# from collections import defaultdict # import json # import random # import requests # import streamlit as st # from datetime import datetime, timedelta # from youtube_transcript_api import YouTubeTranscriptApi # from utils.helpers import display_progress_bar, create_notification, format_datetime # from file_upload_vectorize import upload_resource, extract_text_from_file, create_vector_store, resources_collection, model, assignment_submit # from db import courses_collection2, chat_history_collection, students_collection, faculty_collection, vectors_collection # from chatbot import give_chat_response # from bson import ObjectId # from live_polls import LivePollFeature # import pandas as pd # import plotly.express as px # from dotenv import load_dotenv # import os # from pymongo import MongoClient # from gen_mcqs import generate_mcqs, save_quiz, quizzes_collection, get_student_quiz_score, submit_quiz_answers # from create_course import courses_collection # # from pre_class_analytics import NovaScholarAnalytics # from pre_class_analytics2 import NovaScholarAnalytics # import openai # from openai import OpenAI # import google.generativeai as genai # from google.generativeai import caching # from goals2 import GoalAnalyzer # from openai import OpenAI # import asyncio # import numpy as np # import re # from analytics import derive_analytics, create_embeddings, cosine_similarity # from bs4 import BeautifulSoup # import streamlit.components.v1 as components # from live_chat_feature import display_live_chat_interface # from code_playground import display_code_playground # from urllib.parse import urlparse, parse_qs # from bs4 import BeautifulSoup # from rubrics import display_rubrics_tab # from subjective_test_evaluation import evaluate_subjective_answers, display_evaluation_to_faculty # # Load environment variables # load_dotenv() # MONGO_URI = os.getenv('MONGO_URI') # PERPLEXITY_API_KEY = os.getenv('PERPLEXITY_KEY') # OPENAI_API_KEY = os.getenv('OPENAI_KEY') # client = MongoClient(MONGO_URI) # db = client["novascholar_db"] # polls_collection = db["polls"] # subjective_tests_collection = db["subjective_tests"] # subjective_test_evaluation_collection = db["subjective_test_evaluation"] # assignment_evaluation_collection = db["assignment_evaluation"] # subjective_tests_collection = db["subjective_tests"] # synoptic_store_collection = db["synoptic_store"] # assignments_collection = db["assignments"] # # for implementing Context Caching: # # PROJECT_ID = "novascholar-446709" # # vertexai.init(project=PROJECT_ID, location="us-west4") # def get_current_user(): # if 'current_user' not in st.session_state: # return None # return students_collection.find_one({"_id": st.session_state.user_id}) # # def display_preclass_content(session, student_id, course_id): # """Display pre-class materials for a session""" # # Initialize 'messages' in session_state if it doesn't exist # if 'messages' not in st.session_state: # st.session_state.messages = [] # # Display pre-class materials # materials = list(resources_collection.find({"course_id": course_id, "session_id": session['session_id']})) # st.subheader("Pre-class Materials") # if materials: # for material in materials: # with st.expander(f"{material['file_name']} ({material['material_type'].upper()})"): # file_type = material.get('file_type', 'unknown') # if file_type == 'application/pdf': # st.markdown(f"📑 [Open PDF Document]({material['file_name']})") # if st.button("View PDF", key=f"view_pdf_{material['file_name']}"): # st.text_area("PDF Content", material['text_content'], height=300) # if st.button("Download PDF", key=f"download_pdf_{material['file_name']}"): # st.download_button( # label="Download PDF", # data=material['file_content'], # file_name=material['file_name'], # mime='application/pdf' # ) # if st.button("Mark PDF as Read", key=f"pdf_{material['file_name']}"): # create_notification("PDF marked as read!", "success") # else: # st.info("No pre-class materials uploaded by the faculty.") # st.subheader("Upload Pre-class Material") # # File upload section for students # uploaded_file = st.file_uploader("Upload Material", type=['txt', 'pdf', 'docx']) # if uploaded_file is not None: # with st.spinner("Processing document..."): # file_name = uploaded_file.name # file_content = extract_text_from_file(uploaded_file) # if file_content: # material_type = st.selectbox("Select Material Type", ["pdf", "docx", "txt"]) # if st.button("Upload Material"): # upload_resource(course_id, session['session_id'], file_name, uploaded_file, material_type) # # Search for the newly uploaded resource's _id in resources_collection # resource_id = resources_collection.find_one({"file_name": file_name})["_id"] # create_vector_store(file_content, resource_id) # st.success("Material uploaded successfully!") # st.subheader("Learn the Topic Using Chatbot") # st.write(f"**Session Title:** {session['title']}") # st.write(f"**Description:** {session.get('description', 'No description available.')}") # # Chatbot interface # if prompt := st.chat_input("Ask a question about the session topic"): # if len(st.session_state.messages) >= 20: # st.warning("Message limit (20) reached for this session.") # return # st.session_state.messages.append({"role": "user", "content": prompt}) # # Display User Message # with st.chat_message("user"): # st.markdown(prompt) # # Get response from chatbot # context = "" # for material in materials: # if 'text_content' in material: # context += material['text_content'] + "\n" # response = give_chat_response(student_id, session['session_id'], prompt, session['title'], session.get('description', ''), context) # st.session_state.messages.append({"role": "assistant", "content": response}) # # Display Assistant Response # with st.chat_message("assistant"): # st.markdown(response) # # st.subheader("Your Chat History") # # for message in st.session_state.messages: # # content = message.get("content", "") # Default to an empty string if "content" is not present # # role = message.get("role", "user") # Default to "user" if "role" is not present # # with st.chat_message(role): # # st.markdown(content) # # user = get_current_user() # def display_preclass_content(session, student_id, course_id): # """Display pre-class materials for a session including external resources""" # st.subheader("Pre-class Materials") # print("Session ID is: ", session['session_id']) # # Display uploaded materials # materials = resources_collection.find({"session_id": session['session_id']}) # for material in materials: # file_type = material.get('file_type', 'unknown') # # Handle external resources # if file_type == 'external' or file_type == 'video': # with st.expander(f"📌 {material['file_name']}"): # st.markdown(f"Source: [{material['source_url']}]({material['source_url']})") # if material['material_type'].lower() == 'video': # # Embed YouTube video if it's a YouTube URL # if 'youtube.com' in material['source_url'] or 'youtu.be' in material['source_url']: # video_id = extract_youtube_id(material['source_url']) # if video_id: # st.video(f"https://youtube.com/watch?v={video_id}") # if st.button("View Content", key=f"view_external_{material['_id']}"): # st.text_area("Extracted Content", material['text_content'], height=300) # if st.button("Mark as Read", key=f"external_{material['_id']}"): # create_notification(f"{material['material_type']} content marked as read!", "success") # # Handle traditional file types # else: # with st.expander(f"{material['file_name']} ({material['material_type'].upper()})"): # if file_type == 'application/pdf': # st.markdown(f"📑 [Open PDF Document]({material['file_name']})") # if st.button("View PDF", key=f"view_pdf_{material['_id']}"): # st.text_area("PDF Content", material['text_content'], height=300) # if st.button("Download PDF", key=f"download_pdf_{material['_id']}"): # st.download_button( # label="Download PDF", # data=material['file_content'], # file_name=material['file_name'], # mime='application/pdf' # ) # if st.button("Mark PDF as Read", key=f"pdf_{material['_id']}"): # create_notification("PDF marked as read!", "success") # elif file_type == 'text/plain': # st.markdown(f"📄 [Open Text Document]({material['file_name']})") # if st.button("View Text", key=f"view_text_{material['_id']}"): # st.text_area("Text Content", material['text_content'], height=300) # if st.button("Download Text", key=f"download_text_{material['_id']}"): # st.download_button( # label="Download Text", # data=material['file_content'], # file_name=material['file_name'], # mime='text/plain' # ) # if st.button("Mark Text as Read", key=f"text_{material['_id']}"): # create_notification("Text marked as read!", "success") # elif file_type == 'application/vnd.openxmlformats-officedocument.wordprocessingml.document': # st.markdown(f"📄 [Open Word Document]({material['file_name']})") # if st.button("View Word", key=f"view_word_{material['_id']}"): # st.text_area("Word Content", material['text_content'], height=300) # if st.button("Download Word", key=f"download_word_{material['_id']}"): # st.download_button( # label="Download Word", # data=material['file_content'], # file_name=material['file_name'], # mime='application/vnd.openxmlformats-officedocument.wordprocessingml.document' # ) # if st.button("Mark Word as Read", key=f"word_{material['_id']}"): # create_notification("Word document marked as read!", "success") # elif file_type == 'application/vnd.openxmlformats-officedocument.presentationml.presentation': # st.markdown(f"📊 [Open PowerPoint Presentation]({material['file_name']})") # if st.button("View PowerPoint", key=f"view_pptx_{material['_id']}"): # st.text_area("PowerPoint Content", material['text_content'], height=300) # if st.button("Download PowerPoint", key=f"download_pptx_{material['_id']}"): # st.download_button( # label="Download PowerPoint", # data=material['file_content'], # file_name=material['file_name'], # mime='application/vnd.openxmlformats-officedocument.presentationml.presentation' # ) # if st.button("Mark PowerPoint as Read", key=f"pptx_{material['_id']}"): # create_notification("PowerPoint presentation marked as read!", "success") # # Initialize 'messages' in session_state if it doesn't exist # if 'messages' not in st.session_state: # st.session_state.messages = [] # # Chat input # # Add a check, if materials are available, only then show the chat input # if(st.session_state.user_type == "student"): # if materials: # if prompt := st.chat_input("Ask a question about Pre-class Materials"): # # if len(st.session_state.messages) >= 20: # # st.warning("Message limit (20) reached for this session.") # # return # st.session_state.messages.append({"role": "user", "content": prompt}) # # Display User Message # with st.chat_message("user"): # st.markdown(prompt) # # Get document context # context = "" # print("Session ID is: ", session['session_id']) # materials = resources_collection.find({"session_id": session['session_id']}) # print(materials) # context = "" # vector_data = None # # for material in materials: # # print(material) # context = "" # for material in materials: # resource_id = material['_id'] # print("Supposed Resource ID is: ", resource_id) # vector_data = vectors_collection.find_one({"resource_id": resource_id}) # # print(vector_data) # if vector_data and 'text' in vector_data: # context += vector_data['text'] + "\n" # if not vector_data: # st.error("No Pre-class materials found for this session.") # return # try: # # Generate response using Gemini # # context_prompt = f""" # # Based on the following context, answer the user's question: # # Context: # # {context} # # Question: {prompt} # # Please provide a clear and concise answer based only on the information provided in the context. # # """ # # context_prompt = f""" # # You are a highly intelligent and resourceful assistant capable of synthesizing information from the provided context. # # Context: # # {context} # # Instructions: # # 1. Base your answers primarily on the given context. # # 2. If the answer to the user's question is not explicitly in the context but can be inferred or synthesized from the information provided, do so thoughtfully. # # 3. Only use external knowledge or web assistance when: # # - The context lacks sufficient information, and # # - The question requires knowledge beyond what can be reasonably inferred from the context. # # 4. Clearly state if you are relying on web assistance for any part of your answer. # # 5. Do not respond with a negative. If the answer is not in the context, provide a thoughtful response based on the information available on the web about it. # # Question: {prompt} # # Please provide a clear and comprehensive answer based on the above instructions. # # """ # context_prompt = f""" # You are a highly intelligent and resourceful assistant capable of synthesizing information from the provided context and external sources. # Context: # {context} # Instructions: # 1. Base your answers on the provided context wherever possible. # 2. If the answer to the user's question is not explicitly in the context: # - Use external knowledge or web assistance to provide a clear and accurate response. # 3. Do not respond negatively. If the answer is not in the context, use web assistance or your knowledge to generate a thoughtful response. # 4. Clearly state if part of your response relies on web assistance. # Question: {prompt} # Please provide a clear and comprehensive answer based on the above instructions. # """ # response = model.generate_content(context_prompt) # if not response or not response.text: # st.error("No response received from the model") # return # assistant_response = response.text # # Display Assistant Response # with st.chat_message("assistant"): # st.markdown(assistant_response) # # Build the message # new_message = { # "prompt": prompt, # "response": assistant_response, # "timestamp": datetime.utcnow() # } # st.session_state.messages.append(new_message) # # Update database # try: # chat_history_collection.update_one( # { # "user_id": student_id, # "session_id": session['session_id'] # }, # { # "$push": {"messages": new_message}, # "$setOnInsert": { # "user_id": student_id, # "session_id": session['session_id'], # "timestamp": datetime.utcnow() # } # }, # upsert=True # ) # except Exception as db_error: # st.error(f"Error saving chat history: {str(db_error)}") # except Exception as e: # st.error(f"Error generating response: {str(e)}") # else: # st.subheader("Upload Pre-class Material") # # File upload section for students # uploaded_file = st.file_uploader("Upload Material", type=['txt', 'pdf', 'docx']) # if uploaded_file is not None: # with st.spinner("Processing document..."): # file_name = uploaded_file.name # file_content = extract_text_from_file(uploaded_file) # if file_content: # material_type = st.selectbox("Select Material Type", ["pdf", "docx", "txt"]) # if st.button("Upload Material"): # upload_resource(course_id, session['session_id'], file_name, uploaded_file, material_type) # # print("Resource ID is: ", resource_id) # # Search for the newly uploaded resource's _id in resources_collection # # resource_id = resources_collection.find_one({"file_name": file_name})["_id"] # st.success("Material uploaded successfully!") # # st.experimental_rerun() # # st.subheader("Your Chat History") # if st.button("View Chat History"): # # Initialize chat messages from database # if 'messages' not in st.session_state or not st.session_state.messages: # existing_chat = chat_history_collection.find_one({ # "user_id": student_id, # "session_id": session['session_id'] # }) # if existing_chat and 'messages' in existing_chat: # st.session_state.messages = existing_chat['messages'] # else: # st.session_state.messages = [] # # Display existing chat history # try: # for message in st.session_state.messages: # if 'prompt' in message and 'response' in message: # with st.chat_message("user"): # st.markdown(message["prompt"]) # with st.chat_message("assistant"): # st.markdown(message["response"]) # except Exception as e: # st.error(f"Error displaying chat history: {str(e)}") # st.session_state.messages = [] # if st.session_state.user_type == 'student': # st.subheader("Create a Practice Quiz") # questions = [] # quiz_id = "" # with st.form("create_quiz_form"): # num_questions = st.number_input("Number of Questions", min_value=1, max_value=20, value=2) # submit_quiz = st.form_submit_button("Generate Quiz") # if submit_quiz: # # Get pre-class materials from resources_collection # materials = resources_collection.find({"session_id": session['session_id']}) # context = "" # for material in materials: # if 'text_content' in material: # context += material['text_content'] + "\n" # if not context: # st.error("No pre-class materials found for this session.") # return # # Generate MCQs from context # questions = generate_mcqs(context, num_questions, session['title'], session.get('description', '')) # if questions: # quiz_id = save_quiz(course_id, session['session_id'], "Practice Quiz", questions, student_id) # if quiz_id: # st.success("Quiz saved successfully!") # st.session_state.show_quizzes = True # else: # st.error("Error saving quiz.") # else: # st.error("Error generating questions.") # # if st.button("Attempt Practice Quizzes "): # # quizzes = list(quizzes_collection.find({"course_id": course_id, "session_id": session['session_id'], "user_id": student_id})) # if getattr(st.session_state, 'show_quizzes', False): # # quiz = quizzes_collection.find_one({"course_id": course_id, "session_id": session['session_id'], "user_id": student_id}) # quiz = quizzes_collection.find_one( # {"course_id": course_id, "session_id": session['session_id'], "user_id": student_id}, # sort=[("created_at", -1)] # ) # if not quiz: # st.info("No practice quizzes created.") # else: # with st.expander(f"📝 Practice Quiz", expanded=False): # # Check if student has already taken this quiz # existing_score = get_student_quiz_score(quiz['_id'], student_id) # if existing_score is not None: # st.success(f"Quiz completed! Your score: {existing_score:.1f}%") # # Display correct answers after submission # st.subheader("Quiz Review") # for i, question in enumerate(quiz['questions']): # st.markdown(f"**Question {i+1}:** {question['question']}") # for opt in question['options']: # if opt.startswith(question['correct_option']): # st.markdown(f"✅ {opt}") # else: # st.markdown(f"- {opt}") # else: # # Initialize quiz state for this specific quiz # quiz_key = f"quiz_{quiz['_id']}_student_{student_id}" # if quiz_key not in st.session_state: # st.session_state[quiz_key] = { # 'submitted': False, # 'score': None, # 'answers': {} # } # # If quiz was just submitted, show the results # if st.session_state[quiz_key]['submitted']: # st.success(f"Quiz submitted successfully! Your score: {st.session_state[quiz_key]['score']:.1f}%") # # Reset the quiz state # st.session_state[quiz_key]['submitted'] = False # # Display quiz questions # st.write("Please select your answers:") # # Create a form for quiz submission # form_key = f"quiz_form_{quiz['_id']}_student_{student_id}" # with st.form(key=form_key): # student_answers = {} # for i, question in enumerate(quiz['questions']): # st.markdown(f"**Question {i+1}:** {question['question']}") # options = [opt for opt in question['options']] # # student_answers[str(i)] = st.radio( # # f"Select answer for question {i+1}:", # # options=options, # # key=f"q_{i}", # # index=None # # ) # answer = st.radio( # f"Select answer for question {i+1}:", # options=options, # key=f"{quiz['_id']}_{i}", # Simplify the radio button key # index=None # ) # if answer: # Only add to answers if a selection was made # student_answers[str(i)] = answer # # Submit button # # submitted = st.form_submit_button("Submit Quiz") # print("Before the submit button") # submit_button = st.form_submit_button("Submit Quiz") # print("After the submit button") # if submit_button and student_answers: # print("Clicked the button") # print(student_answers) # correct_answers = 0 # for i, question in enumerate(quiz['questions']): # if student_answers[str(i)] == question['correct_option']: # correct_answers += 1 # score = (correct_answers / len(quiz['questions'])) * 100 # if score is not None: # st.success(f"Quiz submitted successfully! Your score: {score:.1f}%") # st.session_state[quiz_key]['submitted'] = True # st.session_state[quiz_key]['score'] = score # st.session_state[quiz_key]['answers'] = student_answers # # This will trigger a rerun, but now we'll handle it properly # st.rerun() # else: # st.error("Error submitting quiz. Please try again.") # # correct_answers = 0 # # for i, question in enumerate(quiz['questions']): # # if student_answers[str(i)] == question['correct_option']: # # correct_answers += 1 # # score = (correct_answers / len(quiz['questions'])) * 100 # # print(score) # # try: # # quizzes_collection.update_one( # # {"_id": quiz['_id']}, # # {"$push": {"submissions": {"student_id": student_id, "score": score}}} # # ) # # st.success(f"Quiz submitted successfully! Your score: {score:.1f}%") # # except Exception as db_error: # # st.error(f"Error saving submission: {str(db_error)}") # import requests # def get_supported_url_formats(): # """Return a list of supported URL formats for faculty reference""" # return """ # Supported YouTube URL formats: # 1. Standard watch URL: https://www.youtube.com/watch?v=VIDEO_ID # 2. Short URL: https://youtu.be/VIDEO_ID # 3. Embed URL: https://www.youtube.com/embed/VIDEO_ID # 4. Mobile URL: https://m.youtube.com/watch?v=VIDEO_ID # 5. YouTube Shorts: https://www.youtube.com/shorts/VIDEO_ID # You can copy any of these formats from: # - YouTube website (Share button) # - YouTube mobile app (Share button) # - Browser address bar while watching the video # """ # def display_url_guidance(): # """Display guidance for faculty on how to get the correct URL""" # st.info(""" # 📝 How to get the correct YouTube URL: # 1. Go to the YouTube video you want to share # 2. Click the 'Share' button below the video # 3. Copy the URL provided in the share dialog # 4. Paste it here # The URL should start with either 'youtube.com' or 'youtu.be' # """) # def fetch_youtube_video_title(video_url): # """ # Fetch the title of a YouTube video with detailed error handling # """ # api_key = os.getenv("YOUTUBE_API_KEY") # if not api_key: # st.error("⚠ī¸ System Configuration Error: YouTube API key not configured.") # st.write("Please contact technical support for assistance.") # return None # video_id = extract_youtube_id(video_url) # if not video_id: # return None # url = f"https://www.googleapis.com/youtube/v3/videos?id={video_id}&key={api_key}&part=snippet" # try: # response = requests.get(url, timeout=10) # response.raise_for_status() # data = response.json() # if not data.get("items"): # st.error("⚠ī¸ Video not found or might be private.") # st.write(""" # Please check if: # 1. The video is publicly available # 2. The URL is correct # 3. The video hasn't been deleted # """) # return None # return data["items"][0]["snippet"]["title"] # except requests.exceptions.RequestException as e: # if "quotaExceeded" in str(e): # st.error("⚠ī¸ YouTube API quota exceeded.") # st.write(""" # The system has reached its daily limit for video processing. # Please try: # 1. Waiting a few hours # 2. Trying again tomorrow # 3. Contact support if the issue persists # """) # else: # st.error(f"Error fetching video title: {str(e)}") # st.write("Please try again or choose a different video.") # return None # def upload_video_source(course_id, session_id, video_url): # """ # Upload video source and its transcript with comprehensive error handling # """ # if not video_url: # st.error("Please provide a YouTube URL.") # display_url_guidance() # return None # # Display processing message # # with st.spinner("Processing your YouTube video..."): # # Validate video URL # video_id = extract_youtube_id(video_url) # if not video_id: # return None # # Fetch video title # video_title = fetch_youtube_video_title(video_url) # if not video_title: # return None # # Extract transcript # transcript = extract_youtube_transcript(video_url) # if not transcript: # return None # # Create resource document # resource_data = { # "_id": ObjectId(), # "course_id": course_id, # "session_id": session_id, # "file_name": video_title, # "file_type": "video", # "text_content": transcript, # "material_type": "video", # "source_url": video_url, # "uploaded_at": datetime.utcnow(), # "video_id": video_id # } # # Check if resource already exists # existing_resource = resources_collection.find_one({ # "session_id": session_id, # "video_id": video_id # }) # if existing_resource: # st.warning("⚠ī¸ This video has already been added to this session.") # st.write(""" # Options: # 1. Choose a different video # 2. Use the existing video resource # 3. Remove the existing video first if you want to re-add it # """) # return existing_resource["_id"] # try: # # Insert new resource # result = resources_collection.insert_one(resource_data) # resource_id = result.inserted_id # # Update course document # update_result = courses_collection.update_one( # { # "course_id": course_id, # "sessions.session_id": session_id # }, # { # "$push": {"sessions.$.pre_class.resources": resource_id} # } # ) # if update_result.modified_count == 0: # st.error("⚠ī¸ Failed to update course with new resource.") # st.write(""" # The video was processed but couldn't be added to the course. # This might be because: # 1. The course or session ID is invalid # 2. You don't have permission to modify this course # 3. There was a system error # Please try again or contact support if the issue persists. # """) # # Rollback resource insertion # resources_collection.delete_one({"_id": resource_id}) # return None # # Create vector store for the transcript # # create_vector_store(transcript, resource_id) # # Create vector store for the transcript # vector_store_result = create_vector_store(transcript, resource_id) # if not vector_store_result: # st.error("⚠ī¸ Failed to create vector store for the transcript.") # # Rollback insertions # resources_collection.delete_one({"_id": resource_id}) # return None # st.success("✅ Video successfully added to your course!") # st.write(f""" # Added: "{video_title}" # You can now: # 1. Add more videos # 2. Preview the added video # 3. Continue building your course # """) # return resource_id # except Exception as e: # st.error("⚠ī¸ Error uploading video source.") # st.write(f""" # There was an error while saving the video: # {str(e)} # Please: # 1. Try again # 2. Choose a different video # 3. Contact support if the issue persists # """) # return None # def upload_preclass_materials(session_id, course_id): # """Upload pre-class materials and manage external resources for a session""" # st.subheader("Pre-class Materials Management") # # Create tabs for different functionalities # upload_tab, videos_tab, external_tab= st.tabs(["Upload Materials","Upload Video Sources","External Resources"]) # with upload_tab: # # Original file upload functionality # uploaded_file = st.file_uploader("Upload Material", type=['txt', 'pdf', 'docx']) # if uploaded_file is not None: # with st.spinner("Processing document..."): # file_name = uploaded_file.name # file_content = extract_text_from_file(uploaded_file) # if file_content: # material_type = st.selectbox("Select Material Type", ["pdf", "docx", "txt"]) # if st.button("Upload Material"): # upload_resource(course_id, session_id, file_name, uploaded_file, material_type) # st.success("Material uploaded successfully!") # with videos_tab: # # Upload video sources # st.info("Upload video sources for this session.") # video_url = st.text_input("Enter a Youtube Video URL") # if st.button("Upload Video"): # with st.spinner("Processing video source..."): # video_resource_id = upload_video_source(course_id, session_id, video_url) # # if video_resource_id: # # st.success("Video source uploaded successfully!") # with external_tab: # # Fetch and display external resources # session_data = courses_collection.find_one( # {"course_id": course_id, "sessions.session_id": session_id}, # {"sessions.$": 1} # ) # if session_data and session_data.get('sessions'): # session = session_data['sessions'][0] # external = session.get('external_resources', {}) # # Display web articles # if 'readings' in external: # st.subheader("Web Articles and Videos") # for reading in external['readings']: # col1, col2 = st.columns([3, 1]) # with col1: # st.markdown(f"**{reading['title']}**") # st.markdown(f"Type: {reading['type']} | Est. time: {reading['estimated_read_time']}") # st.markdown(f"URL: [{reading['url']}]({reading['url']})") # with col2: # if st.button("Extract Content", key=f"extract_{reading['url']}"): # with st.spinner("Extracting content..."): # content = extract_external_content(reading['url'], reading['type']) # if content: # resource_id = upload_external_resource( # course_id, # session_id, # reading['title'], # content, # reading['type'].lower(), # reading['url'] # ) # st.success("Content extracted and stored successfully!") # # Display books # if 'books' in external: # st.subheader("Recommended Books") # for book in external['books']: # st.markdown(f""" # **{book['title']}** by {book['author']} # - ISBN: {book['isbn']} # - Chapters: {book['chapters']} # """) # # Display additional resources # if 'additional_resources' in external: # st.subheader("Additional Resources") # for resource in external['additional_resources']: # st.markdown(f""" # **{resource['title']}** ({resource['type']}) # - {resource['description']} # - URL: [{resource['url']}]({resource['url']}) # """) # # Display pre-class materials # # Group resources by their types # grouped_resources = defaultdict(list) # materials = resources_collection.find({"session_id": session_id}) # for material in materials: # grouped_resources[material['material_type']].append(material) # # Display grouped resources # for material_type, resources in grouped_resources.items(): # st.markdown(f"##### {material_type.capitalize()} Resources") # for material in resources: # resource_info = f"- **{material['file_name']}** ({material['file_type']})" # if 'source_url' in material: # resource_info += f" - [URL]({material['source_url']})" # st.markdown(resource_info) # def extract_external_content(url, content_type): # """Extract content from external resources based on their type""" # try: # if content_type.lower() == 'video' and 'youtube.com' in url: # return extract_youtube_transcript(url) # else: # return extract_web_article(url) # except Exception as e: # st.error(f"Error extracting content: {str(e)}") # return None # def extract_youtube_transcript(url): # """ # Extract transcript from YouTube videos with detailed error handling # """ # try: # video_id = extract_youtube_id(url) # if not video_id: # return None # # Get transcript with retries # max_retries = 3 # for attempt in range(max_retries): # try: # transcript = YouTubeTranscriptApi.get_transcript(video_id) # # Combine transcript text with proper spacing and punctuation # full_text = '' # for entry in transcript: # text = entry['text'].strip() # if text: # if not full_text.endswith(('.', '!', '?', '..."')): # full_text += '. ' # full_text += text + ' ' # return full_text.strip() # except Exception as e: # if attempt == max_retries - 1: # raise e # continue # except Exception as e: # error_message = str(e) # if "Video unavailable" in error_message: # st.error("⚠ī¸ This video is unavailable or private. Please check if:") # st.write(""" # - The video is set to public or unlisted # - The video hasn't been deleted # - You have the correct URL # """) # elif "Subtitles are disabled" in error_message: # st.error("⚠ī¸ This video doesn't have subtitles/transcript available.") # st.write(""" # Unfortunately, this video cannot be used because: # - It doesn't have closed captions or subtitles # - The creator hasn't enabled transcript generation # Please choose another video that has subtitles available. # You can check if a video has subtitles by: # 1. Playing the video on YouTube # 2. Clicking the 'CC' button in the video player # """) # else: # st.error(f"Could not extract YouTube transcript: {error_message}") # st.write("Please try again or choose a different video.") # return None # def extract_web_article(url): # """Extract text content from web articles""" # try: # headers = { # 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36' # } # response = requests.get(url, headers=headers) # response.raise_for_status() # soup = BeautifulSoup(response.text, 'html.parser') # # Remove unwanted tags # for tag in soup(['script', 'style', 'nav', 'footer', 'header']): # tag.decompose() # # Extract text from paragraphs # paragraphs = soup.find_all('p') # text_content = ' '.join([p.get_text().strip() for p in paragraphs]) # return text_content # except Exception as e: # st.error(f"Could not extract web article content: {str(e)}") # return None # def upload_external_resource(course_id, session_id, title, content, content_type, source_url): # """Upload extracted external resource content to the database""" # resource_data = { # "_id": ObjectId(), # "course_id": course_id, # "session_id": session_id, # "file_name": f"{title} ({content_type})", # "file_type": "external", # "text_content": content, # "material_type": content_type, # "source_url": source_url, # "uploaded_at": datetime.utcnow() # } # # Check if resource already exists # existing_resource = resources_collection.find_one({ # "session_id": session_id, # "source_url": source_url # }) # if existing_resource: # return existing_resource["_id"] # # Insert new resource # resources_collection.insert_one(resource_data) # resource_id = resource_data["_id"] # # Update course document # courses_collection.update_one( # { # "course_id": course_id, # "sessions.session_id": session_id # }, # { # "$push": {"sessions.$.pre_class.resources": resource_id} # } # ) # if content: # create_vector_store(content, resource_id) # return resource_id # def extract_youtube_id(url): # """ # Extract YouTube video ID from various URL formats # """ # if not url: # st.error("Please provide a YouTube URL.") # display_url_guidance() # return None # # Clean the URL # url = url.strip() # # Basic URL validation # if not ('youtube.com' in url or 'youtu.be' in url): # st.error("This doesn't appear to be a YouTube URL.") # st.write(get_supported_url_formats()) # return None # # Try to extract using regex patterns # patterns = [ # r'(?:youtube\.com\/watch\?v=|youtu\.be\/|youtube\.com\/embed\/|youtube\.com\/v\/|youtube\.com\/e\/|youtube\.com\/shorts\/)([^&\n?#]+)', # r'(?:youtube\.com\/(?:[^\/]+\/.+\/|(?:v|e(?:mbed)?)\/|.*[?&]v=)|youtu\.be\/)([^"&?\/\s]{11})' # ] # for pattern in patterns: # match = re.search(pattern, url) # if match: # video_id = match.group(1) # if len(video_id) != 11: # YouTube IDs are always 11 characters # st.error("Invalid YouTube video ID length. Please check your URL.") # display_url_guidance() # return None # return video_id # # If regex fails, try parsing URL components # try: # parsed_url = urlparse(url) # if 'youtube.com' in parsed_url.netloc: # query_params = parse_qs(parsed_url.query) # if 'v' in query_params: # return query_params['v'][0] # elif 'youtu.be' in parsed_url.netloc: # return parsed_url.path.lstrip('/') # except Exception: # pass # # If all extraction methods fail # st.error("Could not extract video ID from the provided URL.") # st.write(get_supported_url_formats()) # return None # def display_live_presentation(session, user_type, course_id): # st.markdown("### Live Presentation") # # Get active presentation # session_data = courses_collection.find_one( # {"course_id": course_id, "sessions.session_id": session['session_id']}, # {"sessions.$": 1} # ) # active_presentation = session_data["sessions"][0].get("in_class", {}).get("active_presentation") # # Faculty Interface # if user_type == 'faculty': # if not active_presentation: # st.markdown(""" # # """, unsafe_allow_html=True) # # URL input section # with st.container(): # ppt_url = st.text_input("🔗 Enter Google Slides Presentation URL", # placeholder="https://docs.google.com/presentation/...") # if ppt_url: # if st.button("â–ļī¸ Activate Presentation", # use_container_width=True): # courses_collection.update_one( # {"course_id": course_id, "sessions.session_id": session['session_id']}, # {"$set": {"sessions.$.in_class.active_presentation": ppt_url}} # ) # st.success("✅ Presentation activated successfully!") # st.rerun() # else: # # Display active presentation # st.markdown("#### đŸŽ¯ Active Presentation") # components.iframe(active_presentation, height=800) # # Deactivate button # st.markdown("
", unsafe_allow_html=True) # if st.button("⏚ī¸ Deactivate Presentation", # type="secondary", # use_container_width=True): # courses_collection.update_one( # {"course_id": course_id, "sessions.session_id": session['session_id']}, # {"$unset": {"sessions.$.in_class.active_presentation": ""}} # ) # st.success("✅ Presentation deactivated successfully!") # st.rerun() # # Student Interface # else: # if active_presentation: # st.markdown("#### đŸŽ¯ Active Presentation") # components.iframe(active_presentation, height=800) # else: # st.info("📝 No active presentations at this time.") # def display_in_class_content(session, user_type, course_id, user_id): # # """Display in-class activities and interactions""" # """Display in-class activities and interactions""" # st.header("In-class Activities") # # Initialize Live Polls feature # live_polls = LivePollFeature() # # Display appropriate interface based on user role # if user_type == 'faculty': # live_polls.display_faculty_interface(session['session_id']) # else: # live_polls.display_student_interface(session['session_id']) # display_live_chat_interface(session, user_id, course_id=course_id) # # Live Presentation Feature # display_live_presentation(session, user_type, course_id) # def generate_random_assignment_id(): # """Generate a random integer ID for assignments""" # return random.randint(100000, 999999) # def display_post_class_content(session, student_id, course_id): # """Display post-class assignments and submissions""" # st.header("Post-class Work") # if st.session_state.user_type == 'faculty': # faculty_id = st.session_state.user_id # st.subheader("Create Subjective Test") # # Create a form for test generation # with st.form("create_subjective_test_form"): # test_title = st.text_input("Test Title") # num_subjective_questions = st.number_input("Number of Subjective Questions", min_value=1, value=5) # generation_method = st.radio( # "Question Generation Method", # ["Generate from Pre-class Materials", "Generate Random Questions"] # ) # generate_test_btn = st.form_submit_button("Generate Test") # # Handle test generation outside the form # if generate_test_btn: # if not test_title: # st.error("Please enter a test title.") # return # context = "" # if generation_method == "Generate from Pre-class Materials": # materials = resources_collection.find({"session_id": session['session_id']}) # for material in materials: # if 'text_content' in material: # context += material['text_content'] + "\n" # with st.spinner("Generating questions and synoptic..."): # try: # # Store generated content in session state to persist between rerenders # questions = generate_questions( # context if context else None, # num_subjective_questions, # session['title'], # session.get('description', '') # ) # if questions: # synoptic = generate_synoptic( # questions, # context if context else None, # session['title'], # num_subjective_questions # ) # if synoptic: # # Store in session state # st.session_state.generated_questions = questions # st.session_state.generated_synoptic = synoptic # st.session_state.test_title = test_title # # Display preview # st.subheader("Preview Subjective Questions and Synoptic") # for i, (q, s) in enumerate(zip(questions, synoptic), 1): # st.markdown(f"**Question {i}:** {q['question']}") # with st.expander(f"View Synoptic {i}"): # st.markdown(s) # # Save button outside the form # if st.button("Save Test"): # test_id = save_subjective_test( # course_id, # session['session_id'], # test_title, # questions # ) # if test_id: # st.success("Subjective test saved successfully!") # else: # st.error("Error saving subjective test.") # else: # st.error("Error generating synoptic answers. Please try again.") # else: # st.error("Error generating questions. Please try again.") # except Exception as e: # st.error(f"An error occurred: {str(e)}") # # Display previously generated test if it exists in session state # elif hasattr(st.session_state, 'generated_questions') and hasattr(st.session_state, 'generated_synoptic'): # st.subheader("Preview Subjective Questions and Synoptic") # for i, (q, s) in enumerate(zip(st.session_state.generated_questions, st.session_state.generated_synoptic), 1): # st.markdown(f"**Question {i}:** {q['question']}") # with st.expander(f"View Synoptic {i}"): # st.markdown(s) # if st.button("Save Test"): # test_id = save_subjective_test( # course_id, # session['session_id'], # st.session_state.test_title, # st.session_state.generated_questions, # ) # if test_id: # st.success("Subjective test saved successfully!") # # Clear session state after successful save # del st.session_state.generated_questions # del st.session_state.generated_synoptic # del st.session_state.test_title # else: # st.error("Error saving subjective test.") # # st.subheader("Create quiz section UI for faculty") # st.subheader("Create Quiz") # questions = [] # with st.form("create_quiz_form"): # quiz_title = st.text_input("Quiz Title") # num_questions = st.number_input("Number of Questions", min_value=1, max_value=20, value=5) # # Option to choose quiz generation method # generation_method = st.radio( # "Question Generation Method", # ["Generate from Pre-class Materials", "Generate Random Questions"] # ) # submit_quiz = st.form_submit_button("Generate Quiz") # if submit_quiz: # if generation_method == "Generate from Pre-class Materials": # # Get pre-class materials from resources_collection # materials = resources_collection.find({"session_id": session['session_id']}) # context = "" # for material in materials: # if 'text_content' in material: # context += material['text_content'] + "\n" # if not context: # st.error("No pre-class materials found for this session.") # return # # Generate MCQs from context # questions = generate_mcqs(context, num_questions, session['title'], session.get('description', '')) # else: # # Generate random MCQs based on session title and description # questions = generate_mcqs(None, num_questions, session['title'], session.get('description', '')) # print(questions) # if questions: # # Preview generated questions # st.subheader("Preview Generated Questions") # for i, q in enumerate(questions, 1): # st.markdown(f"**Question {i}:** {q['question']}") # for opt in q['options']: # st.markdown(f"- {opt}") # st.markdown(f"*Correct Answer: {q['correct_option']}*") # # Save quiz # quiz_id = save_quiz(course_id, session['session_id'], quiz_title, questions, faculty_id) # if quiz_id: # st.success("Quiz saved successfully!") # else: # st.error("Error saving quiz.") # st.subheader("Add Assignment") # with st.form("add_assignment_form"): # title = st.text_input("Assignment Title") # description = st.text_area("Assignment Description") # due_date = st.date_input("Due Date") # submit = st.form_submit_button("Add Assignment") # if submit: # if not title or not description: # st.error("Please fill in all required fields.") # return # due_date = datetime.combine(due_date, datetime.min.time()) # assignment = { # "_id": ObjectId(), # "title": title, # "description": description, # "due_date": due_date, # "course_id": course_id, # "session_id": session['session_id'], # "faculty_id": faculty_id, # "created_at": datetime.utcnow(), # "status": "active", # "submissions": [] # } # assignments_collection.insert_one(assignment) # st.success("Assignment added successfully!") # st.subheader("Existing Assignments") # assignments = assignments_collection.find({ # "session_id": session['session_id'], # "course_id": course_id # }) # for assignment in assignments: # with st.expander(f"📝 {assignment['title']}", expanded=True): # st.markdown(f"**Due Date:** {assignment['due_date'].strftime('%Y-%m-%d')}") # st.markdown(f"**Description:** {assignment['description']}") # total_submissions = len(assignment.get('submissions', [])) # total_students = students_collection.count_documents({ # "enrolled_courses": { # "$elemMatch": {"course_id": course_id} # } # }) # col1, col2, col3 = st.columns(3) # with col1: # st.metric("Total Submissions", total_submissions) # with col2: # submission_rate = (total_submissions / total_students * 100) if total_students > 0 else 0 # st.metric("Submission Rate", f"{submission_rate:.1f}%") # with col3: # st.metric("Pending Submissions", total_students - total_submissions) # # Display evaluation button and status # evaluation_status = st.empty() # eval_button = st.button("View/Generate Evaluations", key=f"eval_{assignment['_id']}") # if eval_button: # st.session_state.show_evaluations = True # st.session_state.current_assignment = assignment['_id'] # # Show evaluation interface in a new container instead of an expander # evaluation_container = st.container() # with evaluation_container: # from assignment_evaluation import display_evaluation_to_faculty # display_evaluation_to_faculty(session['session_id'], student_id, course_id) # else: # Student view # assignments = assignments_collection.find({ # "session_id": session['session_id'], # "course_id": course_id, # "status": "active" # }) # for assignment in assignments: # with st.expander(f"📝 {assignment['title']}", expanded=True): # st.markdown(f"**Due Date:** {assignment['due_date'].strftime('%Y-%m-%d')}") # st.markdown(f"**Description:** {assignment['description']}") # existing_submission = next( # (sub for sub in assignment.get('submissions', []) # if sub['student_id'] == str(student_id)), # None # ) # if existing_submission: # st.success("Assignment submitted!") # st.markdown(f"**Submitted on:** {existing_submission['submitted_at'].strftime('%Y-%m-%d %H:%M')}") # # Show evaluation status and feedback in the same container # evaluation = assignment_evaluation_collection.find_one({ # "assignment_id": assignment['_id'], # "student_id": str(student_id) # }) # if evaluation: # st.markdown("### Evaluation") # st.markdown(evaluation['evaluation']) # else: # st.info("Evaluation pending. Check back later.") # else: # uploaded_file = st.file_uploader( # "Upload your work", # type=['pdf', 'doc', 'docx', 'txt', 'py', 'ipynb', 'ppt', 'pptx'], # key=f"upload_{assignment['_id']}" # ) # if uploaded_file is not None: # if st.button("Submit Assignment", key=f"submit_{assignment['_id']}"): # text_content = extract_text_from_file(uploaded_file) # submission = { # "student_id": str(student_id), # "file_name": uploaded_file.name, # "file_type": uploaded_file.type, # "file_content": uploaded_file.getvalue(), # "text_content": text_content, # "submitted_at": datetime.utcnow() # } # assignments_collection.update_one( # {"_id": assignment['_id']}, # {"$push": {"submissions": submission}} # ) # st.success("Assignment submitted successfully!") # st.rerun() # def display_inclass_analytics(session, course_id): # """Display in-class analytics for faculty""" # st.subheader("In-class Analytics") # # Get all enrolled students count for participation rate calculation # total_students = students_collection.count_documents({ # "enrolled_courses": { # "$elemMatch": {"course_id": course_id} # } # }) # if total_students == 0: # st.warning("No students enrolled in this course.") # return # # Get all polls for this session # polls = polls_collection.find({ # "session_id": session['session_id'] # }) # polls_list = list(polls) # if not polls_list: # st.warning("No polls have been conducted in this session yet.") # return # # Overall Poll Participation Metrics # st.markdown("### Overall Poll Participation") # # Calculate overall participation metrics # total_polls = len(polls_list) # participating_students = set() # poll_participation_data = [] # for poll in polls_list: # respondents = set(poll.get('respondents', [])) # participating_students.update(respondents) # poll_participation_data.append({ # 'Poll Title': poll.get('question', 'Untitled Poll'), # 'Respondents': len(respondents), # 'Participation Rate': (len(respondents) / total_students * 100) # }) # # Display summary metrics # col1, col2, col3 = st.columns(3) # with col1: # st.metric("Total Polls Conducted", total_polls) # with col2: # st.metric("Active Participants", len(participating_students)) # with col3: # avg_participation = sum(p['Participation Rate'] for p in poll_participation_data) / total_polls # st.metric("Average Participation Rate", f"{avg_participation:.1f}%") # # Participation Trend Graph # # st.markdown("### Poll Participation Trends") # # participation_df = pd.DataFrame(poll_participation_data) # # # Create line chart for participation trends # # fig = px.line(participation_df, # # x='Poll Title', # # y='Participation Rate', # # title='Poll Participation Rates Over Time', # # markers=True) # # fig.update_layout( # # xaxis_title="Polls", # # yaxis_title="Participation Rate (%)", # # yaxis_range=[0, 100] # # ) # # st.plotly_chart(fig) # # Individual Poll Results # st.markdown("### Individual Poll Results") # for poll in polls_list: # with st.expander(f"📊 {poll.get('question', 'Untitled Poll')}"): # responses = poll.get('responses', {}) # respondents = poll.get('respondents', []) # # Calculate metrics for this poll # response_count = len(respondents) # participation_rate = (response_count / total_students) * 100 # # Display poll metrics # col1, col2 = st.columns(2) # with col1: # st.metric("Total Responses", response_count) # with col2: # st.metric("Participation Rate", f"{participation_rate:.1f}%") # if responses: # # Create DataFrame for responses # response_df = pd.DataFrame(list(responses.items()), # columns=['Option', 'Votes']) # response_df['Percentage'] = (response_df['Votes'] / response_df['Votes'].sum() * 100).round(1) # # Display response distribution # fig = px.bar(response_df, # x='Option', # y='Votes', # title='Response Distribution', # text='Percentage') # fig.update_traces(texttemplate='%{text:.1f}%', textposition='outside') # st.plotly_chart(fig) # # Display detailed response table # st.markdown("#### Detailed Response Breakdown") # response_df['Percentage'] = response_df['Percentage'].apply(lambda x: f"{x}%") # st.table(response_df) # # Non-participating students # non_participants = list(students_collection.find({ # "courses": course_id, # "_id": {"$nin": respondents} # })) # if non_participants: # st.markdown("#### Students Who Haven't Participated") # non_participant_data = [{ # 'Name': student.get('name', 'Unknown'), # 'SID': student.get('sid', 'Unknown') # } for student in non_participants] # st.table(pd.DataFrame(non_participant_data)) # # Export functionality for participation data # st.markdown("### Export Analytics") # if st.button("Download Poll Analytics Report"): # # Create a more detailed DataFrame for export # export_data = [] # for poll in polls_list: # poll_data = { # 'Poll Question': poll.get('question', 'Untitled'), # 'Total Responses': len(poll.get('respondents', [])), # 'Participation Rate': f"{(len(poll.get('respondents', [])) / total_students * 100):.1f}%" # } # # Add response distribution # for option, votes in poll.get('responses', {}).items(): # poll_data[f"Option: {option}"] = votes # export_data.append(poll_data) # export_df = pd.DataFrame(export_data) # csv = export_df.to_csv(index=False).encode('utf-8') # st.download_button( # "đŸ“Ĩ Download Complete Report", # csv, # "poll_analytics.csv", # "text/csv", # key='download-csv' # ) # def display_postclass_analytics(session, course_id): # """Display post-class analytics for faculty""" # st.subheader("Post-class Analytics") # # Get all assignments for this session # session_data = courses_collection2.find_one( # {"sessions.session_id": session['session_id']}, # {"sessions.$": 1} # ) # if not session_data or 'sessions' not in session_data: # st.warning("No assignments found for this session.") # return # assignments = session_data['sessions'][0].get('post_class', {}).get('assignments', []) # for assignment in assignments: # with st.expander(f"📝 Assignment: {assignment.get('title', 'Untitled')}"): # # Get submission analytics # submissions = assignment.get('submissions', []) # # total_students = students_collection.count_documents({"courses": session['course_id']}) # total_students = students_collection.count_documents({ # "enrolled_courses": { # "$elemMatch": {"course_id": course_id} # } # }) # # Calculate submission metrics # submitted_count = len(submissions) # submission_rate = (submitted_count / total_students) * 100 if total_students > 0 else 0 # # Display metrics # col1, col2, col3 = st.columns(3) # with col1: # st.metric("Submissions Received", submitted_count) # with col2: # st.metric("Submission Rate", f"{submission_rate:.1f}%") # with col3: # st.metric("Pending Submissions", total_students - submitted_count) # # Display submission timeline # if submissions: # submission_dates = [sub.get('submitted_at') for sub in submissions if 'submitted_at' in sub] # if submission_dates: # df = pd.DataFrame(submission_dates, columns=['Submission Date']) # fig = px.histogram(df, x='Submission Date', # title='Submission Timeline', # labels={'Submission Date': 'Date', 'count': 'Number of Submissions'}) # st.plotly_chart(fig) # # Display submission status breakdown # status_counts = { # 'pending': total_students - submitted_count, # 'submitted': submitted_count, # 'late': len([sub for sub in submissions if sub.get('is_late', False)]) # } # st.markdown("### Submission Status Breakdown") # status_df = pd.DataFrame(list(status_counts.items()), # columns=['Status', 'Count']) # st.bar_chart(status_df.set_index('Status')) # # List of students who haven't submitted # if status_counts['pending'] > 0: # st.markdown("### Students with Pending Submissions") # # submitted_ids = [sub.get('student_id') for sub in submissions] # submitted_ids = [ObjectId(sub.get('student_id')) for sub in submissions] # print(submitted_ids) # pending_students = students_collection.find({ # "enrolled_courses.course_id": course_id, # "_id": {"$nin": submitted_ids} # }) # print(pending_students) # for student in pending_students: # st.markdown(f"- {student.get('full_name', 'Unknown Student')} (SID: {student.get('SID', 'Unknown SID')})") # def get_chat_history(user_id, session_id): # query = { # "user_id": ObjectId(user_id), # "session_id": session_id, # "timestamp": {"$lte": datetime.utcnow()} # } # result = chat_history_collection.find(query) # return list(result) # def get_response_from_llm(raw_data): # messages = [ # { # "role": "system", # "content": "You are an AI that refines raw analytics data into actionable insights for faculty reports." # }, # { # "role": "user", # "content": f""" # Based on the following analytics data, refine and summarize the insights: # Raw Data: # {raw_data} # Instructions: # 1. Group similar topics together under appropriate categories. # 2. Remove irrelevant or repetitive entries. # 3. Summarize the findings into actionable insights. # 4. Provide concise recommendations for improvement based on the findings. # Output: # Provide a structured response with the following format: # {{ # "Low Engagement Topics": ["List of Topics"], # "Frustration Areas": ["List of areas"], # "Recommendations": ["Actionable recommendations"], # }} # """ # } # ] # try: # client = OpenAI(api_key=OPENAI_API_KEY) # response = client.chat.completions.create( # model="gpt-4o-mini", # messages=messages, # temperature=0.2 # ) # content = response.choices[0].message.content # return json.loads(content) # except Exception as e: # st.error(f"Error generating response: {str(e)}") # return None # import typing_extensions as typing # from typing import Union, List, Dict # # class Topics(typing.TypedDict): # # overarching_theme: List[Dict[str, Union[str, List[Dict[str, Union[str, List[str]]]]]]] # # indirect_topics: List[Dict[str, str]] # def extract_topics_from_materials(session): # """Extract topics from pre-class materials""" # materials = resources_collection.find({"session_id": session['session_id']}) # texts = "" # if materials: # for material in materials: # if 'text_content' in material: # text = material['text_content'] # texts += text + "\n" # else: # st.warning("No text content found in the material.") # return # else: # st.error("No pre-class materials found for this session.") # return # if texts: # context_prompt = f""" # Task: Extract Comprehensive Topics in a List Format # You are tasked with analyzing the provided text content and extracting a detailed, flat list of topics. # Instructions: # Identify All Topics: Extract a comprehensive list of all topics, subtopics, and indirect topics present in the provided text content. This list should include: # Overarching themes # Main topics # Subtopics and their sub-subtopics # Indirectly related topics # Flat List Format: Provide a flat list where each item is a topic. Ensure topics at all levels (overarching, main, sub, sub-sub, indirect) are represented as individual entries in the list. # Be Exhaustive: Ensure the response captures every topic, subtopic, and indirectly related concept comprehensively. # Output Requirements: # Use this structure: # {{ # "topics": [ # "Topic 1", # "Topic 2", # "Topic 3", # ... # ] # }} # Do Not Include: Do not include backticks, hierarchical structures, or the word 'json' in your response. # Content to Analyze: # {texts} # """ # try: # # response = model.generate_content(context_prompt, generation_config=genai.GenerationConfig(response_mime_type="application/json", response_schema=list[Topics])) # response = model.generate_content(context_prompt, generation_config=genai.GenerationConfig(temperature=0.3)) # if not response or not response.text: # st.error("Error extracting topics from materials.") # return # topics = response.text # return topics # except Exception as e: # st.error(f"Error extracting topics: {str(e)}") # return None # else: # st.error("No text content found in the pre-class materials.") # return None # def convert_json_to_dict(json_str): # try: # return json.loads(json_str) # except Exception as e: # st.error(f"Error converting JSON to dictionary. {str(e)}") # return None # # Load topics from a JSON file # # topics = [] # # with open(r'topics.json', 'r') as file: # # topics = json.load(file) # def get_preclass_analytics(session, course_id): # # Earlier Code: # # """Get all user_ids from chat_history collection where session_id matches""" # # user_ids = chat_history_collection.distinct("user_id", {"session_id": session['session_id']}) # # print(user_ids) # # session_id = session['session_id'] # # all_chat_histories = [] # # for user_id in user_ids: # # result = get_chat_history(user_id, session_id) # # if result: # # for record in result: # # chat_history = { # # "user_id": record["user_id"], # # "session_id": record["session_id"], # # "messages": record["messages"] # # } # # all_chat_histories.append(chat_history) # # else: # # st.warning("No chat history found for this session.") # # # Pass the pre-class materials content to the analytics engine # # topics = extract_topics_from_materials(session) # # # dict_topics = convert_json_to_dict(topics) # # print(topics) # # # # Use the 1st analytics engine # # # analytics_engine = NovaScholarAnalytics(all_topics_list=topics) # # # # extracted_topics = analytics_engine._extract_topics(None, topics) # # # # print(extracted_topics) # # # results = analytics_engine.process_chat_history(all_chat_histories) # # # faculty_report = analytics_engine.generate_faculty_report(results) # # # print(faculty_report) # # # # Pass this Faculty Report to an LLM model for refinements and clarity # # # refined_report = get_response_from_llm(faculty_report) # # # return refined_report # # # Use the 2nd analytice engine (using LLM): # fallback_analytics = { # "topic_insights": [], # "student_insights": [], # "recommended_actions": [ # { # "action": "Review analytics generation process", # "priority": "high", # "target_group": "system_administrators", # "reasoning": "Analytics generation failed", # "expected_impact": "Restore analytics functionality" # } # ], # "course_health": { # "overall_engagement": 0, # "critical_topics": [], # "class_distribution": { # "high_performers": 0, # "average_performers": 0, # "at_risk": 0 # } # }, # "intervention_metrics": { # "immediate_attention_needed": [], # "monitoring_required": [] # } # } # # analytics_generator = NovaScholarAnalytics() # # analytics2 = analytics_generator.generate_analytics(all_chat_histories, topics) # # # enriched_analytics = analytics_generator._enrich_analytics(analytics2) # # print("Analytics is: ", analytics2) # # if analytics2 == fallback_analytics: # # return None # # else: # # return analytics2 # # # print(json.dumps(analytics, indent=2)) # # New Code: # # Debug print 1: Check session # print("Starting get_preclass_analytics with session:", session['session_id']) # user_ids = chat_history_collection.distinct("user_id", {"session_id": session['session_id']}) # # Debug print 2: Check user_ids # print("Found user_ids:", user_ids) # all_chat_histories = [] # for user_id in user_ids: # result = get_chat_history(user_id, session['session_id']) # # Debug print 3: Check each chat history result # print(f"Chat history for user {user_id}:", "Found" if result else "Not found") # if result: # for record in result: # chat_history = { # "user_id": record["user_id"], # "session_id": record["session_id"], # "messages": record["messages"] # } # all_chat_histories.append(chat_history) # # Debug print 4: Check chat histories # print("Total chat histories collected:", len(all_chat_histories)) # # Extract topics with debug print # topics = extract_topics_from_materials(session) # # Debug print 5: Check topics # print("Extracted topics:", topics) # if not topics: # print("Topics extraction failed") # Debug print 6 # return None # analytics_generator = NovaScholarAnalytics() # analytics2 = analytics_generator.generate_analytics(all_chat_histories, topics) # # Debug print 7: Check analytics # print("Generated analytics:", analytics2) # if analytics2 == fallback_analytics: # print("Fallback analytics returned") # Debug print 8 # return None # else: # try: # courses_collection.update_one( # {"course_id": course_id, "sessions.session_id": session['session_id']}, # {"$set": {"sessions.$.pre_class.analytics": analytics2}} # ) # except Exception as e: # print("Error storing analytics:", str(e)) # return analytics2 # # Load Analytics from a JSON file # # analytics = [] # # with open(r'new_analytics2.json', 'r') as file: # # analytics = json.load(file) # def display_preclass_analytics2(session, course_id): # # Earlier Code: # # Initialize or get analytics data from session state # # if 'analytics_data' not in st.session_state: # # st.session_state.analytics_data = get_preclass_analytics(session) # # analytics = st.session_state.analytics_data # # print(analytics) # # New Code: # # Initialize or get analytics data from session state # if 'analytics_data' not in st.session_state: # # Add debug prints # analytics_data = get_preclass_analytics(session, course_id) # if analytics_data is None: # st.info("Fetching new analytics data...") # if analytics_data is None: # st.error("Failed to generate analytics. Please check the following:") # st.write("1. Ensure pre-class materials contain text content") # st.write("2. Verify chat history exists for this session") # st.write("3. Check if topic extraction was successful") # return # st.session_state.analytics_data = analytics_data # analytics = st.session_state.analytics_data # # Validate analytics data structure # if not isinstance(analytics, dict): # st.error(f"Invalid analytics data type: {type(analytics)}") # return # required_keys = ["topic_wise_insights", "ai_recommended_actions", "student_analytics"] # missing_keys = [key for key in required_keys if key not in analytics] # if missing_keys: # st.error(f"Missing required keys in analytics data: {missing_keys}") # return # # Initialize topic indices only if we have valid data # if 'topic_indices' not in st.session_state: # try: # st.session_state.topic_indices = list(range(len(analytics["topic_wise_insights"]))) # except Exception as e: # st.error(f"Error creating topic indices: {str(e)}") # st.write("Analytics data structure:", analytics) # return # # Enhanced CSS for better styling and interactivity # st.markdown(""" # # """, unsafe_allow_html=True) # # Topic-wise Analytics Section # st.markdown('

Topic-wise Analytics

', unsafe_allow_html=True) # # Initialize session state for topic expansion # if 'expanded_topic' not in st.session_state: # st.session_state.expanded_topic = None # # Store topic indices in session state if not already done # if 'topic_indices' not in st.session_state: # st.session_state.topic_indices = list(range(len(analytics["topic_wise_insights"]))) # if st.session_state.topic_indices: # st.markdown('
', unsafe_allow_html=True) # for idx in st.session_state.topic_indices: # topic = analytics["topic_wise_insights"][idx] # topic_id = f"topic_{idx}" # # Create clickable header # col1, col2 = st.columns([3, 1]) # with col1: # if st.button( # topic["topic"], # key=f"topic_button_{idx}", # use_container_width=True, # type="secondary" # ): # st.session_state.expanded_topic = topic_id if st.session_state.expanded_topic != topic_id else None # with col2: # st.markdown(f""" #
# {topic["struggling_percentage"]*100:.1f}% Struggling #
# """, unsafe_allow_html=True) # # Show content if topic is expanded # if st.session_state.expanded_topic == topic_id: # st.markdown(f""" #
#
Key Issues
# #
Key Misconceptions
# #
# """, unsafe_allow_html=True) # st.markdown('
', unsafe_allow_html=True) # # AI Recommendations Section # st.markdown('

AI-Powered Recommendations

', unsafe_allow_html=True) # st.markdown('
', unsafe_allow_html=True) # for idx, rec in enumerate(analytics["ai_recommended_actions"]): # st.markdown(f""" #
#

# Recommendation {idx + 1} # {rec["priority"]} #

#

{rec["action"]}

#

Reason: {rec["reasoning"]}

#

Expected Outcome: {rec["expected_outcome"]}

#
# """, unsafe_allow_html=True) # st.markdown('
', unsafe_allow_html=True) # # Student Analytics Section # st.markdown('

Student Analytics

', unsafe_allow_html=True) # # Filters # with st.container(): # # st.markdown('
', unsafe_allow_html=True) # col1, col2, col3 = st.columns(3) # with col1: # concept_understanding = st.selectbox( # "Filter by Understanding", # ["All", "Strong", "Moderate", "Needs Improvement"] # ) # with col2: # participation_level = st.selectbox( # "Filter by Participation", # ["All", "High (>80%)", "Medium (50-80%)", "Low (<50%)"] # ) # with col3: # struggling_topic = st.selectbox( # "Filter by Struggling Topic", # ["All"] + list(set([topic for student in analytics["student_analytics"] # for topic in student["struggling_topics"]])) # ) # # st.markdown('
', unsafe_allow_html=True) # # Display student metrics in a grid # st.markdown('
', unsafe_allow_html=True) # for student in analytics["student_analytics"]: # # Apply filters # if (concept_understanding != "All" and # student["engagement_metrics"]["concept_understanding"].replace("_", " ").title() != concept_understanding): # continue # participation = student["engagement_metrics"]["participation_level"] * 100 # if participation_level != "All": # if participation_level == "High (>80%)" and participation <= 80: # continue # elif participation_level == "Medium (50-80%)" and (participation < 50 or participation > 80): # continue # elif participation_level == "Low (<50%)" and participation >= 50: # continue # if struggling_topic != "All" and struggling_topic not in student["struggling_topics"]: # continue # st.markdown(f""" #
#
# Student {student["student_id"][-6:]} #
#
#
#
Participation
#
{student["engagement_metrics"]["participation_level"]*100:.1f}%
#
#
#
Understanding
#
{student["engagement_metrics"]["concept_understanding"].replace('_', ' ').title()}
#
#
#
Struggling Topics:
#
{", ".join(student["struggling_topics"]) if student["struggling_topics"] else "None"}
#
#
# {student["personalized_recommendation"]} #
#
#
# """, unsafe_allow_html=True) # st.markdown('
', unsafe_allow_html=True) # def reset_analytics_state(): # """ # Helper function to reset the analytics state when needed # (e.g., when loading a new session or when data needs to be refreshed) # """ # if 'analytics_data' in st.session_state: # del st.session_state.analytics_data # if 'expanded_topic' in st.session_state: # del st.session_state.expanded_topic # if 'topic_indices' in st.session_state: # del st.session_state.topic_indice # def display_session_analytics(session, course_id): # """Display session analytics for faculty""" # st.header("Session Analytics") # # Display Pre-class Analytics # display_preclass_analytics2(session, course_id) # # Display In-class Analytics # display_inclass_analytics(session, course_id) # # Display Post-class Analytics # display_postclass_analytics(session, course_id) # # def upload_preclass_materials(session_id, course_id): # # """Upload pre-class materials for a session""" # # st.subheader("Upload Pre-class Materials") # # # File upload section # # uploaded_file = st.file_uploader("Upload Material", type=['txt', 'pdf', 'docx']) # # if uploaded_file is not None: # # with st.spinner("Processing document..."): # # file_name = uploaded_file.name # # file_content = extract_text_from_file(uploaded_file) # # if file_content: # # material_type = st.selectbox("Select Material Type", ["pdf", "docx", "txt"]) # # if st.button("Upload Material"): # # upload_resource(course_id, session_id, file_name, uploaded_file, material_type) # # # Search for the newly uploaded resource's _id in resources_collection # # resource_id = resources_collection.find_one({"file_name": file_name})["_id"] # # create_vector_store(file_content, resource_id) # # st.success("Material uploaded successfully!") # # # Display existing materials # # materials = resources_collection.find({"course_id": course_id, "session_id": session_id}) # # for material in materials: # # st.markdown(f""" # # * **{material['file_name']}** ({material['material_type']}) # # Uploaded on: {material['uploaded_at'].strftime('%Y-%m-%d %H:%M')} # # """) # def display_quiz_tab(student_id, course_id, session_id): # """Display quizzes for students""" # st.header("Course Quizzes") # # Get available quizzes for this session # quizzes = quizzes_collection.find({ # "course_id": course_id, # "session_id": session_id, # "status": "active" # }) # quizzes = list(quizzes) # if not quizzes: # st.info("No quizzes available for this session.") # return # for quiz in quizzes: # with st.expander(f"📝 {quiz['title']}", expanded=True): # # Check if student has already taken this quiz # existing_score = get_student_quiz_score(quiz['_id'], student_id) # if existing_score is not None: # st.success(f"Quiz completed! Your score: {existing_score:.1f}%") # # Display correct answers after submission # st.subheader("Quiz Review") # for i, question in enumerate(quiz['questions']): # st.markdown(f"**Question {i+1}:** {question['question']}") # for opt in question['options']: # if opt.startswith(question['correct_option']): # st.markdown(f"✅ {opt}") # else: # st.markdown(f"- {opt}") # else: # # Display quiz questions # st.write("Please select your answers:") # # Create a form for quiz submission # with st.form(f"quiz_form_{quiz['_id']}"): # student_answers = {} # for i, question in enumerate(quiz['questions']): # st.markdown(f"**Question {i+1}:** {question['question']}") # options = [opt for opt in question['options']] # student_answers[str(i)] = st.radio( # f"Select answer for question {i+1}:", # options=options, # key=f"q_{quiz['_id']}_{i}" # ) # # Submit button # if st.form_submit_button("Submit Quiz"): # print(student_answers) # score = submit_quiz_answers(quiz['_id'], student_id, student_answers) # if score is not None: # st.success(f"Quiz submitted successfully! Your score: {score:.1f}%") # st.rerun() # Refresh to show results # else: # st.error("Error submitting quiz. Please try again.") # def display_session_content(student_id, course_id, session, username, user_type): # st.title(f"{session['title']}") # # Check if the date is a string or a datetime object # if isinstance(session['date'], str): # # Convert date string to datetime object # session_date = datetime.fromisoformat(session['date']) # else: # session_date = session['date'] # course_name = courses_collection.find_one({"course_id": course_id})['title'] # st.markdown(f"**Date:** {format_datetime(session_date)}") # st.markdown(f"**Course Name:** {course_name}") # # Find the course_id of the session in # if user_type == 'student': # # Create all tabs at once for students # tabs = st.tabs([ # "Pre-class Work", # "In-class Work", # "Post-class Work", # "Quizzes", # "Subjective Tests", # "Group Work", # "End Terms", # "Code Playground" # ]) # if len(tabs) <= 8: # with tabs[0]: # display_preclass_content(session, student_id, course_id) # with tabs[1]: # display_in_class_content(session, user_type, course_id, user_type) # with tabs[2]: # display_post_class_content(session, student_id, course_id) # with tabs[3]: # display_quiz_tab(student_id, course_id, session['session_id']) # with tabs[4]: # display_subjective_test_tab(student_id, course_id, session['session_id']) # with tabs[5]: # st.subheader("Group Work") # st.info("Group work content will be available soon.") # with tabs[6]: # st.subheader("End Terms") # st.info("End term content will be available soon.") # with tabs[7]: # # st.subheader("Code Playground") # display_code_playground(session, student_id, course_id) # else: # st.error("Error creating tabs. Please try again.") # else: # faculty user # # Create all tabs at once for faculty # tabs = st.tabs([ # "Pre-class Work", # "In-class Work", # "Post-class Work", # "Pre-class Analytics", # "In-class Analytics", # "Post-class Analytics", # "Rubrics", # "End Terms", # "Evaluate Subjective Tests" # ]) # with tabs[0]: # upload_preclass_materials(session['session_id'], course_id) # with tabs[1]: # display_in_class_content(session, user_type, course_id, user_type) # with tabs[2]: # display_post_class_content(session, student_id, course_id) # with tabs[3]: # display_preclass_analytics2(session, course_id) # with tabs[4]: # display_inclass_analytics(session, course_id) # with tabs[5]: # display_postclass_analytics(session, course_id) # with tabs[6]: # display_rubrics_tab(session, course_id) # with tabs[7]: # st.subheader("End Terms") # st.info("End term content will be available soon.") # with tabs[8]: # New tab for evaluation # display_evaluation_to_faculty(session['session_id'], student_id, course_id) # def parse_model_response(response_text): # """Enhanced parser for model responses with better error handling. # Args: # response_text (str): Raw response text from the model # Returns: # dict or list: Parsed response object # Raises: # ValueError: If parsing fails # """ # import json # import ast # import re # # Remove markdown formatting and whitespace # cleaned_text = re.sub(r'```[a-zA-Z]*\n', '', response_text) # cleaned_text = cleaned_text.replace('```', '').strip() # # Try multiple parsing methods # parsing_methods = [ # # Method 1: Direct JSON parsing # lambda x: json.loads(x), # # Method 2: AST literal evaluation # lambda x: ast.literal_eval(x), # # Method 3: Extract and parse content between curly braces # lambda x: json.loads(re.search(r'\{.*\}', x, re.DOTALL).group()), # # Method 4: Extract and parse content between square brackets # lambda x: json.loads(re.search(r'\[.*\]', x, re.DOTALL).group()), # # Method 5: Try to fix common JSON formatting issues and parse # lambda x: json.loads(x.replace("'", '"').replace('\n', '\\n')) # ] # last_error = None # for parse_method in parsing_methods: # try: # result = parse_method(cleaned_text) # if result: # Ensure we have actual content # return result # except Exception as e: # last_error = e # continue # raise ValueError(f"Could not parse the model's response: {last_error}") # def generate_questions(context, num_questions, session_title, session_description): # """Generate subjective questions based on context or session details""" # try: # # Construct the prompt # prompt = f"""You are a professional educator creating {num_questions} subjective questions. # Topic: {session_title} # Description: {session_description} # {'Context: ' + context if context else ''} # Generate exactly {num_questions} questions in this specific format: # [ # {{"question": "Write your first question here?"}}, # {{"question": "Write your second question here?"}} # ] # Requirements: # 1. Questions must require detailed explanations # 2. Focus on critical thinking and analysis # 3. Ask for specific examples or case studies # 4. Questions should test deep understanding # IMPORTANT: Return ONLY the JSON array. Do not include any additional text or explanations. # """ # # Generate response # response = model.generate_content(prompt) # questions = parse_model_response(response.text) # # Validate response # if not isinstance(questions, list): # raise ValueError("Generated content is not a list") # if len(questions) != num_questions: # raise ValueError(f"Generated {len(questions)} questions instead of {num_questions}") # # Validate each question # for q in questions: # if not isinstance(q, dict) or 'question' not in q: # raise ValueError("Invalid question format") # return questions # except Exception as e: # print(f"Error generating questions: {str(e)}") # return None # def generate_synoptic(questions, context, session_title, num_questions): # """Generate synoptic answers for the questions with improved error handling and response validation. # Args: # questions (list): List of question dictionaries # context (str): Additional context for answer generation # session_title (str): Title of the session # num_questions (int): Expected number of questions # Returns: # list: List of synoptic answers or None if generation fails # """ # try: # # First, let's validate our input # if not questions or not isinstance(questions, list): # raise ValueError("Questions must be provided as a non-empty list") # # Format questions for better prompt clarity # formatted_questions = "\n".join( # f"{i+1}. {q['question']}" # for i, q in enumerate(questions) # ) # # Construct a more structured prompt # prompt = f"""You are a subject matter expert creating detailed model answers for {num_questions} questions about {session_title}. # Here are the questions: # {formatted_questions} # {f'Additional context: {context}' if context else ''} # Please provide {num_questions} comprehensive answers following this JSON format: # {{ # "answers": [ # {{ # "answer": "Your detailed answer for question 1...", # "key_points": ["Point 1", "Point 2", "Point 3"] # }}, # {{ # "answer": "Your detailed answer for question 2...", # "key_points": ["Point 1", "Point 2", "Point 3"] # }} # ] # }} # Requirements for each answer: # 1. Minimum 150 words # 2. Include specific examples and evidence # 3. Reference key concepts and terminology # 4. Demonstrate critical analysis # 5. Structure with clear introduction, body, and conclusion # IMPORTANT: Return ONLY the JSON object with the answers array. No additional text. # """ # # Generate response # response = model.generate_content(prompt) # # Parse and validate the response # parsed_response = parse_model_response(response.text) # # Additional validation of parsed response # if not isinstance(parsed_response, (dict, list)): # raise ValueError("Response must be a dictionary or list") # # Handle both possible valid response formats # if isinstance(parsed_response, dict): # answers = parsed_response.get('answers', []) # else: # If it's a list # answers = parsed_response # # Validate answer count # if len(answers) != num_questions: # raise ValueError(f"Expected {num_questions} answers, got {len(answers)}") # # Extract just the answer texts for consistency with existing code # final_answers = [] # for ans in answers: # if isinstance(ans, dict): # answer_text = ans.get('answer', '') # key_points = ans.get('key_points', []) # formatted_answer = f"{answer_text}\n\nKey Points:\n" + "\n".join(f"â€ĸ {point}" for point in key_points) # final_answers.append(formatted_answer) # else: # final_answers.append(str(ans)) # # Final validation of the answers # for i, answer in enumerate(final_answers): # if not answer or len(answer.split()) < 50: # Basic length check # raise ValueError(f"Answer {i+1} is too short or empty") # # Save the synoptic to the synoptic_store collection # synoptic_data = { # "session_title": session_title, # "questions": questions, # "synoptic": final_answers, # "created_at": datetime.utcnow() # } # synoptic_store_collection.insert_one(synoptic_data) # return final_answers # except Exception as e: # # Log the error for debugging # print(f"Error in generate_synoptic: {str(e)}") # print(f"Response text: {response.text if 'response' in locals() else 'No response generated'}") # return None # def save_subjective_test(course_id, session_id, title, questions): # """Save subjective test to database with proper ID handling""" # try: # # Ensure proper string format for IDs # course_id = str(course_id) # session_id = str(session_id) # # Format questions # formatted_questions = [] # for q in questions: # formatted_question = { # "question": q["question"], # "expected_points": [], # "difficulty_level": "medium", # "suggested_time": "5 minutes" # } # formatted_questions.append(formatted_question) # test_data = { # "course_id": course_id, # "session_id": session_id, # "title": title, # "questions": formatted_questions, # "created_at": datetime.utcnow(), # "status": "active", # "submissions": [] # } # result = subjective_tests_collection.insert_one(test_data) # return str(result.inserted_id) # except Exception as e: # print(f"Error saving test: {e}") # return None # def submit_subjective_test(test_id, student_id, answers): # """Submit test answers with proper ID handling""" # try: # # Ensure IDs are strings # test_id = str(test_id) # student_id = str(student_id) # # Create submission document # submission = { # "student_id": student_id, # "answers": answers, # "submitted_at": datetime.utcnow(), # "status": "submitted" # } # # Update test document with new submission # result = subjective_tests_collection.update_one( # {"_id": ObjectId(test_id)}, # {"$push": {"submissions": submission}} # ) # return result.modified_count > 0 # except Exception as e: # print(f"Error submitting test: {e}") # return False # def display_subjective_test_tab(student_id, course_id, session_id): # """Display subjective tests and results for students""" # st.header("Subjective Tests") # try: # subjective_tests = list(subjective_tests_collection.find({ # "course_id": course_id, # "session_id": session_id, # "status": "active" # })) # if not subjective_tests: # st.info("No subjective tests available for this session.") # return # # Create tabs for Tests and Results # test_tab, results_tab = st.tabs(["Available Tests", "Test Results"]) # with test_tab: # for test in subjective_tests: # with st.expander(f"📝 {test['title']}", expanded=True): # # Check for existing submission # existing_submission = next( # (sub for sub in test.get('submissions', []) # if sub['student_id'] == str(student_id)), # None # ) # if existing_submission: # st.success("Test completed! Your answers have been submitted.") # st.subheader("Your Answers") # for i, ans in enumerate(existing_submission['answers']): # st.markdown(f"**Question {i+1}:** {test['questions'][i]['question']}") # st.markdown(f"**Your Answer:** {ans}") # st.markdown("---") # else: # st.write("Please write your answers:") # with st.form(key=f"subjective_test_form_{test['_id']}"): # student_answers = [] # for i, question in enumerate(test['questions']): # st.markdown(f"**Question {i+1}:** {question['question']}") # answer = st.text_area( # "Your answer:", # key=f"q_{test['_id']}_{i}", # height=200 # ) # student_answers.append(answer) # if st.form_submit_button("Submit Test"): # if all(answer.strip() for answer in student_answers): # success = submit_subjective_test( # test['_id'], # str(student_id), # student_answers # ) # if success: # st.success("Test submitted successfully!") # st.rerun() # else: # st.error("Error submitting test. Please try again.") # else: # st.error("Please answer all questions before submitting.") # with results_tab: # # Display results for completed tests # completed_tests = [ # test for test in subjective_tests # if any(sub['student_id'] == str(student_id) for sub in test.get('submissions', [])) # ] # if not completed_tests: # st.info("You haven't completed any tests yet.") # return # # Create a selectbox for choosing which test results to view # test_options = { # f"{test['title']} (Submitted: {next(sub['submitted_at'].strftime('%Y-%m-%d') for sub in test['submissions'] if sub['student_id'] == str(student_id))})" # : test['_id'] # for test in completed_tests # } # selected_test = st.selectbox( # "Select a test to view results:", # options=list(test_options.keys()) # ) # if selected_test: # test_id = test_options[selected_test] # display_test_results(test_id, student_id) # except Exception as e: # st.error("An error occurred while loading the tests. Please try again later.") # print(f"Error in display_subjective_test_tab: {str(e)}") # def display_test_results(test_id, student_id): # """ # Display test results and analysis for a student # Args: # test_id: ObjectId or str of the test # student_id: str of the student ID # """ # try: # # Fetch analysis from evaluation collection # analysis = subjective_test_evaluation_collection.find_one({ # "test_id": test_id, # "student_id": str(student_id) # }) # if not analysis: # st.info("Analysis will be available soon. Please check back later.") # return # st.header("Test Analysis") # # Display overall evaluation summary if available # if "overall_summary" in analysis: # with st.expander("Overall Performance Summary", expanded=True): # st.markdown(analysis["overall_summary"]) # # Display individual question evaluations # st.subheader("Question-wise Analysis") # for eval_item in analysis.get('evaluations', []): # with st.expander(f"Question {eval_item['question_number']}", expanded=True): # st.markdown("**Question:**") # st.markdown(eval_item['question']) # st.markdown("**Your Answer:**") # st.markdown(eval_item['answer']) # st.markdown("**Evaluation:**") # st.markdown(eval_item['evaluation']) # # Extract and display score if available # if "Score:" in eval_item['evaluation']: # score_line = next((line for line in eval_item['evaluation'].split('\n') if "Score:" in line), None) # if score_line: # score = score_line.split("Score:")[1].strip() # st.metric("Score", score) # # Display improvement points if available # if "Key Areas for Improvement" in eval_item['evaluation']: # st.markdown("**Areas for Improvement:**") # improvement_section = eval_item['evaluation'].split("Key Areas for Improvement")[1] # points = [point.strip('- ').strip() for point in improvement_section.split('\n') if point.strip().startswith('-')] # for point in points: # if point: # Only display non-empty points # st.markdown(f"â€ĸ {point}") # # Display evaluation timestamp # if "evaluated_at" in analysis: # st.caption(f"Analysis generated on: {analysis['evaluated_at'].strftime('%Y-%m-%d %H:%M:%S UTC')}") # except Exception as e: # st.error("An error occurred while loading the analysis. Please try again later.") # print(f"Error in display_test_results: {str(e)}") from collections import defaultdict import json import random import requests import streamlit as st from datetime import datetime, timedelta from youtube_transcript_api import YouTubeTranscriptApi from utils.helpers import display_progress_bar, create_notification, format_datetime from file_upload_vectorize import ( upload_resource, extract_text_from_file, create_vector_store, resources_collection, model, assignment_submit, ) from db import ( courses_collection2, chat_history_collection, students_collection, faculty_collection, vectors_collection, ) from chatbot import give_chat_response from bson import ObjectId from live_polls import LivePollFeature import pandas as pd import plotly.express as px from dotenv import load_dotenv import os from pymongo import MongoClient from gen_mcqs import ( generate_mcqs, save_quiz, save_surprise_quiz, quizzes_collection, surprise_quizzes_collection, get_student_quiz_score, get_student_surprise_quiz_score, submit_quiz_answers, submit_surprise_quiz_answers ) from create_course import courses_collection from pre_class_analytics2 import NovaScholarAnalytics import openai from openai import OpenAI import google.generativeai as genai from goals2 import GoalAnalyzer from openai import OpenAI import asyncio import numpy as np import re from analytics import derive_analytics, create_embeddings, cosine_similarity from bs4 import BeautifulSoup from rubrics import display_rubrics_tab from subjective_test_evaluation import ( evaluate_subjective_answers, display_evaluation_to_faculty, ) import streamlit.components.v1 as components from live_chat_feature import display_live_chat_interface load_dotenv() MONGO_URI = os.getenv("MONGO_URI") PERPLEXITY_API_KEY = os.getenv("PERPLEXITY_KEY") OPENAI_API_KEY = os.getenv("OPENAI_KEY") client = MongoClient(MONGO_URI) db = client["novascholar_db"] polls_collection = db["polls"] subjective_test_evaluation_collection = db["subjective_test_evaluation"] pre_subjective_test_evaluation_collection = db["pre_subjective_test_evaluation"] assignment_evaluation_collection = db["assignment_evaluation"] subjective_tests_collection = db["subjective_tests"] pre_subjective_tests_collection = db["pre_subjective_tests"] synoptic_store_collection = db["synoptic_store"] assignments_collection = db["assignments"] def get_current_user(): if "current_user" not in st.session_state: return None return students_collection.find_one({"_id": st.session_state.user_id}) # def display_preclass_content(session, student_id, course_id): """Display pre-class materials for a session""" # Initialize 'messages' in session_state if it doesn't exist if "messages" not in st.session_state: st.session_state.messages = [] # Display pre-class materials materials = list( resources_collection.find( {"course_id": course_id, "session_id": session["session_id"]} ) ) st.subheader("Pre-class Materials") if materials: for material in materials: with st.expander( f"{material['file_name']} ({material['material_type'].upper()})" ): file_type = material.get("file_type", "unknown") if file_type == "application/pdf": st.markdown(f"📑 [Open PDF Document]({material['file_name']})") if st.button("View PDF", key=f"view_pdf_{material['file_name']}"): st.text_area( "PDF Content", material["text_content"], height=300 ) if st.button( "Download PDF", key=f"download_pdf_{material['file_name']}" ): st.download_button( label="Download PDF", data=material["file_content"], file_name=material["file_name"], mime="application/pdf", ) if st.button( "Mark PDF as Read", key=f"pdf_{material['file_name']}" ): create_notification("PDF marked as read!", "success") else: st.info("No pre-class materials uploaded by the faculty.") st.subheader("Upload Pre-class Material") # File upload section for students uploaded_file = st.file_uploader("Upload Material", type=["txt", "pdf", "docx"]) if uploaded_file is not None: with st.spinner("Processing document..."): file_name = uploaded_file.name file_content = extract_text_from_file(uploaded_file) if file_content: material_type = st.selectbox( "Select Material Type", ["pdf", "docx", "txt"] ) if st.button("Upload Material"): upload_resource( course_id, session["session_id"], file_name, uploaded_file, material_type, ) # Search for the newly uploaded resource's _id in resources_collection resource_id = resources_collection.find_one( {"file_name": file_name} )["_id"] create_vector_store(file_content, resource_id) st.success("Material uploaded successfully!") st.subheader("Learn the Topic Using Chatbot") st.write(f"**Session Title:** {session['title']}") st.write( f"**Description:** {session.get('description', 'No description available.')}" ) # Chatbot interface if prompt := st.chat_input("Ask a question about the session topic"): if len(st.session_state.messages) >= 20: st.warning("Message limit (20) reached for this session.") return st.session_state.messages.append({"role": "user", "content": prompt}) # Display User Message with st.chat_message("user"): st.markdown(prompt) # Get response from chatbot context = "" for material in materials: if "text_content" in material: context += material["text_content"] + "\n" response = give_chat_response( student_id, session["session_id"], prompt, session["title"], session.get("description", ""), context, ) st.session_state.messages.append({"role": "assistant", "content": response}) # Display Assistant Response with st.chat_message("assistant"): st.markdown(response) # st.subheader("Your Chat History") # for message in st.session_state.messages: # content = message.get("content", "") # Default to an empty string if "content" is not present # role = message.get("role", "user") # Default to "user" if "role" is not present # with st.chat_message(role): # st.markdown(content) # user = get_current_user() def display_preclass_content(session, student_id, course_id): """Display pre-class materials for a session including external resources""" st.subheader("Pre-class Materials") print("Session ID is: ", session["session_id"]) # Display uploaded materials materials = resources_collection.find({"session_id": session["session_id"]}) for material in materials: file_type = material.get("file_type", "unknown") # Handle external resources if file_type == "external": with st.expander(f"📌 {material['file_name']}"): st.markdown( f"Source: [{material['source_url']}]({material['source_url']})" ) if material["material_type"].lower() == "video": # Embed YouTube video if it's a YouTube URL if ( "youtube.com" in material["source_url"] or "youtu.be" in material["source_url"] ): video_id = extract_youtube_id(material["source_url"]) if video_id: st.video(f"https://youtube.com/watch?v={video_id}") if st.button("View Content", key=f"view_external_{material['_id']}"): st.text_area( "Extracted Content", material["text_content"], height=300 ) if st.button("Mark as Read", key=f"external_{material['_id']}"): create_notification( f"{material['material_type']} content marked as read!", "success", ) # Handle traditional file types else: with st.expander( f"{material['file_name']} ({material['material_type'].upper()})" ): if file_type == "application/pdf": st.markdown(f"📑 [Open PDF Document]({material['file_name']})") if st.button("View PDF", key=f"view_pdf_{material['_id']}"): st.text_area( "PDF Content", material["text_content"], height=300 ) if st.button("Download PDF", key=f"download_pdf_{material['_id']}"): st.download_button( label="Download PDF", data=material["file_content"], file_name=material["file_name"], mime="application/pdf", ) if st.button("Mark PDF as Read", key=f"pdf_{material['_id']}"): create_notification("PDF marked as read!", "success") elif file_type == "text/plain": st.markdown(f"📄 [Open Text Document]({material['file_name']})") if st.button("View Text", key=f"view_text_{material['_id']}"): st.text_area( "Text Content", material["text_content"], height=300 ) if st.button( "Download Text", key=f"download_text_{material['_id']}" ): st.download_button( label="Download Text", data=material["file_content"], file_name=material["file_name"], mime="text/plain", ) if st.button("Mark Text as Read", key=f"text_{material['_id']}"): create_notification("Text marked as read!", "success") elif ( file_type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document" ): st.markdown(f"📄 [Open Word Document]({material['file_name']})") if st.button("View Word", key=f"view_word_{material['_id']}"): st.text_area( "Word Content", material["text_content"], height=300 ) if st.button( "Download Word", key=f"download_word_{material['_id']}" ): st.download_button( label="Download Word", data=material["file_content"], file_name=material["file_name"], mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document", ) if st.button("Mark Word as Read", key=f"word_{material['_id']}"): create_notification("Word document marked as read!", "success") elif ( file_type == "application/vnd.openxmlformats-officedocument.presentationml.presentation" ): st.markdown( f"📊 [Open PowerPoint Presentation]({material['file_name']})" ) if st.button("View PowerPoint", key=f"view_pptx_{material['_id']}"): st.text_area( "PowerPoint Content", material["text_content"], height=300 ) if st.button( "Download PowerPoint", key=f"download_pptx_{material['_id']}" ): st.download_button( label="Download PowerPoint", data=material["file_content"], file_name=material["file_name"], mime="application/vnd.openxmlformats-officedocument.presentationml.presentation", ) if st.button( "Mark PowerPoint as Read", key=f"pptx_{material['_id']}" ): create_notification( "PowerPoint presentation marked as read!", "success" ) # Initialize 'messages' in session_state if it doesn't exist if "messages" not in st.session_state: st.session_state.messages = [] # Chat input # Add a check, if materials are available, only then show the chat input if st.session_state.user_type == "student": if materials: if prompt := st.chat_input("Ask a question about Pre-class Materials"): # if len(st.session_state.messages) >= 20: # st.warning("Message limit (20) reached for this session.") # return st.session_state.messages.append({"role": "user", "content": prompt}) # Display User Message with st.chat_message("user"): st.markdown(prompt) # Get document context context = "" print("Session ID is: ", session["session_id"]) materials = resources_collection.find( {"session_id": session["session_id"]} ) print(materials) context = "" vector_data = None # for material in materials: # print(material) context = "" for material in materials: resource_id = material["_id"] print("Supposed Resource ID is: ", resource_id) vector_data = vectors_collection.find_one( {"resource_id": resource_id} ) # print(vector_data) if vector_data and "text" in vector_data: context += vector_data["text"] + "\n" if not vector_data: st.error("No Pre-class materials found for this session.") return try: # Generate response using Gemini # context_prompt = f""" # Based on the following context, answer the user's question: # Context: # {context} # Question: {prompt} # Please provide a clear and concise answer based only on the information provided in the context. # """ # context_prompt = f""" # You are a highly intelligent and resourceful assistant capable of synthesizing information from the provided context. # Context: # {context} # Instructions: # 1. Base your answers primarily on the given context. # 2. If the answer to the user's question is not explicitly in the context but can be inferred or synthesized from the information provided, do so thoughtfully. # 3. Only use external knowledge or web assistance when: # - The context lacks sufficient information, and # - The question requires knowledge beyond what can be reasonably inferred from the context. # 4. Clearly state if you are relying on web assistance for any part of your answer. # 5. Do not respond with a negative. If the answer is not in the context, provide a thoughtful response based on the information available on the web about it. # Question: {prompt} # Please provide a clear and comprehensive answer based on the above instructions. # """ context_prompt = f""" You are a highly intelligent and resourceful assistant capable of synthesizing information from the provided context and external sources. Context: {context} Instructions: 1. Base your answers on the provided context wherever possible. 2. If the answer to the user's question is not explicitly in the context: - Use external knowledge or web assistance to provide a clear and accurate response. 3. Do not respond negatively. If the answer is not in the context, use web assistance or your knowledge to generate a thoughtful response. 4. Clearly state if part of your response relies on web assistance. Question: {prompt} Please provide a clear and comprehensive answer based on the above instructions. """ response = model.generate_content(context_prompt) if not response or not response.text: st.error("No response received from the model") return assistant_response = response.text # Display Assistant Response with st.chat_message("assistant"): st.markdown(assistant_response) # Build the message new_message = { "prompt": prompt, "response": assistant_response, "timestamp": datetime.utcnow(), } st.session_state.messages.append(new_message) # Update database try: chat_history_collection.update_one( { "user_id": student_id, "session_id": session["session_id"], }, { "$push": {"messages": new_message}, "$setOnInsert": { "user_id": student_id, "session_id": session["session_id"], "timestamp": datetime.utcnow(), }, }, upsert=True, ) except Exception as db_error: st.error(f"Error saving chat history: {str(db_error)}") except Exception as e: st.error(f"Error generating response: {str(e)}") else: st.subheader("Upload Pre-class Material") # File upload section for students uploaded_file = st.file_uploader("Upload Material", type=["txt", "pdf", "docx"]) if uploaded_file is not None: with st.spinner("Processing document..."): file_name = uploaded_file.name file_content = extract_text_from_file(uploaded_file) if file_content: material_type = st.selectbox( "Select Material Type", ["pdf", "docx", "txt"] ) if st.button("Upload Material"): upload_resource( course_id, session["session_id"], file_name, uploaded_file, material_type, ) # print("Resource ID is: ", resource_id) # Search for the newly uploaded resource's _id in resources_collection # resource_id = resources_collection.find_one({"file_name": file_name})["_id"] st.success("Material uploaded successfully!") # st.experimental_rerun() # st.subheader("Your Chat History") if st.button("View Chat History"): # Initialize chat messages from database if "messages" not in st.session_state or not st.session_state.messages: existing_chat = chat_history_collection.find_one( {"user_id": student_id, "session_id": session["session_id"]} ) if existing_chat and "messages" in existing_chat: st.session_state.messages = existing_chat["messages"] else: st.session_state.messages = [] # Display existing chat history try: for message in st.session_state.messages: if "prompt" in message and "response" in message: with st.chat_message("user"): st.markdown(message["prompt"]) with st.chat_message("assistant"): st.markdown(message["response"]) except Exception as e: st.error(f"Error displaying chat history: {str(e)}") st.session_state.messages = [] if st.session_state.user_type == "student": st.subheader("Create a Practice Quiz") questions = [] quiz_id = "" with st.form("create_quiz_form"): num_questions = st.number_input( "Number of Questions", min_value=1, max_value=20, value=2 ) submit_quiz = st.form_submit_button("Generate Quiz") if submit_quiz: # Get pre-class materials from resources_collection materials = resources_collection.find( {"session_id": session["session_id"]} ) context = "" for material in materials: if "text_content" in material: context += material["text_content"] + "\n" if not context: st.error("No pre-class materials found for this session.") return # Generate MCQs from context questions = generate_mcqs( context, num_questions, session["title"], session.get("description", ""), ) if questions: quiz_id = save_quiz( course_id, session["session_id"], "Practice Quiz", questions, student_id, ) if quiz_id: st.success("Quiz saved successfully!") st.session_state.show_quizzes = True else: st.error("Error saving quiz.") else: st.error("Error generating questions.") # if st.button("Attempt Practice Quizzes "): # quizzes = list(quizzes_collection.find({"course_id": course_id, "session_id": session['session_id'], "user_id": student_id})) if getattr(st.session_state, "show_quizzes", False): # quiz = quizzes_collection.find_one({"course_id": course_id, "session_id": session['session_id'], "user_id": student_id}) quiz = quizzes_collection.find_one( { "course_id": course_id, "session_id": session["session_id"], "user_id": student_id, }, sort=[("created_at", -1)], ) if not quiz: st.info("No practice quizzes created.") else: with st.expander(f"📝 Practice Quiz", expanded=False): # Check if student has already taken this quiz existing_score = get_student_quiz_score(quiz["_id"], student_id) if existing_score is not None: st.success(f"Quiz completed! Your score: {existing_score:.1f}%") # Display correct answers after submission st.subheader("Quiz Review") for i, question in enumerate(quiz["questions"]): st.markdown(f"**Question {i+1}:** {question['question']}") for opt in question["options"]: if opt.startswith(question["correct_option"]): st.markdown(f"✅ {opt}") else: st.markdown(f"- {opt}") else: # Initialize quiz state for this specific quiz quiz_key = f"quiz_{quiz['_id']}_student_{student_id}" if quiz_key not in st.session_state: st.session_state[quiz_key] = { "submitted": False, "score": None, "answers": {}, } # If quiz was just submitted, show the results if st.session_state[quiz_key]["submitted"]: st.success( f"Quiz submitted successfully! Your score: {st.session_state[quiz_key]['score']:.1f}%" ) # Reset the quiz state st.session_state[quiz_key]["submitted"] = False # Display quiz questions st.write("Please select your answers:") # Create a form for quiz submission form_key = f"quiz_form_{quiz['_id']}_student_{student_id}" with st.form(key=form_key): student_answers = {} for i, question in enumerate(quiz["questions"]): st.markdown( f"**Question {i+1}:** {question['question']}" ) options = [opt for opt in question["options"]] # student_answers[str(i)] = st.radio( # f"Select answer for question {i+1}:", # options=options, # key=f"q_{i}", # index=None # ) answer = st.radio( f"Select answer for question {i+1}:", options=options, key=f"{quiz['_id']}_{i}", # Simplify the radio button key index=None, ) if ( answer ): # Only add to answers if a selection was made student_answers[str(i)] = answer # Submit button # submitted = st.form_submit_button("Submit Quiz") print("Before the submit button") submit_button = st.form_submit_button("Submit Quiz") print("After the submit button") if submit_button and student_answers: print("Clicked the button") print(student_answers) correct_answers = 0 for i, question in enumerate(quiz["questions"]): if ( student_answers[str(i)] == question["correct_option"] ): correct_answers += 1 score = (correct_answers / len(quiz["questions"])) * 100 if score is not None: st.success( f"Quiz submitted successfully! Your score: {score:.1f}%" ) st.session_state[quiz_key]["submitted"] = True st.session_state[quiz_key]["score"] = score st.session_state[quiz_key]["answers"] = student_answers # This will trigger a rerun, but now we'll handle it properly st.rerun() else: st.error("Error submitting quiz. Please try again.") # correct_answers = 0 # for i, question in enumerate(quiz['questions']): # if student_answers[str(i)] == question['correct_option']: # correct_answers += 1 # score = (correct_answers / len(quiz['questions'])) * 100 # print(score) # try: # quizzes_collection.update_one( # {"_id": quiz['_id']}, # {"$push": {"submissions": {"student_id": student_id, "score": score}}} # ) # st.success(f"Quiz submitted successfully! Your score: {score:.1f}%") # except Exception as db_error: # st.error(f"Error saving submission: {str(db_error)}") if st.session_state.user_type == "student": # display_pre_subjective_test_tab(session, course_id, session_id) display_pre_subjective_test_tab( student_id, course_id, session["session_id"] ) # Added this line pass def extract_youtube_id(url): """Extract YouTube video ID from URL""" if "youtube.com" in url: try: return url.split("v=")[1].split("&")[0] except IndexError: return None elif "youtu.be" in url: try: return url.split("/")[-1] except IndexError: return None return None def display_live_presentation(session, user_type, course_id): st.markdown("### Live Presentation") # Get active presentation session_data = courses_collection.find_one( {"course_id": course_id, "sessions.session_id": session["session_id"]}, {"sessions.$": 1}, ) active_presentation = ( session_data["sessions"][0].get("in_class", {}).get("active_presentation") ) # Faculty Interface if user_type == "faculty": if not active_presentation: st.markdown( """ """, unsafe_allow_html=True, ) # URL input section with st.container(): ppt_url = st.text_input( "🔗 Enter Google Slides Presentation URL", placeholder="https://docs.google.com/presentation/...", ) if ppt_url: if st.button("â–ļī¸ Activate Presentation", use_container_width=True): courses_collection.update_one( { "course_id": course_id, "sessions.session_id": session["session_id"], }, { "$set": { "sessions.$.in_class.active_presentation": ppt_url } }, ) st.success("✅ Presentation activated successfully!") st.rerun() else: # Display active presentation st.markdown("#### đŸŽ¯ Active Presentation") components.iframe(active_presentation, height=800) # Deactivate button st.markdown("
", unsafe_allow_html=True) if st.button( "⏚ī¸ Deactivate Presentation", type="secondary", use_container_width=True ): courses_collection.update_one( { "course_id": course_id, "sessions.session_id": session["session_id"], }, {"$unset": {"sessions.$.in_class.active_presentation": ""}}, ) st.success("✅ Presentation deactivated successfully!") st.rerun() # Student Interface else: if active_presentation: st.markdown("#### đŸŽ¯ Active Presentation") components.iframe(active_presentation, height=800) else: st.info("📝 No active presentations at this time.") def display_in_class_content(session, user_id, course_id, user_type): # """Display in-class activities and interactions""" """Display in-class activities and interactions""" st.header("In-class Activities") # Initialize Live Polls feature live_polls = LivePollFeature() # Display appropriate interface based on user role if user_type == "faculty": live_polls.display_faculty_interface(session["session_id"]) else: live_polls.display_student_interface(session["session_id"]) display_live_chat_interface(session, user_id, course_id=course_id) # Live Presentation Feature display_live_presentation(session, user_type, course_id) if user_type == "faculty": faculty_id = st.session_state.user_id st.subheader("Create Surprise Quiz") questions = [] with st.form("create_surprise_quiz_form"): quiz_title = st.text_input("Surprise Quiz Title") num_questions = st.number_input( "Number of Questions", min_value=1, max_value=20, value=5 ) no_minutes = st.number_input( "Duration of Quiz (in minutes)", min_value=1, max_value=60, value=5 ) # Option to choose quiz generation method generation_method = st.radio( "Question Generation Method", ["Generate from Pre-class Materials", "Generate Random Questions"], ) submit_quiz = st.form_submit_button("Generate Surprise Quiz") if submit_quiz: if generation_method == "Generate from Pre-class Materials": # Get pre-class materials from resources_collection materials = resources_collection.find( {"session_id": session["session_id"]} ) context = "" for material in materials: if "text_content" in material: context += material["text_content"] + "\n" if not context: st.error("No pre-class materials found for this session.") return # Generate MCQs from context questions = generate_mcqs( context, num_questions, session["title"], session.get("description", ""), ) else: # Generate random MCQs based on session title and description questions = generate_mcqs( None, num_questions, session["title"], session.get("description", ""), ) print(questions) if questions: # Preview generated questions st.subheader("Preview Generated Questions") for i, q in enumerate(questions, 1): st.markdown(f"**Question {i}:** {q['question']}") for opt in q["options"]: st.markdown(f"- {opt}") st.markdown(f"*Correct Answer: {q['correct_option']}*") # Save quiz quiz_id = save_surprise_quiz( course_id, session["session_id"], quiz_title, questions, faculty_id, no_minutes ) if quiz_id: st.success("Quiz saved successfully!") else: st.error("Error saving quiz.") else: display_surprise_quiz_tab(st.session_state.user_id, course_id=course_id, session_id=session["session_id"]) def generate_random_assignment_id(): """Generate a random integer ID for assignments""" return random.randint(100000, 999999) def display_post_class_content(session, student_id, course_id): """Display post-class assignments and submissions""" st.header("Post-class Work") if st.session_state.user_type == "faculty": faculty_id = st.session_state.user_id st.subheader("Create Subjective Test") # Create a form for test generation with st.form("create_subjective_test_form"): test_title = st.text_input("Test Title") num_subjective_questions = st.number_input( "Number of Subjective Questions", min_value=1, value=5 ) generation_method = st.radio( "Question Generation Method", ["Generate from Pre-class Materials", "Generate Random Questions"], ) generate_test_btn = st.form_submit_button("Generate Test") # Handle test generation outside the form if generate_test_btn: if not test_title: st.error("Please enter a test title.") return context = "" if generation_method == "Generate from Pre-class Materials": materials = resources_collection.find( {"session_id": session["session_id"]} ) for material in materials: if "text_content" in material: context += material["text_content"] + "\n" with st.spinner("Generating questions and synoptic..."): try: # Store generated content in session state to persist between rerenders questions = generate_questions( context if context else None, num_subjective_questions, session["title"], session.get("description", ""), ) if questions: synoptic = generate_synoptic( questions, context if context else None, session["title"], num_subjective_questions, ) if synoptic: # Store in session state st.session_state.generated_questions = questions st.session_state.generated_synoptic = synoptic st.session_state.test_title = test_title # Display preview st.subheader("Preview Subjective Questions and Synoptic") for i, (q, s) in enumerate(zip(questions, synoptic), 1): st.markdown(f"**Question {i}:** {q['question']}") with st.expander(f"View Synoptic {i}"): st.markdown(s) # Save button outside the form if st.button("Save Test"): test_id = save_subjective_test( course_id, session["session_id"], test_title, questions, ) if test_id: st.success("Subjective test saved successfully!") else: st.error("Error saving subjective test.") else: st.error( "Error generating synoptic answers. Please try again." ) else: st.error("Error generating questions. Please try again.") except Exception as e: st.error(f"An error occurred: {str(e)}") # Display previously generated test if it exists in session state elif hasattr(st.session_state, "generated_questions") and hasattr( st.session_state, "generated_synoptic" ): st.subheader("Preview Subjective Questions and Synoptic") for i, (q, s) in enumerate( zip( st.session_state.generated_questions, st.session_state.generated_synoptic, ), 1, ): st.markdown(f"**Question {i}:** {q['question']}") with st.expander(f"View Synoptic {i}"): st.markdown(s) if st.button("Save Test"): test_id = save_subjective_test( course_id, session["session_id"], st.session_state.test_title, st.session_state.generated_questions, ) if test_id: st.success("Subjective test saved successfully!") # Clear session state after successful save del st.session_state.generated_questions del st.session_state.generated_synoptic del st.session_state.test_title else: st.error("Error saving subjective test.") # st.subheader("Create quiz section UI for faculty") st.subheader("Create Quiz") questions = [] with st.form("create_quiz_form"): quiz_title = st.text_input("Quiz Title") num_questions = st.number_input( "Number of Questions", min_value=1, max_value=20, value=5 ) # Option to choose quiz generation method generation_method = st.radio( "Question Generation Method", ["Generate from Pre-class Materials", "Generate Random Questions"], ) submit_quiz = st.form_submit_button("Generate Quiz") if submit_quiz: if generation_method == "Generate from Pre-class Materials": # Get pre-class materials from resources_collection materials = resources_collection.find( {"session_id": session["session_id"]} ) context = "" for material in materials: if "text_content" in material: context += material["text_content"] + "\n" if not context: st.error("No pre-class materials found for this session.") return # Generate MCQs from context questions = generate_mcqs( context, num_questions, session["title"], session.get("description", ""), ) else: # Generate random MCQs based on session title and description questions = generate_mcqs( None, num_questions, session["title"], session.get("description", ""), ) print(questions) if questions: # Preview generated questions st.subheader("Preview Generated Questions") for i, q in enumerate(questions, 1): st.markdown(f"**Question {i}:** {q['question']}") for opt in q["options"]: st.markdown(f"- {opt}") st.markdown(f"*Correct Answer: {q['correct_option']}*") # Save quiz quiz_id = save_quiz( course_id, session["session_id"], quiz_title, questions, faculty_id, ) if quiz_id: st.success("Quiz saved successfully!") else: st.error("Error saving quiz.") st.subheader("Add Assignment") with st.form("add_assignment_form"): title = st.text_input("Assignment Title") description = st.text_area("Assignment Description") due_date = st.date_input("Due Date") submit = st.form_submit_button("Add Assignment") if submit: if not title or not description: st.error("Please fill in all required fields.") return due_date = datetime.combine(due_date, datetime.min.time()) assignment = { "_id": ObjectId(), "title": title, "description": description, "due_date": due_date, "course_id": course_id, "session_id": session["session_id"], "faculty_id": faculty_id, "created_at": datetime.utcnow(), "status": "active", "submissions": [], } assignments_collection.insert_one(assignment) st.success("Assignment added successfully!") st.subheader("Existing Assignments") assignments = assignments_collection.find( {"session_id": session["session_id"], "course_id": course_id} ) for assignment in assignments: with st.expander(f"📝 {assignment['title']}", expanded=True): st.markdown( f"**Due Date:** {assignment['due_date'].strftime('%Y-%m-%d')}" ) st.markdown(f"**Description:** {assignment['description']}") total_submissions = len(assignment.get("submissions", [])) total_students = students_collection.count_documents( {"enrolled_courses": {"$elemMatch": {"course_id": course_id}}} ) col1, col2, col3 = st.columns(3) with col1: st.metric("Total Submissions", total_submissions) with col2: submission_rate = ( (total_submissions / total_students * 100) if total_students > 0 else 0 ) st.metric("Submission Rate", f"{submission_rate:.1f}%") with col3: st.metric("Pending Submissions", total_students - total_submissions) # Display evaluation button and status evaluation_status = st.empty() eval_button = st.button( "View/Generate Evaluations", key=f"eval_{assignment['_id']}" ) if eval_button: st.session_state.show_evaluations = True st.session_state.current_assignment = assignment["_id"] # Show evaluation interface in a new container instead of an expander evaluation_container = st.container() with evaluation_container: from assignment_evaluation import display_evaluation_to_faculty display_evaluation_to_faculty( session["session_id"], student_id, course_id ) else: # Student view assignments = assignments_collection.find( { "session_id": session["session_id"], "course_id": course_id, "status": "active", } ) for assignment in assignments: with st.expander(f"📝 {assignment['title']}", expanded=True): st.markdown( f"**Due Date:** {assignment['due_date'].strftime('%Y-%m-%d')}" ) st.markdown(f"**Description:** {assignment['description']}") existing_submission = next( ( sub for sub in assignment.get("submissions", []) if sub["student_id"] == str(student_id) ), None, ) if existing_submission: st.success("Assignment submitted!") st.markdown( f"**Submitted on:** {existing_submission['submitted_at'].strftime('%Y-%m-%d %H:%M')}" ) # Show evaluation status and feedback in the same container evaluation = assignment_evaluation_collection.find_one( { "assignment_id": assignment["_id"], "student_id": str(student_id), } ) if evaluation: st.markdown("### Evaluation") st.markdown(evaluation["evaluation"]) else: st.info("Evaluation pending. Check back later.") else: uploaded_file = st.file_uploader( "Upload your work", type=[ "pdf", "doc", "docx", "txt", "py", "ipynb", "ppt", "pptx", ], key=f"upload_{assignment['_id']}", ) if uploaded_file is not None: if st.button( "Submit Assignment", key=f"submit_{assignment['_id']}" ): text_content = extract_text_from_file(uploaded_file) submission = { "student_id": str(student_id), "file_name": uploaded_file.name, "file_type": uploaded_file.type, "file_content": uploaded_file.getvalue(), "text_content": text_content, "submitted_at": datetime.utcnow(), } assignments_collection.update_one( {"_id": assignment["_id"]}, {"$push": {"submissions": submission}}, ) st.success("Assignment submitted successfully!") st.rerun() def display_inclass_analytics(session, course_id): """Display in-class analytics for faculty""" st.subheader("In-class Analytics") # Get all enrolled students count for participation rate calculation total_students = students_collection.count_documents( {"enrolled_courses": {"$elemMatch": {"course_id": course_id}}} ) if total_students == 0: st.warning("No students enrolled in this course.") return # Get all polls for this session polls = polls_collection.find({"session_id": session["session_id"]}) polls_list = list(polls) if not polls_list: st.warning("No polls have been conducted in this session yet.") return # Overall Poll Participation Metrics st.markdown("### Overall Poll Participation") # Calculate overall participation metrics total_polls = len(polls_list) participating_students = set() poll_participation_data = [] for poll in polls_list: respondents = set(poll.get("respondents", [])) participating_students.update(respondents) poll_participation_data.append( { "Poll Title": poll.get("question", "Untitled Poll"), "Respondents": len(respondents), "Participation Rate": (len(respondents) / total_students * 100), } ) # Display summary metrics col1, col2, col3 = st.columns(3) with col1: st.metric("Total Polls Conducted", total_polls) with col2: st.metric("Active Participants", len(participating_students)) with col3: avg_participation = ( sum(p["Participation Rate"] for p in poll_participation_data) / total_polls ) st.metric("Average Participation Rate", f"{avg_participation:.1f}%") # Participation Trend Graph # st.markdown("### Poll Participation Trends") # participation_df = pd.DataFrame(poll_participation_data) # # Create line chart for participation trends # fig = px.line(participation_df, # x='Poll Title', # y='Participation Rate', # title='Poll Participation Rates Over Time', # markers=True) # fig.update_layout( # xaxis_title="Polls", # yaxis_title="Participation Rate (%)", # yaxis_range=[0, 100] # ) # st.plotly_chart(fig) # Individual Poll Results st.markdown("### Individual Poll Results") for poll in polls_list: with st.expander(f"📊 {poll.get('question', 'Untitled Poll')}"): responses = poll.get("responses", {}) respondents = poll.get("respondents", []) # Calculate metrics for this poll response_count = len(respondents) participation_rate = (response_count / total_students) * 100 # Display poll metrics col1, col2 = st.columns(2) with col1: st.metric("Total Responses", response_count) with col2: st.metric("Participation Rate", f"{participation_rate:.1f}%") if responses: # Create DataFrame for responses response_df = pd.DataFrame( list(responses.items()), columns=["Option", "Votes"] ) response_df["Percentage"] = ( response_df["Votes"] / response_df["Votes"].sum() * 100 ).round(1) # Display response distribution fig = px.bar( response_df, x="Option", y="Votes", title="Response Distribution", text="Percentage", ) fig.update_traces(texttemplate="%{text:.1f}%", textposition="outside") st.plotly_chart(fig) # Display detailed response table st.markdown("#### Detailed Response Breakdown") response_df["Percentage"] = response_df["Percentage"].apply( lambda x: f"{x}%" ) st.table(response_df) # Non-participating students non_participants = list( students_collection.find( {"courses": course_id, "_id": {"$nin": respondents}} ) ) if non_participants: st.markdown("#### Students Who Haven't Participated") non_participant_data = [ { "Name": student.get("name", "Unknown"), "SID": student.get("sid", "Unknown"), } for student in non_participants ] st.table(pd.DataFrame(non_participant_data)) # Export functionality for participation data st.markdown("### Export Analytics") if st.button("Download Poll Analytics Report"): # Create a more detailed DataFrame for export export_data = [] for poll in polls_list: poll_data = { "Poll Question": poll.get("question", "Untitled"), "Total Responses": len(poll.get("respondents", [])), "Participation Rate": f"{(len(poll.get('respondents', [])) / total_students * 100):.1f}%", } # Add response distribution for option, votes in poll.get("responses", {}).items(): poll_data[f"Option: {option}"] = votes export_data.append(poll_data) export_df = pd.DataFrame(export_data) csv = export_df.to_csv(index=False).encode("utf-8") st.download_button( "đŸ“Ĩ Download Complete Report", csv, "poll_analytics.csv", "text/csv", key="download-csv", ) def display_postclass_analytics(session, course_id): """Display post-class analytics for faculty""" st.subheader("Post-class Analytics") # Get all assignments for this session session_data = courses_collection2.find_one( {"sessions.session_id": session["session_id"]}, {"sessions.$": 1} ) if not session_data or "sessions" not in session_data: st.warning("No assignments found for this session.") return assignments = ( session_data["sessions"][0].get("post_class", {}).get("assignments", []) ) for assignment in assignments: with st.expander(f"📝 Assignment: {assignment.get('title', 'Untitled')}"): # Get submission analytics submissions = assignment.get("submissions", []) # total_students = students_collection.count_documents({"courses": session['course_id']}) total_students = students_collection.count_documents( {"enrolled_courses": {"$elemMatch": {"course_id": course_id}}} ) # Calculate submission metrics submitted_count = len(submissions) submission_rate = ( (submitted_count / total_students) * 100 if total_students > 0 else 0 ) # Display metrics col1, col2, col3 = st.columns(3) with col1: st.metric("Submissions Received", submitted_count) with col2: st.metric("Submission Rate", f"{submission_rate:.1f}%") with col3: st.metric("Pending Submissions", total_students - submitted_count) # Display submission timeline if submissions: submission_dates = [ sub.get("submitted_at") for sub in submissions if "submitted_at" in sub ] if submission_dates: df = pd.DataFrame(submission_dates, columns=["Submission Date"]) fig = px.histogram( df, x="Submission Date", title="Submission Timeline", labels={ "Submission Date": "Date", "count": "Number of Submissions", }, ) st.plotly_chart(fig) # Display submission status breakdown status_counts = { "pending": total_students - submitted_count, "submitted": submitted_count, "late": len([sub for sub in submissions if sub.get("is_late", False)]), } st.markdown("### Submission Status Breakdown") status_df = pd.DataFrame( list(status_counts.items()), columns=["Status", "Count"] ) st.bar_chart(status_df.set_index("Status")) # List of students who haven't submitted if status_counts["pending"] > 0: st.markdown("### Students with Pending Submissions") # submitted_ids = [sub.get('student_id') for sub in submissions] submitted_ids = [ObjectId(sub.get("student_id")) for sub in submissions] print(submitted_ids) pending_students = students_collection.find( { "enrolled_courses.course_id": course_id, "_id": {"$nin": submitted_ids}, } ) print(pending_students) for student in pending_students: st.markdown( f"- {student.get('full_name', 'Unknown Student')} (SID: {student.get('SID', 'Unknown SID')})" ) def get_chat_history(user_id, session_id): query = { "user_id": ObjectId(user_id), "session_id": session_id, "timestamp": {"$lte": datetime.utcnow()}, } result = chat_history_collection.find(query) return list(result) def get_response_from_llm(raw_data): messages = [ { "role": "system", "content": "You are an AI that refines raw analytics data into actionable insights for faculty reports.", }, { "role": "user", "content": f""" Based on the following analytics data, refine and summarize the insights: Raw Data: {raw_data} Instructions: 1. Group similar topics together under appropriate categories. 2. Remove irrelevant or repetitive entries. 3. Summarize the findings into actionable insights. 4. Provide concise recommendations for improvement based on the findings. Output: Provide a structured response with the following format: {{ "Low Engagement Topics": ["List of Topics"], "Frustration Areas": ["List of areas"], "Recommendations": ["Actionable recommendations"], }} """, }, ] try: client = OpenAI(api_key=OPENAI_API_KEY) response = client.chat.completions.create( model="gpt-4o-mini", messages=messages, temperature=0.2 ) content = response.choices[0].message.content return json.loads(content) except Exception as e: st.error(f"Error generating response: {str(e)}") return None import typing_extensions as typing from typing import Union, List, Dict # class Topics(typing.TypedDict): # overarching_theme: List[Dict[str, Union[str, List[Dict[str, Union[str, List[str]]]]]]] # indirect_topics: List[Dict[str, str]] def extract_topics_from_materials(session): """Extract topics from pre-class materials""" materials = resources_collection.find({"session_id": session["session_id"]}) texts = "" if materials: for material in materials: if "text_content" in material: text = material["text_content"] texts += text + "\n" else: st.warning("No text content found in the material.") return else: st.error("No pre-class materials found for this session.") return if texts: context_prompt = f""" Task: Extract Comprehensive Topics in a List Format You are tasked with analyzing the provided text content and extracting a detailed, flat list of topics. Instructions: Identify All Topics: Extract a comprehensive list of all topics, subtopics, and indirect topics present in the provided text content. This list should include: Overarching themes Main topics Subtopics and their sub-subtopics Indirectly related topics Flat List Format: Provide a flat list where each item is a topic. Ensure topics at all levels (overarching, main, sub, sub-sub, indirect) are represented as individual entries in the list. Be Exhaustive: Ensure the response captures every topic, subtopic, and indirectly related concept comprehensively. Output Requirements: Use this structure: {{ "topics": [ "Topic 1", "Topic 2", "Topic 3", ... ] }} Do Not Include: Do not include backticks, hierarchical structures, or the word 'json' in your response. Content to Analyze: {texts} """ try: # response = model.generate_content(context_prompt, generation_config=genai.GenerationConfig(response_mime_type="application/json", response_schema=list[Topics])) response = model.generate_content( context_prompt, generation_config=genai.GenerationConfig(temperature=0.3), ) if not response or not response.text: st.error("Error extracting topics from materials.") return topics = response.text return topics except Exception as e: st.error(f"Error extracting topics: {str(e)}") return None else: st.error("No text content found in the pre-class materials.") return None def convert_json_to_dict(json_str): try: return json.loads(json_str) except Exception as e: st.error(f"Error converting JSON to dictionary. {str(e)}") return None # Load topics from a JSON file # topics = [] # with open(r'topics.json', 'r') as file: # topics = json.load(file) def get_preclass_analytics(session): # Earlier Code: # """Get all user_ids from chat_history collection where session_id matches""" # user_ids = chat_history_collection.distinct("user_id", {"session_id": session['session_id']}) # print(user_ids) # session_id = session['session_id'] # all_chat_histories = [] # for user_id in user_ids: # result = get_chat_history(user_id, session_id) # if result: # for record in result: # chat_history = { # "user_id": record["user_id"], # "session_id": record["session_id"], # "messages": record["messages"] # } # all_chat_histories.append(chat_history) # else: # st.warning("No chat history found for this session.") # # Pass the pre-class materials content to the analytics engine # topics = extract_topics_from_materials(session) # # dict_topics = convert_json_to_dict(topics) # print(topics) # # # Use the 1st analytics engine # # analytics_engine = NovaScholarAnalytics(all_topics_list=topics) # # # extracted_topics = analytics_engine._extract_topics(None, topics) # # # print(extracted_topics) # # results = analytics_engine.process_chat_history(all_chat_histories) # # faculty_report = analytics_engine.generate_faculty_report(results) # # print(faculty_report) # # # Pass this Faculty Report to an LLM model for refinements and clarity # # refined_report = get_response_from_llm(faculty_report) # # return refined_report # # Use the 2nd analytice engine (using LLM): fallback_analytics = { "topic_insights": [], "student_insights": [], "recommended_actions": [ { "action": "Review analytics generation process", "priority": "high", "target_group": "system_administrators", "reasoning": "Analytics generation failed", "expected_impact": "Restore analytics functionality", } ], "course_health": { "overall_engagement": 0, "critical_topics": [], "class_distribution": { "high_performers": 0, "average_performers": 0, "at_risk": 0, }, }, "intervention_metrics": { "immediate_attention_needed": [], "monitoring_required": [], }, } # analytics_generator = NovaScholarAnalytics() # analytics2 = analytics_generator.generate_analytics(all_chat_histories, topics) # # enriched_analytics = analytics_generator._enrich_analytics(analytics2) # print("Analytics is: ", analytics2) # if analytics2 == fallback_analytics: # return None # else: # return analytics2 # # print(json.dumps(analytics, indent=2)) # New Code: # Debug print 1: Check session print("Starting get_preclass_analytics with session:", session["session_id"]) user_ids = chat_history_collection.distinct( "user_id", {"session_id": session["session_id"]} ) # Debug print 2: Check user_ids print("Found user_ids:", user_ids) all_chat_histories = [] for user_id in user_ids: result = get_chat_history(user_id, session["session_id"]) # Debug print 3: Check each chat history result print(f"Chat history for user {user_id}:", "Found" if result else "Not found") if result: for record in result: chat_history = { "user_id": record["user_id"], "session_id": record["session_id"], "messages": record["messages"], } all_chat_histories.append(chat_history) # Debug print 4: Check chat histories print("Total chat histories collected:", len(all_chat_histories)) # Extract topics with debug print topics = extract_topics_from_materials(session) # Debug print 5: Check topics print("Extracted topics:", topics) if not topics: print("Topics extraction failed") # Debug print 6 return None analytics_generator = NovaScholarAnalytics() analytics2 = analytics_generator.generate_analytics(all_chat_histories, topics) # Debug print 7: Check analytics print("Generated analytics:", analytics2) if analytics2 == fallback_analytics: print("Fallback analytics returned") # Debug print 8 return None else: return analytics2 # Load Analytics from a JSON file # analytics = [] # with open(r'new_analytics2.json', 'r') as file: # analytics = json.load(file) def display_preclass_analytics2(session, course_id): # Earlier Code: # Initialize or get analytics data from session state # if 'analytics_data' not in st.session_state: # st.session_state.analytics_data = get_preclass_analytics(session) # analytics = st.session_state.analytics_data # print(analytics) # New Code: # Initialize or get analytics data from session state if "analytics_data" not in st.session_state: # Add debug prints analytics_data = get_preclass_analytics(session) if analytics_data is None: st.info("Fetching new analytics data...") if analytics_data is None: st.error("Failed to generate analytics. Please check the following:") st.write("1. Ensure pre-class materials contain text content") st.write("2. Verify chat history exists for this session") st.write("3. Check if topic extraction was successful") return st.session_state.analytics_data = analytics_data analytics = st.session_state.analytics_data # Validate analytics data structure if not isinstance(analytics, dict): st.error(f"Invalid analytics data type: {type(analytics)}") return required_keys = [ "topic_wise_insights", "ai_recommended_actions", "student_analytics", ] missing_keys = [key for key in required_keys if key not in analytics] if missing_keys: st.error(f"Missing required keys in analytics data: {missing_keys}") return # Initialize topic indices only if we have valid data if "topic_indices" not in st.session_state: try: st.session_state.topic_indices = list( range(len(analytics["topic_wise_insights"])) ) except Exception as e: st.error(f"Error creating topic indices: {str(e)}") st.write("Analytics data structure:", analytics) return # Enhanced CSS for better styling and interactivity st.markdown( """ """, unsafe_allow_html=True, ) # Topic-wise Analytics Section st.markdown( '

Topic-wise Analytics

', unsafe_allow_html=True ) # Initialize session state for topic expansion if "expanded_topic" not in st.session_state: st.session_state.expanded_topic = None # Store topic indices in session state if not already done if "topic_indices" not in st.session_state: st.session_state.topic_indices = list( range(len(analytics["topic_wise_insights"])) ) if st.session_state.topic_indices: st.markdown('
', unsafe_allow_html=True) for idx in st.session_state.topic_indices: topic = analytics["topic_wise_insights"][idx] topic_id = f"topic_{idx}" # Create clickable header col1, col2 = st.columns([3, 1]) with col1: if st.button( topic["topic"], key=f"topic_button_{idx}", use_container_width=True, type="secondary", ): st.session_state.expanded_topic = ( topic_id if st.session_state.expanded_topic != topic_id else None ) with col2: st.markdown( f"""
{topic["struggling_percentage"]*100:.1f}% Struggling
""", unsafe_allow_html=True, ) # Show content if topic is expanded if st.session_state.expanded_topic == topic_id: st.markdown( f"""
Key Issues
Key Misconceptions
""", unsafe_allow_html=True, ) st.markdown("
", unsafe_allow_html=True) # AI Recommendations Section st.markdown( '

AI-Powered Recommendations

', unsafe_allow_html=True, ) st.markdown('
', unsafe_allow_html=True) for idx, rec in enumerate(analytics["ai_recommended_actions"]): st.markdown( f"""

Recommendation {idx + 1} {rec["priority"]}

{rec["action"]}

Reason: {rec["reasoning"]}

Expected Outcome: {rec["expected_outcome"]}

""", unsafe_allow_html=True, ) st.markdown("
", unsafe_allow_html=True) # Student Analytics Section st.markdown( '

Student Analytics

', unsafe_allow_html=True ) # Filters with st.container(): # st.markdown('
', unsafe_allow_html=True) col1, col2, col3 = st.columns(3) with col1: concept_understanding = st.selectbox( "Filter by Understanding", ["All", "Strong", "Moderate", "Needs Improvement"], ) with col2: participation_level = st.selectbox( "Filter by Participation", ["All", "High (>80%)", "Medium (50-80%)", "Low (<50%)"], ) with col3: struggling_topic = st.selectbox( "Filter by Struggling Topic", ["All"] + list( set( [ topic for student in analytics["student_analytics"] for topic in student["struggling_topics"] ] ) ), ) # st.markdown('
', unsafe_allow_html=True) # Display student metrics in a grid st.markdown('
', unsafe_allow_html=True) for student in analytics["student_analytics"]: # Apply filters if ( concept_understanding != "All" and student["engagement_metrics"]["concept_understanding"] .replace("_", " ") .title() != concept_understanding ): continue participation = student["engagement_metrics"]["participation_level"] * 100 if participation_level != "All": if participation_level == "High (>80%)" and participation <= 80: continue elif participation_level == "Medium (50-80%)" and ( participation < 50 or participation > 80 ): continue elif participation_level == "Low (<50%)" and participation >= 50: continue if ( struggling_topic != "All" and struggling_topic not in student["struggling_topics"] ): continue st.markdown( f"""
Student {student["student_id"][-6:]}
Participation
{student["engagement_metrics"]["participation_level"]*100:.1f}%
Understanding
{student["engagement_metrics"]["concept_understanding"].replace('_', ' ').title()}
Struggling Topics:
{", ".join(student["struggling_topics"]) if student["struggling_topics"] else "None"}
{student["personalized_recommendation"]}
""", unsafe_allow_html=True, ) st.markdown("
", unsafe_allow_html=True) def reset_analytics_state(): """ Helper function to reset the analytics state when needed (e.g., when loading a new session or when data needs to be refreshed) """ if "analytics_data" in st.session_state: del st.session_state.analytics_data if "expanded_topic" in st.session_state: del st.session_state.expanded_topic if "topic_indices" in st.session_state: del st.session_state.topic_indice def display_session_analytics(session, course_id): """Display session analytics for faculty""" st.header("Session Analytics") # Display Pre-class Analytics display_preclass_analytics2(session, course_id) # Display In-class Analytics display_inclass_analytics(session, course_id) # Display Post-class Analytics display_postclass_analytics(session, course_id) # def upload_preclass_materials(session_id, course_id): # """Upload pre-class materials for a session""" # st.subheader("Upload Pre-class Materials") # # File upload section # uploaded_file = st.file_uploader("Upload Material", type=['txt', 'pdf', 'docx']) # if uploaded_file is not None: # with st.spinner("Processing document..."): # file_name = uploaded_file.name # file_content = extract_text_from_file(uploaded_file) # if file_content: # material_type = st.selectbox("Select Material Type", ["pdf", "docx", "txt"]) # if st.button("Upload Material"): # upload_resource(course_id, session_id, file_name, uploaded_file, material_type) # # Search for the newly uploaded resource's _id in resources_collection # resource_id = resources_collection.find_one({"file_name": file_name})["_id"] # create_vector_store(file_content, resource_id) # st.success("Material uploaded successfully!") # # Display existing materials # materials = resources_collection.find({"course_id": course_id, "session_id": session_id}) # for material in materials: # st.markdown(f""" # * **{material['file_name']}** ({material['material_type']}) # Uploaded on: {material['uploaded_at'].strftime('%Y-%m-%d %H:%M')} # """) def upload_preclass_materials(session_id, course_id, student_id): """Upload pre-class materials and manage external resources for a session""" st.subheader("Pre-class Materials Management") # Create tabs for different functionalities upload_tab, external_tab, pre_class_tab, pre_class_evaluate = st.tabs( ["Upload Materials", "External Resources", "Pre-class Questions", "Pre-class Evaluation"] ) with upload_tab: # Original file upload functionality uploaded_file = st.file_uploader("Upload Material", type=["txt", "pdf", "docx"]) if uploaded_file is not None: with st.spinner("Processing document..."): file_name = uploaded_file.name file_content = extract_text_from_file(uploaded_file) if file_content: material_type = st.selectbox( "Select Material Type", ["pdf", "docx", "txt"] ) if st.button("Upload Material"): upload_resource( course_id, session_id, file_name, uploaded_file, material_type, ) st.success("Material uploaded successfully!") with external_tab: # Fetch and display external resources session_data = courses_collection.find_one( {"course_id": course_id, "sessions.session_id": session_id}, {"sessions.$": 1}, ) if session_data and session_data.get("sessions"): session = session_data["sessions"][0] external = session.get("external_resources", {}) # Display web articles if "readings" in external: st.subheader("Web Articles and Videos") for reading in external["readings"]: col1, col2 = st.columns([3, 1]) with col1: st.markdown(f"**{reading['title']}**") st.markdown( f"Type: {reading['type']} | Est. time: {reading['estimated_read_time']}" ) st.markdown(f"URL: [{reading['url']}]({reading['url']})") with col2: if st.button( "Extract Content", key=f"extract_{reading['url']}" ): with st.spinner("Extracting content..."): content = extract_external_content( reading["url"], reading["type"] ) if content: resource_id = upload_external_resource( course_id, session_id, reading["title"], content, reading["type"].lower(), reading["url"], ) st.success( "Content extracted and stored successfully!" ) # Display books if "books" in external: st.subheader("Recommended Books") for book in external["books"]: st.markdown( f""" **{book['title']}** by {book['author']} - ISBN: {book['isbn']} - Chapters: {book['chapters']} """ ) # Display additional resources if "additional_resources" in external: st.subheader("Additional Resources") for resource in external["additional_resources"]: st.markdown( f""" **{resource['title']}** ({resource['type']}) - {resource['description']} - URL: [{resource['url']}]({resource['url']}) """ ) with pre_class_tab: if st.session_state.user_type == "faculty": faculty_id = st.session_state.user_id st.subheader("Create Pre class Subjective Test") # Create a form for test generation with st.form("pre_create_subjective_test_form"): test_title = st.text_input("Test Title") num_subjective_questions = st.number_input( "Number of Pre class Subjective Questions", min_value=1, value=5 ) generation_method = st.radio( "Question Generation Method", ["Generate from Pre-class Materials"] ) generate_test_btn = st.form_submit_button("Generate Test for Pre Class") # Handle test generation outside the form if generate_test_btn: if not test_title: st.error("Please enter a test title.") return context = "" if generation_method == "Generate from Pre-class Materials": materials = resources_collection.find( {"session_id": session["session_id"]} ) for material in materials: if "text_content" in material: context += material["text_content"] + "\n" with st.spinner("Generating questions and synoptic..."): try: # Store generated content in session state to persist between rerenders questions = pre_generate_questions( context if context else None, num_subjective_questions, session["title"], session.get("description", ""), ) if questions: synoptic = generate_synoptic( questions, context if context else None, session["title"], num_subjective_questions, ) if synoptic: # Store in session state st.session_state.generated_questions = questions st.session_state.generated_synoptic = synoptic st.session_state.test_title = test_title # Display preview st.subheader( "Preview Subjective Questions and Synoptic" ) for i, (q, s) in enumerate(zip(questions, synoptic), 1): st.markdown(f"**Question {i}:** {q['question']}") with st.expander(f"View Synoptic {i}"): st.markdown(s) # Save button outside the form if st.button("Pre Save Test"): test_id = pre_save_subjective_test( course_id, session["session_id"], test_title, questions, ) if test_id: st.success( "Subjective test saved successfully!" ) else: st.error("Error saving subjective test.") else: st.error( "Error generating synoptic answers. Please try again." ) else: st.error("Error generating questions. Please try again.") except Exception as e: st.error(f"An error occurred: {str(e)}") # Display previously generated test if it exists in session state elif hasattr(st.session_state, "generated_questions") and hasattr( st.session_state, "generated_synoptic" ): st.subheader("Preview Subjective Questions and Synoptic") for i, (q, s) in enumerate( zip( st.session_state.generated_questions, st.session_state.generated_synoptic, ), 1, ): st.markdown(f"**Question {i}:** {q['question']}") with st.expander(f"View Synoptic {i}"): st.markdown(s) if st.button("Pre Save Test"): test_id = pre_save_subjective_test( course_id, session["session_id"], st.session_state.test_title, st.session_state.generated_questions, ) if test_id: st.success("Subjective test saved successfully!") # Clear session state after successful save del st.session_state.generated_questions del st.session_state.generated_synoptic del st.session_state.test_title else: st.error("Error saving subjective test.") with pre_class_evaluate: from subjective_test_evaluation import pre_display_evaluation_to_faculty pre_display_evaluation_to_faculty(session["session_id"], student_id, course_id) def extract_external_content(url, content_type): """Extract content from external resources based on their type""" try: if content_type.lower() == "video" and "youtube.com" in url: return extract_youtube_transcript(url) else: return extract_web_article(url) except Exception as e: st.error(f"Error extracting content: {str(e)}") return None def extract_youtube_transcript(url): """Extract transcript from YouTube videos""" try: # Extract video ID from URL video_id = url.split("v=")[1].split("&")[0] # Get transcript transcript = YouTubeTranscriptApi.get_transcript(video_id) # Combine transcript text full_text = " ".join([entry["text"] for entry in transcript]) return full_text except Exception as e: st.error(f"Could not extract YouTube transcript: {str(e)}") return None def extract_web_article(url): """Extract text content from web articles""" try: headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36" } response = requests.get(url, headers=headers) response.raise_for_status() soup = BeautifulSoup(response.text, "html.parser") # Remove unwanted tags for tag in soup(["script", "style", "nav", "footer", "header"]): tag.decompose() # Extract text from paragraphs paragraphs = soup.find_all("p") text_content = " ".join([p.get_text().strip() for p in paragraphs]) return text_content except Exception as e: st.error(f"Could not extract web article content: {str(e)}") return None def upload_external_resource( course_id, session_id, title, content, content_type, source_url ): """Upload extracted external resource content to the database""" resource_data = { "_id": ObjectId(), "course_id": course_id, "session_id": session_id, "file_name": f"{title} ({content_type})", "file_type": "external", "text_content": content, "material_type": content_type, "source_url": source_url, "uploaded_at": datetime.utcnow(), } # Check if resource already exists existing_resource = resources_collection.find_one( {"session_id": session_id, "source_url": source_url} ) if existing_resource: return existing_resource["_id"] # Insert new resource resources_collection.insert_one(resource_data) resource_id = resource_data["_id"] # Update course document courses_collection.update_one( {"course_id": course_id, "sessions.session_id": session_id}, {"$push": {"sessions.$.pre_class.resources": resource_id}}, ) if content: create_vector_store(content, resource_id) return resource_id def display_quiz_tab(student_id, course_id, session_id): """Display quizzes for students""" st.header("Course Quizzes") # Get available quizzes for this session quizzes = quizzes_collection.find( {"course_id": course_id, "session_id": session_id, "status": "active"} ) quizzes = list(quizzes) if not quizzes: st.info("No quizzes available for this session.") return for quiz in quizzes: with st.expander(f"📝 {quiz['title']}", expanded=True): # Check if student has already taken this quiz existing_score = get_student_quiz_score(quiz["_id"], student_id) if existing_score is not None: st.success(f"Quiz completed! Your score: {existing_score:.1f}%") # Display correct answers after submission st.subheader("Quiz Review") for i, question in enumerate(quiz["questions"]): st.markdown(f"**Question {i+1}:** {question['question']}") for opt in question["options"]: if opt.startswith(question["correct_option"]): st.markdown(f"✅ {opt}") else: st.markdown(f"- {opt}") else: # Display quiz questions st.write("Please select your answers:") # Create a form for quiz submission with st.form(f"quiz_form_{quiz['_id']}"): student_answers = {} for i, question in enumerate(quiz["questions"]): st.markdown(f"**Question {i+1}:** {question['question']}") options = [opt for opt in question["options"]] student_answers[str(i)] = st.radio( f"Select answer for question {i+1}:", options=options, key=f"q_{quiz['_id']}_{i}", ) # Submit button if st.form_submit_button("Submit Quiz"): print(student_answers) score = submit_quiz_answers( quiz["_id"], student_id, student_answers ) if score is not None: st.success( f"Quiz submitted successfully! Your score: {score:.1f}%" ) st.rerun() # Refresh to show results else: st.error("Error submitting quiz. Please try again.") def display_surprise_quiz_tab(student_id, course_id, session_id): """Display quizzes for students""" st.header("Surprise Quizzes") # Get available quizzes for this session quizzes = surprise_quizzes_collection.find( {"course_id": course_id, "session_id": session_id, "status": "active"} ) quizzes = list(quizzes) if not quizzes: st.info("No surprise quizzes available for this session.") return for quiz in quizzes: with st.expander(f"📝 {quiz['title']}", expanded=True): # Check if student has already taken this quiz existing_score = get_student_surprise_quiz_score(quiz["_id"], student_id) if existing_score is not None: st.success(f"Quiz completed! Your score: {existing_score:.1f}%") # Display correct answers after submission st.subheader("Quiz Review") for i, question in enumerate(quiz["questions"]): st.markdown(f"**Question {i+1}:** {question['question']}") for opt in question["options"]: if opt.startswith(question["correct_option"]): st.markdown(f"✅ {opt}") else: st.markdown(f"- {opt}") # if quiz["created_at"] + timedelta(minutes=quiz["no_minutes"]) < datetime.now(): # print(quiz["created_at"]) # print(datetime.now()) # print(quiz["created_at"] + timedelta(minutes=quiz["no_minutes"])) # print(type(quiz["created_at"])) # st.error("You didn't submit the quiz on time") else: # Display quiz questions st.write("Please select your answers:") # Create a form for quiz submission with st.form(f"surprize_quiz_form_{quiz['_id']}"): student_answers = {} for i, question in enumerate(quiz["questions"]): st.markdown(f"**Question {i+1}:** {question['question']}") options = [opt for opt in question["options"]] student_answers[str(i)] = st.radio( f"Select answer for question {i+1}:", options=options, key=f"q_{quiz['_id']}_{i}", ) # Submit button if st.form_submit_button("Submit Surprise Quiz"): print(student_answers) if quiz["created_at"] + timedelta(minutes=quiz["no_minutes"]) >= datetime.now(): score = submit_surprise_quiz_answers( quiz["_id"], student_id, student_answers ) if score is not None: st.success( f"Quiz submitted successfully! Your score: {score:.1f}%" ) st.rerun() # Refresh to show results else: st.error("Error submitting quiz. Please try again.") else: st.error("You didn't submit the quiz on time") def display_session_content(student_id, course_id, session, username, user_type): st.title(f"{session['title']}") # Check if the date is a string or a datetime object if isinstance(session["date"], str): session_date = datetime.fromisoformat(session["date"]) else: session_date = session["date"] course_name = courses_collection.find_one({"course_id": course_id})["title"] st.markdown(f"**Date:** {format_datetime(session_date)}") st.markdown(f"**Course Name:** {course_name}") if user_type == "student": tabs = st.tabs( [ "Pre-class Work", "In-class Work", "Post-class Work", "Quizzes", "Subjective Tests", "Group Work", "End Terms", ] ) if len(tabs) <= 7: with tabs[0]: display_preclass_content(session, student_id, course_id) with tabs[1]: display_in_class_content(session, user_type, course_id, user_type) with tabs[2]: display_post_class_content(session, student_id, course_id) with tabs[3]: display_quiz_tab(student_id, course_id, session["session_id"]) with tabs[4]: display_subjective_test_tab( student_id, course_id, session["session_id"] ) # Added this line with tabs[5]: # display_group_work_tab(session, student_id) st.info("End term content will be available soon.") with tabs[6]: st.subheader("End Terms") st.info("End term content will be available soon.") else: # faculty user tabs = st.tabs( [ "Pre-class Work", "In-class Work", "Post-class Work", "Pre-class Analytics", "In-class Analytics", "Post-class Analytics", "Rubrics", "End Terms", "Evaluate Subjective Tests", # New tab for evaluation ] ) with tabs[0]: upload_preclass_materials(session["session_id"], course_id, student_id) with tabs[1]: display_in_class_content(session, user_type, course_id, user_type) with tabs[2]: display_post_class_content(session, student_id, course_id) with tabs[3]: display_preclass_analytics2(session, course_id) with tabs[4]: display_inclass_analytics(session, course_id) with tabs[5]: display_postclass_analytics(session, course_id) with tabs[6]: display_rubrics_tab(session, course_id) with tabs[7]: st.subheader("End Terms") st.info("End term content will be available soon.") with tabs[8]: # New tab for evaluation display_evaluation_to_faculty(session["session_id"], student_id, course_id) def parse_model_response(response_text): """Enhanced parser for model responses with better error handling. Args: response_text (str): Raw response text from the model Returns: dict or list: Parsed response object Raises: ValueError: If parsing fails """ import json import ast import re # Remove markdown formatting and whitespace cleaned_text = re.sub(r"```[a-zA-Z]*\n", "", response_text) cleaned_text = cleaned_text.replace("```", "").strip() # Try multiple parsing methods parsing_methods = [ # Method 1: Direct JSON parsing lambda x: json.loads(x), # Method 2: AST literal evaluation lambda x: ast.literal_eval(x), # Method 3: Extract and parse content between curly braces lambda x: json.loads(re.search(r"\{.*\}", x, re.DOTALL).group()), # Method 4: Extract and parse content between square brackets lambda x: json.loads(re.search(r"\[.*\]", x, re.DOTALL).group()), # Method 5: Try to fix common JSON formatting issues and parse lambda x: json.loads(x.replace("'", '"').replace("\n", "\\n")), ] last_error = None for parse_method in parsing_methods: try: result = parse_method(cleaned_text) if result: # Ensure we have actual content return result except Exception as e: last_error = e continue raise ValueError(f"Could not parse the model's response: {last_error}") def generate_questions(context, num_questions, session_title, session_description): """Generate subjective questions based on context or session details""" try: # Construct the prompt prompt = f"""You are a professional educator creating {num_questions} subjective questions. Topic: {session_title} Description: {session_description} {'Context: ' + context if context else ''} Generate exactly {num_questions} questions in this specific format: [ {{"question": "Write your first question here?"}}, {{"question": "Write your second question here?"}} ] Requirements: 1. Questions must require detailed explanations 2. Focus on critical thinking and analysis 3. Ask for specific examples or case studies 4. Questions should test deep understanding IMPORTANT: Return ONLY the JSON array. Do not include any additional text or explanations. """ # Generate response response = model.generate_content(prompt) questions = parse_model_response(response.text) # Validate response if not isinstance(questions, list): raise ValueError("Generated content is not a list") if len(questions) != num_questions: raise ValueError( f"Generated {len(questions)} questions instead of {num_questions}" ) # Validate each question for q in questions: if not isinstance(q, dict) or "question" not in q: raise ValueError("Invalid question format") return questions except Exception as e: print(f"Error generating questions: {str(e)}") return None def pre_generate_questions(context, num_questions, session_title, session_description): """Generate subjective questions based on context or session details""" try: # Construct the prompt prompt = f"""You are a professional educator creating {num_questions} subjective questions for asking before the class. Topic: {session_title} Description: {session_description} {'Context: ' + context if context else ''} Generate exactly {num_questions} questions in this specific format: [ {{"question": "Write your first question here?"}}, {{"question": "Write your second question here?"}} ] Requirements: 1. Questions should ask something about prerequisites or prior knowledge 2. Focus on critical thinking and analysis 3. Ask for specific examples or case studies 4. Questions should test deep understanding IMPORTANT: Return ONLY the JSON array. Do not include any additional text or explanations. """ # Generate response response = model.generate_content(prompt) questions = parse_model_response(response.text) # Validate response if not isinstance(questions, list): raise ValueError("Generated content is not a list") if len(questions) != num_questions: raise ValueError( f"Generated {len(questions)} questions instead of {num_questions}" ) # Validate each question for q in questions: if not isinstance(q, dict) or "question" not in q: raise ValueError("Invalid question format") return questions except Exception as e: print(f"Error generating questions: {str(e)}") return None def generate_synoptic(questions, context, session_title, num_questions): """Generate synoptic answers for the questions with improved error handling and response validation. Args: questions (list): List of question dictionaries context (str): Additional context for answer generation session_title (str): Title of the session num_questions (int): Expected number of questions Returns: list: List of synoptic answers or None if generation fails """ try: # First, let's validate our input if not questions or not isinstance(questions, list): raise ValueError("Questions must be provided as a non-empty list") # Format questions for better prompt clarity formatted_questions = "\n".join( f"{i+1}. {q['question']}" for i, q in enumerate(questions) ) # Construct a more structured prompt prompt = f"""You are a subject matter expert creating detailed model answers for {num_questions} questions about {session_title}. Here are the questions: {formatted_questions} {f'Additional context: {context}' if context else ''} Please provide {num_questions} comprehensive answers following this JSON format: {{ "answers": [ {{ "answer": "Your detailed answer for question 1...", "key_points": ["Point 1", "Point 2", "Point 3"] }}, {{ "answer": "Your detailed answer for question 2...", "key_points": ["Point 1", "Point 2", "Point 3"] }} ] }} Requirements for each answer: 1. Minimum 150 words 2. Include specific examples and evidence 3. Reference key concepts and terminology 4. Demonstrate critical analysis 5. Structure with clear introduction, body, and conclusion IMPORTANT: Return ONLY the JSON object with the answers array. No additional text. """ # Generate response response = model.generate_content(prompt) # Parse and validate the response parsed_response = parse_model_response(response.text) # Additional validation of parsed response if not isinstance(parsed_response, (dict, list)): raise ValueError("Response must be a dictionary or list") # Handle both possible valid response formats if isinstance(parsed_response, dict): answers = parsed_response.get("answers", []) else: # If it's a list answers = parsed_response # Validate answer count if len(answers) != num_questions: raise ValueError(f"Expected {num_questions} answers, got {len(answers)}") # Extract just the answer texts for consistency with existing code final_answers = [] for ans in answers: if isinstance(ans, dict): answer_text = ans.get("answer", "") key_points = ans.get("key_points", []) formatted_answer = f"{answer_text}\n\nKey Points:\n" + "\n".join( f"â€ĸ {point}" for point in key_points ) final_answers.append(formatted_answer) else: final_answers.append(str(ans)) # Final validation of the answers for i, answer in enumerate(final_answers): if not answer or len(answer.split()) < 50: # Basic length check raise ValueError(f"Answer {i+1} is too short or empty") # Save the synoptic to the synoptic_store collection synoptic_data = { "session_title": session_title, "questions": questions, "synoptic": final_answers, "created_at": datetime.utcnow(), } synoptic_store_collection.insert_one(synoptic_data) return final_answers except Exception as e: # Log the error for debugging print(f"Error in generate_synoptic: {str(e)}") print( f"Response text: {response.text if 'response' in locals() else 'No response generated'}" ) return None def save_subjective_test(course_id, session_id, title, questions): """Save subjective test to database with proper ID handling""" try: # Ensure proper string format for IDs course_id = str(course_id) session_id = str(session_id) # Format questions formatted_questions = [] for q in questions: formatted_question = { "question": q["question"], "expected_points": [], "difficulty_level": "medium", "suggested_time": "5 minutes", } formatted_questions.append(formatted_question) test_data = { "course_id": course_id, "session_id": session_id, "title": title, "questions": formatted_questions, "created_at": datetime.utcnow(), "status": "active", "submissions": [], } result = subjective_tests_collection.insert_one(test_data) return str(result.inserted_id) except Exception as e: print(f"Error saving test: {e}") return None def pre_save_subjective_test(course_id, session_id, title, questions): """Save subjective test to database with proper ID handling""" try: # Ensure proper string format for IDs course_id = str(course_id) session_id = str(session_id) # Format questions formatted_questions = [] for q in questions: formatted_question = { "question": q["question"], "expected_points": [], "difficulty_level": "medium", "suggested_time": "5 minutes", } formatted_questions.append(formatted_question) test_data = { "course_id": course_id, "session_id": session_id, "title": title, "questions": formatted_questions, "created_at": datetime.utcnow(), "status": "active", "submissions": [], } result = pre_subjective_tests_collection.insert_one(test_data) return str(result.inserted_id) except Exception as e: print(f"Error saving test: {e}") return None def submit_subjective_test(test_id, student_id, answers): """Submit test answers with proper ID handling""" try: # Ensure IDs are strings test_id = str(test_id) student_id = str(student_id) # Create submission document submission = { "student_id": student_id, "answers": answers, "submitted_at": datetime.utcnow(), "status": "submitted", } # Update test document with new submission result = subjective_tests_collection.update_one( {"_id": ObjectId(test_id)}, {"$push": {"submissions": submission}} ) return result.modified_count > 0 except Exception as e: print(f"Error submitting test: {e}") return False def submit_pre_subjective_test(test_id, student_id, answers): """Submit test answers with proper ID handling""" try: # Ensure IDs are strings test_id = str(test_id) student_id = str(student_id) # Create submission document submission = { "student_id": student_id, "answers": answers, "submitted_at": datetime.utcnow(), "status": "submitted", } # Update test document with new submission result = pre_subjective_tests_collection.update_one( {"_id": ObjectId(test_id)}, {"$push": {"submissions": submission}} ) return result.modified_count > 0 except Exception as e: print(f"Error submitting test: {e}") return False def display_subjective_test_tab(student_id, course_id, session_id): """Display subjective tests and results for students""" st.header("Subjective Tests") try: subjective_tests = list( subjective_tests_collection.find( {"course_id": course_id, "session_id": session_id, "status": "active"} ) ) if not subjective_tests: st.info("No subjective tests available for this session.") return # Create tabs for Tests and Results test_tab, results_tab = st.tabs(["Available Tests", "Test Results"]) with test_tab: for test in subjective_tests: with st.expander(f"📝 {test['title']}", expanded=True): # Check for existing submission existing_submission = next( ( sub for sub in test.get("submissions", []) if sub["student_id"] == str(student_id) ), None, ) if existing_submission: st.success("Test completed! Your answers have been submitted.") st.subheader("Your Answers") for i, ans in enumerate(existing_submission["answers"]): st.markdown( f"**Question {i+1}:** {test['questions'][i]['question']}" ) st.markdown(f"**Your Answer:** {ans}") st.markdown("---") else: st.write("Please write your answers:") with st.form(key=f"subjective_test_form_{test['_id']}"): student_answers = [] for i, question in enumerate(test["questions"]): st.markdown( f"**Question {i+1}:** {question['question']}" ) answer = st.text_area( "Your answer:", key=f"q_{test['_id']}_{i}", height=200, ) student_answers.append(answer) if st.form_submit_button("Submit Test"): if all(answer.strip() for answer in student_answers): success = submit_subjective_test( test["_id"], str(student_id), student_answers ) if success: st.success("Test submitted successfully!") st.rerun() else: st.error( "Error submitting test. Please try again." ) else: st.error( "Please answer all questions before submitting." ) with results_tab: # Display results for completed tests completed_tests = [ test for test in subjective_tests if any( sub["student_id"] == str(student_id) for sub in test.get("submissions", []) ) ] if not completed_tests: st.info("You haven't completed any tests yet.") return # Create a selectbox for choosing which test results to view test_options = { f"{test['title']} (Submitted: {next(sub['submitted_at'].strftime('%Y-%m-%d') for sub in test['submissions'] if sub['student_id'] == str(student_id))})": test[ "_id" ] for test in completed_tests } selected_test = st.selectbox( "Select a test to view results:", options=list(test_options.keys()) ) if selected_test: test_id = test_options[selected_test] display_test_results(test_id, student_id) except Exception as e: st.error("An error occurred while loading the tests. Please try again later.") print(f"Error in display_subjective_test_tab: {str(e)}") def display_pre_subjective_test_tab(student_id, course_id, session_id): """Display subjective tests and results for students""" st.header("Pre Subjective Tests") try: print("course_id", course_id, "session_id", session_id, "student_id", student_id) subjective_tests = list( pre_subjective_tests_collection.find( {"course_id": course_id,"session_id" : str(session_id), "status": "active"} ) ) print("subjective_tests", subjective_tests) if not subjective_tests: print("bruh") st.info("No pre subjective tests available for this session.") return # Create tabs for Tests and Results test_tab, results_tab = st.tabs(["Available Tests", "Test Results"]) with test_tab: for test in subjective_tests: with st.expander(f"📝 {test['title']}", expanded=True): # Check for existing submission existing_submission = next( ( sub for sub in test.get("submissions", []) if sub["student_id"] == str(student_id) ), None, ) if existing_submission: st.success("Test completed! Your answers have been submitted.") st.subheader("Your Answers") for i, ans in enumerate(existing_submission["answers"]): st.markdown( f"**Question {i+1}:** {test['questions'][i]['question']}" ) st.markdown(f"**Your Answer:** {ans}") st.markdown("---") else: st.write("Please write your answers:") with st.form(key=f"pre_subjective_test_form_{test['_id']}"): student_answers = [] for i, question in enumerate(test["questions"]): st.markdown( f"**Question {i+1}:** {question['question']}" ) answer = st.text_area( "Your answer:", key=f"q_{test['_id']}_{i}", height=200, ) student_answers.append(answer) if st.form_submit_button("Submit Pre Test"): if all(answer.strip() for answer in student_answers): success = submit_pre_subjective_test( test["_id"], str(student_id), student_answers ) if success: st.success("Test submitted successfully!") st.rerun() else: st.error( "Error submitting test. Please try again." ) else: st.error( "Please answer all questions before submitting." ) with results_tab: # Display results for completed tests completed_tests = [ test for test in subjective_tests if any( sub["student_id"] == str(student_id) for sub in test.get("submissions", []) ) ] if not completed_tests: st.info("You haven't completed any tests yet.") return # Create a selectbox for choosing which test results to view test_options = { f"{test['title']} (Submitted: {next(sub['submitted_at'].strftime('%Y-%m-%d') for sub in test['submissions'] if sub['student_id'] == str(student_id))})": test[ "_id" ] for test in completed_tests } selected_test = st.selectbox( "Select a test to view results:", options=list(test_options.keys()) ) if selected_test: test_id = test_options[selected_test] display_pre_test_results(test_id, student_id) except Exception as e: st.error("An error occurred while loading the tests. Please try again later.") print(f"Error in display_subjective_test_tab: {str(e)}") def display_test_results(test_id, student_id): """ Display test results and analysis for a student Args: test_id: ObjectId or str of the test student_id: str of the student ID """ try: # Fetch analysis from evaluation collection analysis = subjective_test_evaluation_collection.find_one( {"test_id": test_id, "student_id": str(student_id)} ) if not analysis: st.info("Analysis will be available soon. Please check back later.") return st.header("Test Analysis") # Display overall evaluation summary if available if "overall_summary" in analysis: with st.expander("Overall Performance Summary", expanded=True): st.markdown(analysis["overall_summary"]) # Display individual question evaluations st.subheader("Question-wise Analysis") for eval_item in analysis.get("evaluations", []): with st.expander(f"Question {eval_item['question_number']}", expanded=True): st.markdown("**Question:**") st.markdown(eval_item["question"]) st.markdown("**Your Answer:**") st.markdown(eval_item["answer"]) st.markdown("**Evaluation:**") st.markdown(eval_item["evaluation"]) # Extract and display score if available if "Score:" in eval_item["evaluation"]: score_line = next( ( line for line in eval_item["evaluation"].split("\n") if "Score:" in line ), None, ) if score_line: score = score_line.split("Score:")[1].strip() st.metric("Score", score) # Display improvement points if available if "Key Areas for Improvement" in eval_item["evaluation"]: st.markdown("**Areas for Improvement:**") improvement_section = eval_item["evaluation"].split( "Key Areas for Improvement" )[1] points = [ point.strip("- ").strip() for point in improvement_section.split("\n") if point.strip().startswith("-") ] for point in points: if point: # Only display non-empty points st.markdown(f"â€ĸ {point}") # Display evaluation timestamp if "evaluated_at" in analysis: st.caption( f"Analysis generated on: {analysis['evaluated_at'].strftime('%Y-%m-%d %H:%M:%S UTC')}" ) except Exception as e: st.error( "An error occurred while loading the analysis. Please try again later." ) print(f"Error in display_test_results: {str(e)}") def display_pre_test_results(test_id, student_id): """ Display test results and analysis for a student Args: test_id: ObjectId or str of the test student_id: str of the student ID """ try: # Fetch analysis from evaluation collection analysis = pre_subjective_test_evaluation_collection.find_one( {"test_id": test_id, "student_id": str(student_id)} ) if not analysis: st.info("Analysis will be available soon. Please check back later.") return st.header("Test Analysis") # Display overall evaluation summary if available if "overall_summary" in analysis: with st.expander("Overall Performance Summary", expanded=True): st.markdown(analysis["overall_summary"]) # Display individual question evaluations st.subheader("Question-wise Analysis") for eval_item in analysis.get("evaluations", []): with st.expander(f"Question {eval_item['question_number']}", expanded=True): st.markdown("**Question:**") st.markdown(eval_item["question"]) st.markdown("**Your Answer:**") st.markdown(eval_item["answer"]) st.markdown("**Evaluation:**") st.markdown(eval_item["evaluation"]) # Extract and display score if available if "Score:" in eval_item["evaluation"]: score_line = next( ( line for line in eval_item["evaluation"].split("\n") if "Score:" in line ), None, ) if score_line: score = score_line.split("Score:")[1].strip() st.metric("Score", score) # Display improvement points if available if "Key Areas for Improvement" in eval_item["evaluation"]: st.markdown("**Areas for Improvement:**") improvement_section = eval_item["evaluation"].split( "Key Areas for Improvement" )[1] points = [ point.strip("- ").strip() for point in improvement_section.split("\n") if point.strip().startswith("-") ] for point in points: if point: # Only display non-empty points st.markdown(f"â€ĸ {point}") # Display evaluation timestamp if "evaluated_at" in analysis: st.caption( f"Analysis generated on: {analysis['evaluated_at'].strftime('%Y-%m-%d %H:%M:%S UTC')}" ) except Exception as e: st.error( "An error occurred while loading the analysis. Please try again later." ) print(f"Error in display_test_results: {str(e)}")