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.gitattributes

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+autoattend/photos/Anshika.jpeg filter=lfs diff=lfs merge=lfs -text
+autoattend/photos/Rishit.jpeg filter=lfs diff=lfs merge=lfs -text
+autoattend/photos/Sakshi.jpeg filter=lfs diff=lfs merge=lfs -text

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README.md

@@ -1,14 +1,251 @@
+# OKULARY: Empowering Educators with Innovative Solutions
+[**Offlicial Docs - Give it a read <3**](https://github.com/SEAR-Innovate/OKULARY/files/14325957/O.pdf)
+
+
+## Made By:
+- [Rishit Chugh](https://github.com/R-C101)
+- [Sakshi Sawarkar](https://github.com/sakshi-sawarkar)
+- [Anshika Bhardwaj](https://github.com/AnshikaSwirl)
+- [Eeman Majumder](https://github.com/Eeman1113)
+
+
+
+Welcome to **OKULARY**, the ultimate teacher helper website designed to revolutionize the teaching experience. Our platform is built to address the diverse needs of educators by providing a comprehensive suite of resources, teaching methodologies, community support, AI-driven assessments, and performance analytics.
+
+🎯 Our aim is to develop an all-encompassing educational platform tailored for teachers, providing comprehensive resources, teaching methodologies, community support, AI-driven assessments, and performance analytics.
+
+🔧 Our platform is designed to empower educators with the tools and resources they need to excel in their profession. Whether you're a seasoned teacher looking for new teaching strategies or a new teacher seeking guidance, **OKULARY** has something for everyone. Sign up now and start your journey towards becoming a more effective and successful educator.
+
+## **Key Features:**
+
+- **📚 Resource Repository:** Access to a vast repository of educational resources.
+- **📝 Teaching Methodologies:** Guidance on effective teaching techniques and methodologies.
+- **👩‍🏫 Teacher Community:** A supportive online community for collaboration and sharing experiences.
+- **🤖 AI Course Outcomes and Answer Checking:** Automated assessment of course outcomes and answer checking using AI.
+- **🕵️‍♂️ Cheating and Malpractice Detection:** AI-powered tools to detect cheating and malpractice.
+- **📊 Student Performance Tracking:** Monitoring and tracking individual student performance.
+- **📈 Class Performance Analytics:** Data analytics to analyze class performance trends and patterns.
+- **👀 AI Class Monitoring:** Innovative system to monitor student attentiveness and manage attendance using AI technology.
+
+## **Get Started with OKULARY Today!**
+
+Join **OKULARY** today and take your teaching to the next level. Our platform is designed to empower educators with the tools and resources they need to excel in their profession. Whether you're a seasoned teacher looking for new teaching strategies or a new teacher seeking guidance, **OKULARY** has something for everyone. Sign up now and start your journey towards becoming a more effective and successful educator.
+
+---
+
+## **Plagiarism Checker**
+
+### **Introduction**
+
+The Plagiarism Checker is a tool designed to help educators detect potential plagiarism in student submissions. It compares the text of two documents and calculates a similarity score, indicating the likelihood of plagiarism. The tool can be used to check individual PDF documents or a collection of documents within a zip file.
+
+### **How to Use**
+
+1. **Upload Documents or Zip File**: You can upload two individual PDF documents or a zip file containing multiple PDF documents.
+2. **Calculate Plagiarism**: Click the "Calculate Plagiarism" button to compare the documents and view the similarity score.
+3. **View Results**: The tool will display the similarity score and indicate whether potential plagiarism has been detected.
+
+### **Features**
+
+- **Individual Document Comparison**: Compare two individual PDF documents for similarity.
+- **Zip File Comparison**: Compare multiple PDF documents within a zip file for similarity.
+- **Similarity Score**: View the similarity score between the documents.
+- **Plagiarism Detection**: Detect potential plagiarism based on the similarity score.
+
+### **Example Usage**
+
+1. **Individual Document Comparison**:
+   - Upload two individual PDF documents.
+   - Click "Calculate Plagiarism" to compare the documents.
+   - View the similarity score and potential plagiarism detection.
+
+2. **Zip File Comparison**:
+   - Upload a zip file containing multiple PDF documents.
+   - Click "Calculate Plagiarism" to compare the documents.
+   - View the similarity score and potential plagiarism detection for each pair of documents.
+
+---
+
+## **AI Class Monitoring**
+
+### **Introduction**
+
+AI Class Monitoring is an innovative system designed to help educators monitor student attentiveness and manage attendance using AI technology. The system uses facial recognition and behavioral analysis to track student engagement and provide real-time feedback to teachers.
+
+### **How to Use**
+
+1. **Face Recognition**: The system uses facial recognition technology to identify students in the classroom.
+2. **Behavioral Analysis**: The system analyzes student behavior to determine attentiveness and engagement levels.
+3. **Real-time Feedback**: Teachers receive real-time feedback on student engagement and attendance.
+
+### **Features**
+
+- **Facial Recognition**: Identify students in the classroom using facial recognition technology.
+- **Behavioral Analysis**: Analyze student behavior to determine attentiveness and engagement levels.
+- **Real-time Feedback**: Provide teachers with real-time feedback on student engagement and attendance.
+
+### **Example Usage**
+
+1. **Attendance Management**:
+   - The system automatically records student attendance based on facial recognition.
+   - Teachers receive real-time updates on student attendance.
+
+2. **Engagement Monitoring**:
+   - The system analyzes student behavior to determine attentiveness levels.
+   - Teachers receive real-time feedback on student engagement.
+
+---
+
+## **Teacher Community**
+
+### **Introduction**
+
+The Teacher Community is an online platform designed to foster collaboration and support among educators. It provides a space for teachers to share resources, exchange ideas, and seek advice from their peers.
+
+### **How to Use**
+
+1. **Join the Community**: Sign up for an account to join the Teacher Community.
+2. **Share Resources**: Share educational resources, teaching strategies, and lesson plans with other teachers.
+3. **Seek Advice**: Ask questions and seek advice from other educators on teaching-related topics.
+
+### **Features**
+
+- **Resource Sharing**: Share educational resources, teaching strategies, and lesson plans with other teachers.
+- **Collaboration**: Collaborate with other educators on projects and initiatives.
+- **Peer Support**: Seek advice and support from other educators on teaching-related topics.
+
+### **Example Usage**
+
+1. **Resource Sharing**:
+   - Share a lesson plan or teaching strategy with other teachers.
+   - Collaborate on a project or initiative with other educators.
+
+2. **Peer Support**:
+   - Ask for advice or seek support from other educators on teaching-related topics.
+   - Share experiences and learn from the experiences of other teachers.
+
+---
+
+## **AI Course Outcomes and Answer Checking**
+
+### **Introduction**
+
+AI Course Outcomes and Answer Checking is a tool designed to help educators assess student learning outcomes and check student answers using AI technology. The tool provides automated assessment of course outcomes and answer checking based on predefined criteria.
+
+### **How to Use**
+
+1. **Upload Teacher and Student Files**: Upload the teacher's sample answers and student submissions in .txt format.
+2. **Generate GPT-3 Response**: The tool uses GPT-3 to generate a response based on the teacher's sample answers and student submissions.
+3. **View Grading Result**: The tool provides a grading result in JSON format, indicating the marks allotted for each question.
+
+### **Features**
+
+- **Automated Assessment**: Assess student learning outcomes and check student answers using AI technology.
+- **GPT-3 Integration**: Use GPT-3 to generate responses based on teacher's sample answers and student submissions.
+- **Grading Result**: View the grading result in JSON format, indicating the marks allotted for each question.
+
+### **Example Usage**
+
+1. **Automated Assessment**:
+   - Upload the teacher's sample answers and student submissions.
+   - Click "Grade" to generate a grading result using AI technology.
+   - View the grading result in JSON format.
+
+2. **GPT-3 Integration**:
+   - Use GPT-3 to generate responses based on teacher's sample answers and student submissions.
+   - View the generated responses and grading result.
+
 ---
-title: Okulary
-emoji: 🔥
-colorFrom: red
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.41.0
-app_file: app.py
-pinned: false
-license: mit
-short_description: 'project '
+
+
+
+## **Cheating and Malpractice Detection**
+
+### **Introduction**
+
+Cheating and Malpractice Detection is a tool designed to help educators detect potential cheating and malpractice in student submissions. The tool uses AI technology to analyze student submissions and identify suspicious patterns.
+
+### **How to Use**
+
+1. **Upload Student Submissions**: Upload the student's submissions in .txt format.
+2. **Analyze Submissions**: The tool uses AI technology to analyze student submissions and identify suspicious patterns.
+3. **View Detection Result**: The tool provides a detection result in JSON format, indicating the likelihood of cheating or malpractice.
+
+### **Features**
+
+- **AI Technology**: Use AI technology to analyze student submissions and identify suspicious patterns.
+- **Detection Result**: View the detection result in JSON format, indicating the likelihood of cheating or malpractice.
+
+### **Example Usage**
+
+1. **Analyze Submissions**:
+   - Upload the student's submissions.
+   - Click "Analyze" to analyze the submissions using AI technology.
+   - View the detection result in JSON format.
+
+2. **Detection Result**:
+   - View the detection result in JSON format, indicating the likelihood of cheating or malpractice.
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## **Student Performance Tracking**
+
+### **Introduction**
+
+Student Performance Tracking is a tool designed to help educators monitor and track individual student performance. The tool provides insights into student progress, strengths, and areas for improvement.
+
+### **How to Use**
+
+1. **Upload Student Submissions**: Upload the student's submissions in .txt format.
+2. **Analyze Submissions**: The tool uses AI technology to analyze student submissions and provide insights into student performance.
+3. **View Performance Insights**: The tool provides performance insights in JSON format, indicating student progress, strengths, and areas for improvement.
+
+### **Features**
+
+- **AI Technology**: Use AI technology to analyze student submissions and provide insights into student performance.
+- **Performance Insights**: View performance insights in JSON format, indicating student progress, strengths, and areas for improvement.
+
+### **Example Usage**
+
+1. **Analyze Submissions**:
+   - Upload the student's submissions.
+   - Click "Analyze" to analyze the submissions using AI technology.
+   - View the performance insights in JSON format.
+
+2. **Performance Insights**:
+   - View the performance insights in JSON format, indicating student progress, strengths, and areas for improvement.
+
+---
+
+## **Class Performance Analytics**
+
+### **Introduction**
+
+Class Performance Analytics is a tool designed to help educators analyze class performance trends and patterns. The tool provides insights into student engagement, attendance, and overall class performance.
+
+### **How to Use**
+
+1. **Upload Student Submissions**: Upload the student's submissions in .txt format.
+2. **Analyze Submissions**: The tool uses AI technology to analyze student submissions and provide insights into class performance.
+3. **View Performance Insights**: The tool provides performance insights in JSON format, indicating class performance trends and patterns.
+
+### **Features**
+
+- **AI Technology**: Use AI technology to analyze student submissions and provide insights into class performance.
+- **Performance Insights**: View performance insights in JSON format, indicating class performance trends and patterns.
+
+### **Example Usage**
+
+1. **Analyze Submissions**:
+   - Upload the student's submissions.
+   - Click "Analyze" to analyze the submissions using AI technology.
+   - View the performance insights in JSON format.
+
+2. **Performance Insights**:
+   - View the performance insights in JSON format, indicating class performance trends and patterns.
+
+---
+
+## **Conclusion**
+
+**OKULARY** is a comprehensive educational platform designed to empower educators with the tools and resources they need to excel in their profession. Whether you're a seasoned teacher looking for new teaching strategies or a new teacher seeking guidance, **OKULARY** has something for everyone. Sign up now and start your journey towards becoming a more effective and successful educator.

Student_Attentiveness/.gitignore

@@ -0,0 +1,160 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

Student_Attentiveness/app.py

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+import cv2
+import numpy as np
+import os
+import pandas as pd
+import streamlit as st
+import matplotlib.pyplot as plt
+
+atnd = []
+
+# Train the face recognition model using the collected dataset
+def train_model():
+    data_path = 'data'
+    face_classifier = cv2.CascadeClassifier("./Student_Attentiveness/haarcascade_frontalface_default.xml")
+    training_data = []
+    labels = []
+
+    for root, dirs, files in os.walk(data_path):
+        for file in files:
+            if file.endswith('jpg'):
+                path = os.path.join(root, file)
+                label = int(path.split('.')[1])
+                image = cv2.imread(path)
+                gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+                face = face_classifier.detectMultiScale(gray, 1.3, 5)
+                if face is not ():
+                    for (x, y, w, h) in face:
+                        cropped_face = gray[y:y + h, x:x + w]
+                        training_data.append(cropped_face)
+                        labels.append(label)
+
+    labels = np.array(labels)
+    model = cv2.face.LBPHFaceRecognizer_create()
+    model.train(training_data, labels)
+
+    return model
+
+# Implement the student attention monitoring system
+def monitor_attention():
+    face_classifier = cv2.CascadeClassifier("./Student_Attentiveness/haarcascade_frontalface_default.xml")
+    eye_classifier = cv2.CascadeClassifier("./Student_Attentiveness/haarcascade_eye.xml")
+    model = train_model()
+    cap = cv2.VideoCapture(0)
+
+    while True:
+        ret, frame = cap.read()
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        faces = face_classifier.detectMultiScale(gray, 1.3, 5)
+        if faces is not ():
+            for (x, y, w, h) in faces:
+                cropped_face = gray[y:y + h, x:x + w]
+                label, confidence = model.predict(cropped_face)
+                if confidence < 100:
+                    eyes = eye_classifier.detectMultiScale(cropped_face)
+                    if eyes is not ():
+                        for (ex, ey, ew, eh) in eyes:
+                            atnd.append(1)
+                    else:
+                        atnd.append(0)
+        # cv2.imshow('Student Attention', frame)
+        df = pd.DataFrame(atnd, columns=['Attention'])
+        
+        df.to_csv('./attention.csv')
+        if cv2.waitKey(1) == 13:
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+# Streamlit app
+st.title('Student Attention Monitoring System')
+
+# Start monitoring attention
+monitor_attention()
+
+# Display the graph of attentiveness output live
+st.line_chart(pd.DataFrame(atnd, columns=['Attention']))