diff --git "a/session_page.py" "b/session_page.py"
--- "a/session_page.py"
+++ "b/session_page.py"
@@ -1,2804 +1,2494 @@
-from collections import defaultdict
-import json
-import random
-import requests
-import streamlit as st
-from datetime import datetime
-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 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
-
-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"]
-synoptic_store_collection = db["synoptic_store"]
-
-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"""
- # st.subheader("Pre-class Materials")
- # print("Session ID is: ", session['session_id'])
- # # Display pre-class materials
- # materials = resources_collection.find({"session_id": session['session_id']})
- # 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['_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")
- """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)}")
-
-
-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_in_class_content(session, 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'])
-
-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,
- synoptic
- )
- 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,
- st.session_state.generated_synoptic
- )
- 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 Assignments")
- # Add assignment form
- with st.form("add_assignment_form"):
- title = st.text_input("Assignment Title")
- due_date = st.date_input("Due Date")
- submit = st.form_submit_button("Add Assignment")
-
- if submit:
- due_date = datetime.combine(due_date, datetime.min.time())
- # Save the assignment to the database
- assignment = {
- "id": ObjectId(),
- "title": title,
- "due_date": due_date,
- "status": "pending",
- "submissions": []
- }
- courses_collection2.update_one(
- {"course_id": course_id, "sessions.session_id": session['session_id']},
- {"$push": {"sessions.$.post_class.assignments": assignment}}
- )
- st.success("Assignment added successfully!")
- else:
- # Display assignments
- session_data = courses_collection2.find_one(
- {"course_id": course_id, "sessions.session_id": session['session_id']},
- {"sessions.$": 1}
- )
-
- if session_data and "sessions" in session_data and len(session_data["sessions"]) > 0:
- assignments = session_data["sessions"][0].get("post_class", {}).get("assignments", [])
- for assignment in assignments:
- title = assignment.get("title", "No Title")
- due_date = assignment.get("due_date", "No Due Date")
- status = assignment.get("status", "No Status")
- assignment_id = assignment.get("id", "No ID")
-
- with st.expander(f"Assignment: {title}", expanded=True):
- st.markdown(f"**Due Date:** {due_date}")
- st.markdown(f"**Status:** {status.replace('_', ' ').title()}")
-
- # Assignment details
- st.markdown("### Instructions")
- st.markdown("Complete the assignment according to the provided guidelines.")
-
- # File submission
- st.markdown("### Submission")
- uploaded_file = st.file_uploader(
- "Upload your work",
- type=['pdf', 'py', 'ipynb'],
- key=f"upload_{assignment['id']}"
- )
-
- if uploaded_file is not None:
- st.success("File uploaded successfully!")
-
- if st.button("Submit Assignment", key=f"submit_{assignment['id']}"):
- # Extract text content from the file
- text_content = extract_text_from_file(uploaded_file)
-
- # Call assignment_submit function
- success = assignment_submit(
- student_id=student_id,
- course_id=course_id,
- session_id=session['session_id'],
- assignment_id=assignment['id'],
- file_name=uploaded_file.name,
- file_content=uploaded_file,
- text_content=text_content,
- material_type="assignment"
- )
-
- if success:
- st.success("Assignment submitted successfully!")
- else:
- st.error("Error saving submission.")
- # Feedback section (if assignment is completed)
- if assignment['status'] == 'completed':
- st.markdown("### Feedback")
- st.info("Feedback will be provided here once the assignment is graded.")
- else:
- st.warning("No assignments found for this session.")
-
-# def display_preclass_analytics(session, course_id):
-# """Display pre-class analytics for faculty based on chat interaction metrics"""
-# st.subheader("Pre-class Analytics")
-
-# # Get all enrolled students
-# # enrolled_students = list(students_collection.find({"enrolled_courses": session['course_id']}))
-# enrolled_students = list(students_collection.find({
-# "enrolled_courses.course_id": course_id
-# }))
-# # total_students = len(enrolled_students)
-
-# 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 chat history for all students in this session
-# chat_data = list(chat_history_collection.find({
-# "session_id": session['session_id']
-# }))
-
-# # Create a DataFrame to store student completion data
-# completion_data = []
-# incomplete_students = []
-
-# for student in enrolled_students:
-# student_id = student['_id']
-# student_name = student.get('full_name', 'Unknown')
-# student_sid = student.get('SID', 'Unknown')
-
-# # Find student's chat history
-# student_chat = next((chat for chat in chat_data if chat['user_id'] == student_id), None)
-
-# if student_chat:
-# messages = student_chat.get('messages', [])
-# message_count = len(messages)
-# status = "Completed" if message_count >= 20 else "Incomplete"
-
-# # Format chat history for display
-# chat_history = []
-# for msg in messages:
-# timestamp_str = msg.get('timestamp', '')
-# if isinstance(timestamp_str, str):
-# timestamp = datetime.fromisoformat(timestamp_str)
-# else:
-# timestamp = timestamp_str
-# # timestamp = msg.get('timestamp', '').strftime("%Y-%m-%d %H:%M:%S")
-# chat_history.append({
-# # 'timestamp': timestamp,
-# 'timestamp': timestamp.strftime("%Y-%m-%d %H:%M:%S"),
-# 'prompt': msg.get('prompt'),
-# 'response': msg.get('response')
-# })
-
-# message_count = len(student_chat.get('messages', []))
-# status = "Completed" if message_count >= 20 else "Incomplete"
-# if status == "Incomplete":
-# incomplete_students.append({
-# 'name': student_name,
-# 'sid': student_sid,
-# 'message_count': message_count
-# })
-# else:
-# message_count = 0
-# status = "Not Started"
-# chat_history = []
-# incomplete_students.append({
-# 'name': student_name,
-# 'sid': student_sid,
-# 'message_count': 0
-# })
-
-# completion_data.append({
-# 'Student Name': student_name,
-# 'SID': student_sid,
-# 'Messages': message_count,
-# 'Status': status,
-# 'Chat History': chat_history
-# })
-
-# # Create DataFrame
-# df = pd.DataFrame(completion_data)
-
-# # Display summary metrics
-# col1, col2, col3 = st.columns(3)
-
-# completed_count = len(df[df['Status'] == 'Completed'])
-# incomplete_count = len(df[df['Status'] == 'Incomplete'])
-# not_started_count = len(df[df['Status'] == 'Not Started'])
-
-# with col1:
-# st.metric("Completed", completed_count)
-# with col2:
-# st.metric("Incomplete", incomplete_count)
-# with col3:
-# st.metric("Not Started", not_started_count)
-
-# # Display completion rate progress bar
-# completion_rate = (completed_count / total_students) * 100
-# st.markdown("### Overall Completion Rate")
-# st.progress(completion_rate / 100)
-# st.markdown(f"**{completion_rate:.1f}%** of students have completed pre-class materials")
-
-# # Create tabs for different views
-# tab1, tab2 = st.tabs(["Student Overview", "Detailed Chat History"])
-
-# with tab1:
-# # Display completion summary table
-# st.markdown("### Student Completion Details")
-# summary_df = df[['Student Name', 'SID', 'Messages', 'Status']].copy()
-# st.dataframe(
-# summary_df.style.apply(lambda x: ['background-color: #90EE90' if v == 'Completed'
-# else 'background-color: #FFB6C1' if v == 'Incomplete'
-# else 'background-color: #FFE4B5'
-# for v in x],
-# subset=['Status'])
-# )
-
-# with tab2:
-# # Display detailed chat history
-# st.markdown("### Student Chat Histories")
-
-# # Add student selector
-# selected_student = st.selectbox(
-# "Select a student to view chat history:",
-# options=df['Student Name'].tolist()
-# )
-
-# # Get selected student's data
-# student_data = df[df['Student Name'] == selected_student].iloc[0]
-# print(student_data)
-# chat_history = student_data['Chat History']
-# # Refresh chat history when a new student is selected
-# if 'selected_student' not in st.session_state or st.session_state.selected_student != selected_student:
-# st.session_state.selected_student = selected_student
-# st.session_state.selected_student_chat_history = chat_history
-# else:
-# chat_history = st.session_state.selected_student_chat_history
-# # Display student info and chat statistics
-# st.markdown(f"**Student ID:** {student_data['SID']}")
-# st.markdown(f"**Status:** {student_data['Status']}")
-# st.markdown(f"**Total Messages:** {student_data['Messages']}")
-
-# # Display chat history in a table
-# if chat_history:
-# chat_df = pd.DataFrame(chat_history)
-# st.dataframe(
-# chat_df.style.apply(lambda x: ['background-color: #E8F0FE' if v == 'response' else 'background-color: #FFFFFF' for v in x], subset=['prompt']), use_container_width=True
-# )
-# else:
-# st.info("No chat history available for this student.")
-
-# # Display students who haven't completed
-# if incomplete_students:
-# st.markdown("### Students Requiring Follow-up")
-# incomplete_df = pd.DataFrame(incomplete_students)
-# st.markdown(f"**{len(incomplete_students)} students** need to complete the pre-class materials:")
-
-# # Create a styled table for incomplete students
-# st.table(
-# incomplete_df.style.apply(lambda x: ['background-color: #FFFFFF'
-# for _ in range(len(x))]))
-
-# # Export option for incomplete students list
-# csv = incomplete_df.to_csv(index=False).encode('utf-8')
-# st.download_button(
-# "Download Follow-up List",
-# csv,
-# "incomplete_students.csv",
-# "text/csv",
-# key='download-csv'
-# )
-
-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": {"$nin": respondents}
- }))
-
-
-
-
- if non_participants:
- st.markdown("#### Students Who Haven't Participated")
- non_participant_data = [{
- 'Name': student.get('name', 'Unknown'),
- 'SID': student.get('sid', 'Unknown')
- } for student in non_participants]
- st.table(pd.DataFrame(non_participant_data))
-
- # Export functionality for participation data
- st.markdown("### Export Analytics")
-
- if st.button("Download Poll Analytics Report"):
- # Create a more detailed DataFrame for export
- export_data = []
- for poll in polls_list:
- poll_data = {
- 'Poll Question': poll.get('question', 'Untitled'),
- 'Total Responses': len(poll.get('respondents', [])),
- 'Participation Rate': f"{(len(poll.get('respondents', [])) / total_students * 100):.1f}%"
- }
- # Add response distribution
- for option, votes in poll.get('responses', {}).items():
- poll_data[f"Option: {option}"] = votes
- export_data.append(poll_data)
-
- export_df = pd.DataFrame(export_data)
- csv = export_df.to_csv(index=False).encode('utf-8')
- st.download_button(
- "📥 Download Complete Report",
- csv,
- "poll_analytics.csv",
- "text/csv",
- key='download-csv'
- )
-
-def display_postclass_analytics(session, course_id):
- """Display post-class analytics for faculty"""
- st.subheader("Post-class Analytics")
-
- # Get all assignments for this session
- session_data = courses_collection2.find_one(
- {"sessions.session_id": session['session_id']},
- {"sessions.$": 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
-
- {"".join([f"- {issue}
" for issue in topic["key_issues"]])}
-
-
Key Misconceptions
-
- {"".join([f"- {misc}
" for misc in topic["key_misconceptions"]])}
-
-
- """, unsafe_allow_html=True)
- 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('
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"""
-
-
-
-
-
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('
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
+
+ {"".join([f"- {issue}
" for issue in topic["key_issues"]])}
+
+
Key Misconceptions
+
+ {"".join([f"- {misc}
" for misc in topic["key_misconceptions"]])}
+
+
+ """, unsafe_allow_html=True)
+ 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('
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"""
+
+
+
+
+
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('