_tensor(image_np)
-
-    input_tensor = input_tensor[tf.newaxis, ...]
-
-    detections = detect_fn(input_tensor)
-
-    num_detections = int(detections.pop('num_detections'))
-    detections = {key: value[0, :num_detections].numpy()
-                  for key, value in detections.items()}
-    detections['num_detections'] = num_detections
-
-    detections['detection_classes'] = detections['detection_classes'].astype(np.int64)
-
-    accurate_indexes = [k for k, v in enumerate(detections['detection_scores']) if (v > 0.7)]
-    num_entities = len(accurate_indexes)
-
-    class_id = operator.itemgetter(*accurate_indexes)(detections['detection_classes'])
-    boxes = detections['detection_boxes']
-    return boxes, num_entities, accurate_indexes, num_entities, category_index, class_id
-
-
-def class_details_detection(_image, class_comp_id):
-    attributes_methods = []
-
-    mdl2_path = app.CLASS_COMP_SAVED_MODEL_PATH
-    lbl2_path = app.CLASS_COMP_SAVED_LABEL_PATH
-    boxes_class, num_entities, accurate_indexes, num_entities, category_index, class_id = class_object_detection(
-        mdl2_path, lbl2_path, _image)
-
-    comp = class_name_detection(class_comp_id, _image, boxes_class, accurate_indexes)
-
-    if num_entities > 1:
-        for j in range(0, len(accurate_indexes)):
-            if category_index[class_id[j]]['name'] == 'class_attributes':
-                class_attributes = crop_image_(_image, boxes_class, j)
-                text = text_extraction(class_attributes)
-                attributes = save_attributes_methods(text, 'attribute')
-                alter_attributes_methods(attributes, comp.id)
-
-            elif category_index[class_id[j]]['name'] == 'class_methods':
-                class_methods = crop_image_(_image, boxes_class, j)
-                text = text_extraction(class_methods)
-                print(text)
-                methods = save_attributes_methods(text, 'method')
-                alter_attributes_methods(methods, comp.id)
-                print(text)
-
-
-def crop_image_(image, boxes, index):
-    # image = cv2.imread(path)
-    height, width, c = image.shape
-    # crop box format: xmin, ymin, xmax, ymax
-    ymin = boxes[index][0] * height
-    xmin = boxes[index][1] * width
-    ymax = boxes[index][2] * height
-    xmax = boxes[index][3] * width
-
-    cropped_image = image[int(ymin):int(ymax), int(xmin):int(xmax)]
-    # image = cv2.cvtColor(cropped_image, cv2.COLOR_BGR2GRAY)
-    # image = cv2.resize(image, (800, 500))
-
-    return cropped_image
-
-
-def text_extraction(image):
-    config = '-l eng --oem 1 --psm 4'
-    text = ts.image_to_string(image, config=config)
-    text = text.splitlines()
-    text = [x.strip(' ') for x in text]
-    text = list(filter(None, text))
-    return text
-
-
-def save_attributes_methods(text, typ):
-    global saved_data
-    nlp = spacy.load('en_core_web_sm')
-    for element in text:
-        print(element)
-        # removable = str.maketrans('', '', '()')
-        nlp_ner = spacy.load('ner_models/model-best')
-        nlp_output = nlp_ner(element)
-        attr = Attribute()
-        method = Method()
-
-        for token in nlp_output.ents:
-
-            if typ == 'attribute':
-                if token.label_ == 'ATTRIBUTE_NAME':
-                    attr.name = token.text
-
-                elif token.label_ == 'ACCESS_SP':
-                    attr.access_spec = covert_to_access_specifier(token.text)
-
-                elif token.label_ == 'DATA_TYPE':
-                    attr.data_type = token.text
-
-            elif typ == 'method':
-                if token.label_ == 'METHOD_NAME':
-                    method.name = token.text
-
-                elif token.label_ == 'ACCESS_SP':
-                    method.access_spec = covert_to_access_specifier(token.text)
-
-                elif token.label_ == 'DATA_TYPE':
-                    method.return_type = token.text
-
-        if typ == 'attribute':
-            print(attr)
-            db.session.add(attr)
-            db.session.commit()
-            saved_data.append(attr)
-
-        else:
-            print(method)
-            db.session.add(method)
-            db.session.commit()
-            saved_data.append(method)
-
-    return saved_data
-
-
-def alter_attributes_methods(element_list, class_id):
-    for element in element_list:
-        print(class_id)
-        print(element_list)
-        element.class_id = class_id
-        db.session.commit()
-
-
-def covert_to_access_specifier(access):
-    if access == "-":
-        return "Private"
-
-    elif access == "#":
-        return "Protected"
-
-    if access == "+":
-        return "Public"
-
-    elif access == "~":
-        return "Package"
-
-    else:
-        return ''
-
-
-def crop_and_hide(image, boxes, index):
-    height, width, c = image.shape
-    for i in range(0, len(index)):
-        ymin = boxes[i][0] * height
-        xmin = boxes[i][1] * width
-        ymax = boxes[i][2] * height
-        xmax = boxes[i][3] * width
-
-        cv2.rectangle(image, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (255, 255, 255), -1)
-    return image
-
-
-def class_name_detection(class_comp_id, image, boxes, index):
-    image = crop_and_hide(image, boxes, index)
-
-    class_name = text_extraction(image)
-    if ''.join(class_name) != '':
-        if "interface" in ''.join(class_name):
-            name = ''.join(class_name).replace("<<interface>>", "")
-            comp = Component(class_answer=class_comp_id, name=name, type="interface")
-        else:
-            name = ''.join(class_name)
-            comp = Component(class_answer=class_comp_id, name=name, type="class")
-
-        db.session.add(comp)
-        db.session.commit()
-        return comp

backend/services/question_preprocess_service.py

@@ -11,15 +11,15 @@ def remove_unwanted_values(data):
     return data
 
 
-# removing duplicates
-def remove_duplicates(data):
-    return list(set(data))
+# # removing duplicates
+# def remove_duplicates(data):
+#     return list(set(data))
 
 
-# punctuation removal
+# punctuation removing
 def remove_punctuation(sentence):
-    text_no_punct = [token for token in sentence if not token.is_punct]
-    cleaned_sentence = ' '.join(token.text for token in text_no_punct)
+    text_without_punctuation = [token for token in sentence if not token.is_punct]
+    cleaned_sentence = ' '.join(token.text for token in text_without_punctuation)
     return cleaned_sentence
 
 
@@ -37,31 +37,32 @@ def main(scenario, assignment_type):
     del sentences[-1]
 
     # creating required lists
-    nc = []
+    nouns_pronouns = []
     cleaned_extracted_actions = []
     cleaned_sentences = []
-    splitted_actions_array = []
+    splitted_actions_and_actor_array = []
 
     # looping through each sentence
     for sentence in sentences:
+        # getting actors using nouns pronouns
         res = get_nouns_pronouns(sentence)
-        nc.append(str(res))
+        nouns_pronouns.append(str(res))
         cleaned_sentence = remove_punctuation(sentence)
         cleaned_sentences.append(cleaned_sentence)
 
-        splitted_actions = split_actions(str(cleaned_sentence))
-        splitted_actions_array.append(splitted_actions)
+        splitted_actions_and_actor = split_actions(str(cleaned_sentence))
+        splitted_actions_and_actor_array.append(splitted_actions_and_actor)
 
-        extracted_actions = get_actions(splitted_actions)
+        extracted_actions = get_actions(splitted_actions_and_actor)
 
         if extracted_actions is not None:
             cleaned_extracted_actions.append(extracted_actions)
 
     # remove duplicates of the actors
-    nc = list(dict.fromkeys(nc))
-    data = remove_unwanted_values(nc)
+    nouns_pronouns = list(dict.fromkeys(nouns_pronouns))
+    data = remove_unwanted_values(nouns_pronouns)
 
-    extracted_relationships = get_include_extend_relationships(splitted_actions_array)
+    extracted_relationships = get_include_extend_relationships(splitted_actions_and_actor_array)
     actors_and_use_cases_array = identify_use_cases(cleaned_extracted_actions)
 
     if assignment_type == 1:

backend/services/tokenization_service.py

@@ -24,12 +24,14 @@ def get_nouns_pronouns(sentence):
             return token
 
 
+# removing punctuations
 def remove_punctuation(sentence):
     text_no_punct = [token for token in sentence if not token.is_punct]
     cleaned_sentence = ' '.join(token.text for token in text_no_punct)
     return cleaned_sentence
 
 
+# get actions and actors
 def split_actions(sentence):
     split_string = "should be able to "
     if split_string in sentence:
@@ -37,16 +39,18 @@ def split_actions(sentence):
         return extracted_string
 
 
-def get_actions(splitted_action):
+# get
+def get_actions(splitted_action_and_actor):
     temp_array = []
-    if splitted_action is not None and '|' in splitted_action[1]:
-        res = splitted_action[1].split(' | ')
-        # print('res',res)
-        temp_array.append(splitted_action[0])
+    if splitted_action_and_actor is not None and '|' in splitted_action_and_actor[1]:
+        res = splitted_action_and_actor[1].split(' | ')
+
+        temp_array.append(splitted_action_and_actor[0])
         temp_array.append(res[0])
+        print(temp_array)
         return temp_array
     else:
-        return splitted_action
+        return splitted_action_and_actor
 
 
 def get_sentences(text):

backend/tensorflow_models/class/label/label_map.pbtxt

 item {
     id: 1
-    name: 'class'
+    name: 'association'
 }
 
 item {
     id: 2
-    name: 'relationship'
+    name: 'class'
+}
+
+item {
+    id: 3
+    name: 'composition'
+}
+
+item {
+    id: 4
+    name: 'indirect association'
+}
+
+item {
+    id: 5
+    name: 'inheritance'
+}
+
+item {
+    id: 6
+    name: 'interface'
+}
+
+item {
+    id: 7
+    name: 'realization'
 }
\ No newline at end of file

frontend/src/pages/StudentDashboard.js

@@ -18,29 +18,23 @@ import ProfilePicture from "../assets/images/admin-user-img.jpg";
 
 import status from "../helpers/greeting";
 
-const SupplierDashboard = () => {
+const StudentDashboard = () => {
 	const [error, setError] = useState("");
 	const [isLoading, setIsLoading] = useState(true);
-	const [orderDetails, setOrderDetails] = useState([]);
+	const [StudentSubjects, setStudentSubjects] = useState([]);
 	const [value, onChange] = useState(new Date());
 
-	const fields = [
-		"",
-		"Assignment",
-		"Subject",
-		"Subject Code",
-		
-	];
+	const fields = ["", "Assignment", "Subject", "Subject Code"];
 	const subjects = [
 		{ ModuleCode: "IT20300", ModuleName: "CTSE", assign: "Assignment 01" },
 		{ ModuleCode: "IT30300", ModuleName: "DMS", assign: "Assignment 02" },
-		{ ModuleCode: "IT40300", ModuleName: "SPM", assign: "Assignment 03" }
-	]
+		{ ModuleCode: "IT40300", ModuleName: "SPM", assign: "Assignment 03" },
+	];
 	const renderOrderHead = (item, index) => <th key={index}>{item}</th>;
 
 	const renderOrderBody = (item, index) => (
 		<tr key={index}>
-			<td>{ }</td>
+			<td>{}</td>
 			<td>{item.assign}</td>
 			<td>{item.ModuleName}</td>
 			<td>{item.ModuleCode}</td>
@@ -64,17 +58,17 @@ const SupplierDashboard = () => {
 		</tr>
 	);
 
-	const getAllOrders = async () => {
+	const getAllSubjects = async () => {
 		try {
-			const res = await axios.get("orders/supplier/my");
-			setOrderDetails(res.data.orders);
+			const res = await axios.get("/subjects");
+			setStudentSubjects(subjects);
 			setIsLoading(false);
 		} catch (err) {
 			console.log(err.response);
 		}
 	};
 
-	useEffect(() => getAllOrders(), []);
+	useEffect(() => getAllSubjects(), []);
 
 	return (
 		<div>
@@ -91,16 +85,16 @@ const SupplierDashboard = () => {
 										<h3>
 											Today you have{" "}
 											{
-												orderDetails.filter(
-													(orderDetail) =>
-														orderDetail.DeliveryStatus === "pending"
+												StudentSubjects.filter(
+													(StudentSubject) =>
+														StudentSubject.stubjectstatus === "pending"
 												).length
 											}
 											{localStorage.setItem(
 												"notifications",
-												orderDetails.filter(
-													(orderDetail) =>
-														orderDetail.DeliveryStatus === "pending"
+												StudentSubjects.filter(
+													(StudentSubject) =>
+														StudentSubject.stubjectstatus === "pending"
 												).length
 											)}{" "}
 											Assignments to Complete
@@ -140,18 +134,17 @@ const SupplierDashboard = () => {
 							<div className="card">
 								<div className="flex">
 									<h2 className="request-title">Assignments to complete</h2>
-									
 								</div>
 								{/* {isLoading ? (
 									<Spinner />
 								) : orderDetails.length > 0 ? ( */}
-									<Table
-										limit="5"
-										headData={fields}
-										renderHead={(item, index) => renderOrderHead(item, index)}
-										bodyData={subjects}
-										renderBody={(item, index) => renderOrderBody(item, index)}
-									/>
+								<Table
+									limit="5"
+									headData={fields}
+									renderHead={(item, index) => renderOrderHead(item, index)}
+									bodyData={subjects}
+									renderBody={(item, index) => renderOrderBody(item, index)}
+								/>
 								{/* ) : (
 									<>
 										{setError("No Assignments found")}
@@ -184,4 +177,4 @@ const SupplierDashboard = () => {
 	);
 };
 
-export default SupplierDashboard;
+export default StudentDashboard;

frontend/src/pages/SubjectsStudent.js

@@ -11,155 +11,44 @@ import Badge from "../components/badge/Badge";
 
 import "../assets/css/Usercreate.css";
 
-const ManageOrdersSupplier = () => {
+const SubjectsStudent = () => {
 	const [error, setError] = useState("");
 	const [isLoading, setIsLoading] = useState(true);
-	const [orderDetails, setOrderDetails] = useState([]);
-	const fields = [
-		"",
-		"Module Code",
-		"Module Name",
-		"Year",
-		"",
-		"Actions",
-	];
+	const [SudentSubjects, setSudentSubjects] = useState([]);
+	const fields = ["", "Module Code", "Module Name", "Year", "", "Actions"];
 	const subjects = [
 		{ ModuleCode: "IT20300", ModuleName: "CTSE", Year: "4th Year" },
 		{ ModuleCode: "IT30300", ModuleName: "DMS", Year: "4th Year" },
-		{ ModuleCode: "IT40300", ModuleName: "SPM:", Year: "4th Year" }
-	]
+		{ ModuleCode: "IT40300", ModuleName: "SPM:", Year: "4th Year" },
+	];
 	const renderOrderHead = (item, index) => <th key={index}>{item}</th>;
 
 	const renderOrderBody = (item, index) => (
 		<tr key={index}>
-			<td>{ }</td>
+			<td>{}</td>
 			<td>{item.ModuleCode}</td>
 			<td>{item.ModuleName}</td>
 			<td>{item.Year}</td>
-			<td>{ }</td>
-			<td><Link to={`/auth/student/assignment`}>
-				<button className="view-btn">View</button>
-			</Link></td>
+			<td>{}</td>
 			<td>
-				<div className="row-user" style={{ paddingTop: "0" }}>
-					{item.DeliveryStatus === "pending" ? (
-						<div
-							style={{ cursor: "pointer" }}
-							onClick={() => {
-								if (
-									window.confirm(
-										"Are you sure to change order status as preparing?"
-									)
-								) {
-									changeDeliveryStatusAsPreparing(item._id);
-								}
-							}}
-						>
-							<Badge type="warning" content="Mark as preparing" />
-						</div>
-					) : item.DeliveryStatus === "preparing" ? (
-						<div
-							style={{ cursor: "pointer" }}
-							onClick={() => {
-								if (
-									window.confirm(
-										"Are you sure to change order status as delivering?"
-									)
-								) {
-									changeDeliveryStatusAsDelivering(item._id);
-								}
-							}}
-						>
-							<Badge type="primary" content="Mark as delivering" />
-						</div>
-					) : item.DeliveryStatus === "delivering" ? (
-						<div
-							style={{ cursor: "pointer" }}
-							onClick={() => {
-								if (
-									window.confirm(
-										"Are you sure to change order status as delivered?"
-									)
-								) {
-									changeDeliveryStatusAsDelivered(item._id);
-								}
-							}}
-						>
-							<Badge type="success" content="Mark as delivered" />
-						</div>
-					) : item.DeliveryStatus === "delivered" ? (
-						<div style={{ cursor: "pointer" }}>
-							<Link to={`/auth/supplier/deliveryreports/${item._id}`}>
-								<Badge type="normal" content="Send Delivery Report" />
-							</Link>
-						</div>
-					) : item.DeliveryStatus === "submitted" ? (
-						<div>
-							<Badge type="normal" content="Completed" />
-						</div>
-					) : (
-						""
-					)}
-				</div>
+				<Link to={`/auth/student/assignment`}>
+					<button className="view-btn">View</button>
+				</Link>
 			</td>
 		</tr>
 	);
 
-	const changeDeliveryStatusAsPreparing = async (id) => {
-		try {
-			const res = await axios.put(`orders/supplier/prepare/${id}`);
-			if (res.statusText === "OK") {
-				setIsLoading(true);
-				getAllOrders();
-				setError("");
-				window.alert("Delivery status changed as preparing");
-				window.location.reload();
-				setIsLoading(false);
-			}
-		} catch (err) {
-			console.log(err.response);
-		}
-	};
-
-	const changeDeliveryStatusAsDelivering = async (id) => {
-		try {
-			const res = await axios.put(`orders/supplier/deliver/${id}`);
-			if (res.statusText === "OK") {
-				getAllOrders();
-				setIsLoading(false);
-				window.alert("Delivery status changed as delivering");
-				window.location.reload();
-			}
-		} catch (err) {
-			console.log(err.response);
-		}
-	};
-
-	const changeDeliveryStatusAsDelivered = async (id) => {
-		try {
-			const res = await axios.put(`orders/supplier/delivered/${id}`);
-			if (res.statusText === "OK") {
-				getAllOrders();
-				window.alert("Delivery status changed as delivered");
-				setIsLoading(false);
-				window.location.reload();
-			}
-		} catch (err) {
-			console.log(err.response);
-		}
-	};
-
-	const getAllOrders = async () => {
+	const getAllSubjects = async () => {
 		try {
-			const res = await axios.get("orders/supplier");
-			setOrderDetails(res.data.orders);
+			const res = await axios.get("/subjects");
+			setSudentSubjects(res.data.orders);
 			setIsLoading(false);
 		} catch (err) {
 			console.log(err.response);
 		}
 	};
 
-	useEffect(() => getAllOrders(), []);
+	useEffect(() => getAllSubjects(), []);
 
 	return (
 		<div>
@@ -193,4 +82,4 @@ const ManageOrdersSupplier = () => {
 	);
 };
 
-export default ManageOrdersSupplier;
+export default SubjectsStudent;