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qizz + panel
This commit is contained in:
5t4l1n
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import tensorflow as tf
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import pickle
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import json
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import numpy as np
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import random
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import os
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from tensorflow.keras.preprocessing.sequence import pad_sequences
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from datetime import datetime
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from bson import ObjectId
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class AdaptiveQuizMasterLLM:
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def __init__(self, models_path="./models/"):
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"""
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Intelligent Quiz Master with optional model loading
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"""
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self.models_path = models_path
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self.model_available = False
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# Try to load model components
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try:
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# Check if model files exist
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model_file = f'{models_path}improved_cnn_model.h5'
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tokenizer_file = f'{models_path}tokenizer.pickle'
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label_encoder_file = f'{models_path}label_encoder.pickle'
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data_file = f'{models_path}processed_commonsenseqa_data.json'
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if all(os.path.exists(f) for f in [model_file, tokenizer_file, label_encoder_file, data_file]):
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# Load model with compatibility handling
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try:
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self.model = tf.keras.models.load_model(model_file)
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self.model_available = True
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print("✅ CNN Model loaded successfully")
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except Exception as model_error:
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print(f"⚠️ Model loading failed: {model_error}")
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print("🔄 Continuing without AI predictions...")
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self.model = None
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self.model_available = False
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# Load other components
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with open(tokenizer_file, 'rb') as f:
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self.tokenizer = pickle.load(f)
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with open(label_encoder_file, 'rb') as f:
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self.label_encoder = pickle.load(f)
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with open(data_file, 'r') as f:
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self.quiz_data = json.load(f)
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else:
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print("⚠️ Model files not found. Using fallback quiz data...")
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self.model = None
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self.tokenizer = None
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self.label_encoder = None
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self.quiz_data = self._get_fallback_questions()
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self.model_available = False
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except Exception as e:
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print(f"⚠️ Model initialization failed: {e}")
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print("🔄 Using fallback mode...")
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self.model = None
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self.tokenizer = None
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self.label_encoder = None
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self.quiz_data = self._get_fallback_questions()
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self.model_available = False
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# Separate questions by difficulty
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self.questions_by_difficulty = {
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'easy': [q for q in self.quiz_data if q.get('difficulty') == 'easy'],
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'medium': [q for q in self.quiz_data if q.get('difficulty') == 'medium'],
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'hard': [q for q in self.quiz_data if q.get('difficulty') == 'hard']
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}
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print("🤖 Adaptive Quiz Master LLM initialized!")
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print(f"📊 Model Available: {self.model_available}")
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print(f"📊 Questions: Easy({len(self.questions_by_difficulty['easy'])}), Medium({len(self.questions_by_difficulty['medium'])}), Hard({len(self.questions_by_difficulty['hard'])})")
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def _get_fallback_questions(self):
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"""
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Fallback questions when model files are not available
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"""
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return [
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{
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"question": "What is the capital of France?",
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"incorrect_answers": ["London", "Berlin", "Madrid"],
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"correct_answer": "Paris",
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"difficulty": "easy"
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},
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{
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"question": "Which programming language is known for its simplicity and readability?",
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"incorrect_answers": ["C++", "Assembly", "Java"],
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"correct_answer": "Python",
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"difficulty": "easy"
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},
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{
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"question": "What does API stand for?",
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"incorrect_answers": ["Advanced Programming Interface", "Automated Program Integration", "Applied Programming Instructions"],
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"correct_answer": "Application Programming Interface",
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"difficulty": "medium"
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},
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{
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"question": "In machine learning, what does 'overfitting' mean?",
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"incorrect_answers": ["Model performs well on all data", "Model is too simple", "Model trains too quickly"],
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"correct_answer": "Model memorizes training data but fails on new data",
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"difficulty": "medium"
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},
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{
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"question": "What is the time complexity of binary search?",
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"incorrect_answers": ["O(n)", "O(n²)", "O(n log n)"],
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"correct_answer": "O(log n)",
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"difficulty": "hard"
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},
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{
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"question": "Which design pattern ensures a class has only one instance?",
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"incorrect_answers": ["Factory", "Observer", "Strategy"],
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"correct_answer": "Singleton",
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"difficulty": "hard"
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}
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]
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def create_session(self, user_id):
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"""
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Create new adaptive quiz session
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"""
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session_id = str(ObjectId())
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session_data = {
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'session_id': session_id,
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'user_id': user_id,
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'current_difficulty': 'easy', # Always start with easy
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'consecutive_correct': {'easy': 0, 'medium': 0, 'hard': 0},
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'total_questions': 0,
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'total_correct': 0,
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'question_history': [],
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'created_at': datetime.utcnow(),
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'status': 'active'
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}
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return session_data
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def get_adaptive_question(self, session_data):
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"""
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Get next question based on current difficulty level
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"""
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current_difficulty = session_data['current_difficulty']
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available_questions = self.questions_by_difficulty[current_difficulty]
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# Avoid repeating questions
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asked_questions = [q['question_id'] for q in session_data.get('question_history', [])]
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available_questions = [q for q in available_questions
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if q.get('id', str(hash(q['question']))) not in asked_questions]
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if not available_questions:
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# Fallback to any difficulty if current level exhausted
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all_available = [q for q in self.quiz_data
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if q.get('id', str(hash(q['question']))) not in asked_questions]
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available_questions = all_available[:10] if all_available else self.quiz_data[:5]
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# Select random question
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question_data = random.choice(available_questions)
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# Create formatted question with shuffled choices
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choices = question_data['incorrect_answers'] + [question_data['correct_answer']]
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random.shuffle(choices)
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# Find correct answer position
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correct_position = choices.index(question_data['correct_answer'])
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correct_letter = chr(65 + correct_position)
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question_obj = {
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'question_id': question_data.get('id', str(hash(question_data['question']))),
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'question_text': question_data['question'],
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'choices': {
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'A': choices[0],
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'B': choices[1],
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'C': choices[2],
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'D': choices[3]
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},
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'correct_answer': correct_letter,
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'difficulty': current_difficulty,
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'explanation': f"The correct answer is {question_data['correct_answer']}."
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}
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return question_obj
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def get_llm_prediction(self, question_text, choices):
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"""
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Use trained model to predict answer (with fallback)
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"""
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if not self.model_available or not self.model:
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# Fallback: Random prediction with low confidence
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import random
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fallback_prediction = random.choice(['A', 'B', 'C', 'D'])
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return {
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'llm_prediction': fallback_prediction,
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'confidence': 0.25, # Random confidence
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'model_accuracy': 25.0, # Random accuracy
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'fallback_mode': True
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}
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try:
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# Format question for model prediction
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formatted_question = f"Difficulty: medium\nQuestion: {question_text}\n"
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formatted_question += f"A) {choices['A']}\n"
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formatted_question += f"B) {choices['B']}\n"
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formatted_question += f"C) {choices['C']}\n"
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formatted_question += f"D) {choices['D']}\n"
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# Tokenize and predict using your trained model
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sequence = self.tokenizer.texts_to_sequences([formatted_question])
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padded = pad_sequences(sequence, maxlen=400, padding='post')
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prediction = self.model.predict(padded, verbose=0)
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predicted_class = np.argmax(prediction[0])
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predicted_letter = self.label_encoder.inverse_transform([predicted_class])[0]
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confidence = float(prediction[0][predicted_class])
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return {
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'llm_prediction': predicted_letter,
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'confidence': confidence,
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'model_accuracy': 33.1, # Your model's test accuracy
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'fallback_mode': False
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}
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except Exception as e:
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print(f"⚠️ Prediction error: {e}")
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# Fallback on error
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import random
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fallback_prediction = random.choice(['A', 'B', 'C', 'D'])
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return {
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'llm_prediction': fallback_prediction,
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'confidence': 0.25,
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'model_accuracy': 25.0,
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'fallback_mode': True,
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'error': str(e)
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}
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def evaluate_answer(self, session_data, question_data, user_answer):
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"""
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Evaluate user answer and adjust difficulty according to your rules
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"""
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is_correct = (user_answer.upper() == question_data['correct_answer'])
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current_difficulty = session_data['current_difficulty']
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# Update session stats
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session_data['total_questions'] += 1
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if is_correct:
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session_data['total_correct'] += 1
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session_data['consecutive_correct'][current_difficulty] += 1
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else:
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# Reset consecutive count for current difficulty
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session_data['consecutive_correct'][current_difficulty] = 0
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# Apply your exact difficulty adjustment rules
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new_difficulty = self._adjust_difficulty(session_data, is_correct)
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# Record question in history
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question_record = {
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'question_id': question_data['question_id'],
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'question_text': question_data['question_text'],
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'user_answer': user_answer,
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'correct_answer': question_data['correct_answer'],
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'is_correct': is_correct,
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'difficulty': current_difficulty,
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'timestamp': datetime.utcnow()
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}
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session_data['question_history'].append(question_record)
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# Get LLM prediction for comparison
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llm_result = self.get_llm_prediction(question_data['question_text'], question_data['choices'])
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result = {
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'is_correct': is_correct,
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'correct_answer': question_data['correct_answer'],
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'explanation': question_data['explanation'],
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'difficulty_changed': new_difficulty != current_difficulty,
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'previous_difficulty': current_difficulty,
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'new_difficulty': new_difficulty,
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'consecutive_correct': session_data['consecutive_correct'][current_difficulty],
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'llm_prediction': llm_result,
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'session_stats': {
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'total_questions': session_data['total_questions'],
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'total_correct': session_data['total_correct'],
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'accuracy': round((session_data['total_correct'] / session_data['total_questions']) * 100, 1)
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}
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}
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session_data['current_difficulty'] = new_difficulty
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return result
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def _adjust_difficulty(self, session_data, is_correct):
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"""
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Your exact difficulty adjustment rules:
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- 3 consecutive correct: Easy→Medium→Hard
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- 1 incorrect: Hard→Medium→Easy (stay on Easy if already there)
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"""
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current_difficulty = session_data['current_difficulty']
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consecutive = session_data['consecutive_correct']
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if is_correct:
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# Move up after 3 consecutive correct answers
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if consecutive[current_difficulty] >= 3:
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if current_difficulty == 'easy':
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# Reset consecutive count for easy, start fresh for medium
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session_data['consecutive_correct']['easy'] = 0
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return 'medium'
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elif current_difficulty == 'medium':
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# Reset consecutive count for medium, start fresh for hard
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session_data['consecutive_correct']['medium'] = 0
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return 'hard'
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# If already hard, stay hard
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else:
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# Move down immediately after 1 wrong answer
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if current_difficulty == 'hard':
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return 'medium'
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elif current_difficulty == 'medium':
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return 'easy'
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# If already easy, stay easy
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return current_difficulty
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def get_session_stats(self, session_data):
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"""
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Get comprehensive session statistics
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"""
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total_questions = session_data['total_questions']
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total_correct = session_data['total_correct']
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accuracy = (total_correct / total_questions * 100) if total_questions > 0 else 0
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difficulty_stats = {}
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for difficulty in ['easy', 'medium', 'hard']:
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questions_at_level = [q for q in session_data['question_history'] if q['difficulty'] == difficulty]
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correct_at_level = sum(1 for q in questions_at_level if q['is_correct'])
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difficulty_stats[difficulty] = {
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'questions': len(questions_at_level),
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'correct': correct_at_level,
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'accuracy': round((correct_at_level / len(questions_at_level) * 100), 1) if questions_at_level else 0
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}
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return {
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'session_id': session_data['session_id'],
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'current_difficulty': session_data['current_difficulty'],
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'total_questions': total_questions,
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'total_correct': total_correct,
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'overall_accuracy': round(accuracy, 1),
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'consecutive_correct': session_data['consecutive_correct'],
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'difficulty_breakdown': difficulty_stats,
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'status': session_data['status']
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}
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@@ -0,0 +1,647 @@
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import tensorflow as tf
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import pickle
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import json
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import numpy as np
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import random
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import os
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from tensorflow.keras.preprocessing.sequence import pad_sequences
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from datetime import datetime
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from bson import ObjectId
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import uuid
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class AdaptiveQuizMasterLLM:
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def __init__(self, models_path="./models/"):
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"""
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Intelligent Quiz Master with enhanced fallback questions and AI generation
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"""
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self.models_path = models_path
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self.model_available = False
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try:
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# Try to load model files
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model_file = f'{models_path}improved_cnn_model.h5'
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tokenizer_file = f'{models_path}tokenizer.pickle'
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label_encoder_file = f'{models_path}label_encoder.pickle'
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data_file = f'{models_path}processed_commonsenseqa_data.json'
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if all(os.path.exists(f) for f in [model_file, tokenizer_file, label_encoder_file, data_file]):
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try:
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self.model = tf.keras.models.load_model(model_file)
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print("✅ CNN Model loaded successfully")
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self.model_available = True
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except Exception as e:
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print(f"⚠️ Model loading failed: {e}")
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self.model = None
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self.model_available = False
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with open(tokenizer_file, 'rb') as f:
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self.tokenizer = pickle.load(f)
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with open(label_encoder_file, 'rb') as f:
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self.label_encoder = pickle.load(f)
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with open(data_file, 'r') as f:
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self.quiz_data = json.load(f)
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else:
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print("⚠️ Model files not found. Using enhanced fallback questions...")
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self.model = None
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self.tokenizer = None
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self.label_encoder = None
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self.quiz_data = self._get_enhanced_fallback_questions()
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self.model_available = False
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except Exception as e:
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print(f"⚠️ Model initialization failed: {e}")
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self.model = None
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self.tokenizer = None
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self.label_encoder = None
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self.quiz_data = self._get_enhanced_fallback_questions()
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self.model_available = False
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# Distribute questions by difficulty
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self.questions_by_difficulty = {
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'easy': [q for q in self.quiz_data if q.get('difficulty') == 'easy'],
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'medium': [q for q in self.quiz_data if q.get('difficulty') == 'medium'],
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'hard': [q for q in self.quiz_data if q.get('difficulty') == 'hard']
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}
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# If no questions are categorized, distribute fallback questions
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if not any(self.questions_by_difficulty.values()):
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self._distribute_fallback_questions()
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print("🤖 AdaptiveQuizMasterLLM initialized")
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print(f"📊 Model Available: {self.model_available}")
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print(f"📊 Questions: Easy({len(self.questions_by_difficulty['easy'])}), "
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f"Medium({len(self.questions_by_difficulty['medium'])}), "
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f"Hard({len(self.questions_by_difficulty['hard'])})")
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def _get_enhanced_fallback_questions(self):
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"""Enhanced fallback questions with comprehensive coverage"""
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return [
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# ===== EASY QUESTIONS =====
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{
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"id": "easy_1",
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"question": "What is the capital of France?",
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"incorrect_answers": ["London", "Berlin", "Madrid"],
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"correct_answer": "Paris",
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"difficulty": "easy",
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"category": "Geography"
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},
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{
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"id": "easy_2",
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"question": "Which programming language is known for its simplicity and readability?",
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"incorrect_answers": ["C++", "Assembly", "Java"],
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"correct_answer": "Python",
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"difficulty": "easy",
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"category": "Programming"
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},
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{
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"id": "easy_3",
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"question": "What does HTML stand for?",
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"incorrect_answers": ["High Tech Modern Language", "Home Tool Markup Language", "Hyperlink Text Language"],
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"correct_answer": "HyperText Markup Language",
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"difficulty": "easy",
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"category": "Web Development"
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},
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{
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"id": "easy_4",
|
||||
"question": "Which of these is a web browser?",
|
||||
"incorrect_answers": ["Microsoft Word", "Adobe Photoshop", "Spotify"],
|
||||
"correct_answer": "Google Chrome",
|
||||
"difficulty": "easy",
|
||||
"category": "Technology"
|
||||
},
|
||||
{
|
||||
"id": "easy_5",
|
||||
"question": "What is 2 + 2?",
|
||||
"incorrect_answers": ["3", "5", "6"],
|
||||
"correct_answer": "4",
|
||||
"difficulty": "easy",
|
||||
"category": "Mathematics"
|
||||
},
|
||||
{
|
||||
"id": "easy_6",
|
||||
"question": "Which planet is closest to the Sun?",
|
||||
"incorrect_answers": ["Venus", "Earth", "Mars"],
|
||||
"correct_answer": "Mercury",
|
||||
"difficulty": "easy",
|
||||
"category": "Science"
|
||||
},
|
||||
{
|
||||
"id": "easy_7",
|
||||
"question": "What does CSS stand for?",
|
||||
"incorrect_answers": ["Computer Style Sheets", "Creative Style Sheets", "Colorful Style Sheets"],
|
||||
"correct_answer": "Cascading Style Sheets",
|
||||
"difficulty": "easy",
|
||||
"category": "Web Development"
|
||||
},
|
||||
{
|
||||
"id": "easy_8",
|
||||
"question": "Which company developed the iPhone?",
|
||||
"incorrect_answers": ["Google", "Microsoft", "Samsung"],
|
||||
"correct_answer": "Apple",
|
||||
"difficulty": "easy",
|
||||
"category": "Technology"
|
||||
},
|
||||
{
|
||||
"id": "easy_9",
|
||||
"question": "What is the largest ocean on Earth?",
|
||||
"incorrect_answers": ["Atlantic", "Indian", "Arctic"],
|
||||
"correct_answer": "Pacific",
|
||||
"difficulty": "easy",
|
||||
"category": "Geography"
|
||||
},
|
||||
{
|
||||
"id": "easy_10",
|
||||
"question": "Which data type stores whole numbers in programming?",
|
||||
"incorrect_answers": ["float", "string", "boolean"],
|
||||
"correct_answer": "integer",
|
||||
"difficulty": "easy",
|
||||
"category": "Programming"
|
||||
},
|
||||
|
||||
# ===== MEDIUM QUESTIONS =====
|
||||
{
|
||||
"id": "medium_1",
|
||||
"question": "What does API stand for?",
|
||||
"incorrect_answers": ["Advanced Programming Interface", "Automated Program Integration", "Applied Programming Instructions"],
|
||||
"correct_answer": "Application Programming Interface",
|
||||
"difficulty": "medium",
|
||||
"category": "Programming"
|
||||
},
|
||||
{
|
||||
"id": "medium_2",
|
||||
"question": "In machine learning, what does 'overfitting' mean?",
|
||||
"incorrect_answers": ["Model performs well on all data", "Model is too simple", "Model trains too quickly"],
|
||||
"correct_answer": "Model memorizes training data but fails on new data",
|
||||
"difficulty": "medium",
|
||||
"category": "Machine Learning"
|
||||
},
|
||||
{
|
||||
"id": "medium_3",
|
||||
"question": "Which HTTP status code indicates 'Not Found'?",
|
||||
"incorrect_answers": ["200", "500", "403"],
|
||||
"correct_answer": "404",
|
||||
"difficulty": "medium",
|
||||
"category": "Web Development"
|
||||
},
|
||||
{
|
||||
"id": "medium_4",
|
||||
"question": "What is the primary purpose of a database index?",
|
||||
"incorrect_answers": ["Store data", "Backup data", "Encrypt data"],
|
||||
"correct_answer": "Speed up data retrieval",
|
||||
"difficulty": "medium",
|
||||
"category": "Database"
|
||||
},
|
||||
{
|
||||
"id": "medium_5",
|
||||
"question": "In React, what is a component?",
|
||||
"incorrect_answers": ["A CSS framework", "A database table", "A server endpoint"],
|
||||
"correct_answer": "A reusable piece of UI",
|
||||
"difficulty": "medium",
|
||||
"category": "React"
|
||||
},
|
||||
{
|
||||
"id": "medium_6",
|
||||
"question": "What does CPU stand for?",
|
||||
"incorrect_answers": ["Computer Programming Unit", "Central Program Unit", "Control Program Utility"],
|
||||
"correct_answer": "Central Processing Unit",
|
||||
"difficulty": "medium",
|
||||
"category": "Hardware"
|
||||
},
|
||||
{
|
||||
"id": "medium_7",
|
||||
"question": "Which sorting algorithm has the best average time complexity?",
|
||||
"incorrect_answers": ["Bubble Sort", "Selection Sort", "Insertion Sort"],
|
||||
"correct_answer": "Quick Sort",
|
||||
"difficulty": "medium",
|
||||
"category": "Algorithms"
|
||||
},
|
||||
{
|
||||
"id": "medium_8",
|
||||
"question": "What is the difference between '==' and '===' in JavaScript?",
|
||||
"incorrect_answers": ["No difference", "=== is for strings only", "== is deprecated"],
|
||||
"correct_answer": "=== checks type and value, == only checks value",
|
||||
"difficulty": "medium",
|
||||
"category": "JavaScript"
|
||||
},
|
||||
{
|
||||
"id": "medium_9",
|
||||
"question": "In SQL, what does JOIN do?",
|
||||
"incorrect_answers": ["Creates a new table", "Deletes records", "Updates data"],
|
||||
"correct_answer": "Combines rows from multiple tables",
|
||||
"difficulty": "medium",
|
||||
"category": "Database"
|
||||
},
|
||||
{
|
||||
"id": "medium_10",
|
||||
"question": "What is the purpose of version control systems like Git?",
|
||||
"incorrect_answers": ["Code compilation", "Database management", "User interface design"],
|
||||
"correct_answer": "Track changes in source code",
|
||||
"difficulty": "medium",
|
||||
"category": "Development Tools"
|
||||
},
|
||||
|
||||
# ===== HARD QUESTIONS =====
|
||||
{
|
||||
"id": "hard_1",
|
||||
"question": "What is the time complexity of binary search?",
|
||||
"incorrect_answers": ["O(n)", "O(n²)", "O(n log n)"],
|
||||
"correct_answer": "O(log n)",
|
||||
"difficulty": "hard",
|
||||
"category": "Algorithms"
|
||||
},
|
||||
{
|
||||
"id": "hard_2",
|
||||
"question": "Which design pattern ensures a class has only one instance?",
|
||||
"incorrect_answers": ["Factory", "Observer", "Strategy"],
|
||||
"correct_answer": "Singleton",
|
||||
"difficulty": "hard",
|
||||
"category": "Design Patterns"
|
||||
},
|
||||
{
|
||||
"id": "hard_3",
|
||||
"question": "In distributed systems, what is the CAP theorem?",
|
||||
"incorrect_answers": ["Consistency, Availability, Performance", "Concurrency, Atomicity, Persistence", "Caching, Authentication, Privacy"],
|
||||
"correct_answer": "Consistency, Availability, Partition tolerance",
|
||||
"difficulty": "hard",
|
||||
"category": "Distributed Systems"
|
||||
},
|
||||
{
|
||||
"id": "hard_4",
|
||||
"question": "What is the space complexity of merge sort?",
|
||||
"incorrect_answers": ["O(1)", "O(log n)", "O(n²)"],
|
||||
"correct_answer": "O(n)",
|
||||
"difficulty": "hard",
|
||||
"category": "Algorithms"
|
||||
},
|
||||
{
|
||||
"id": "hard_5",
|
||||
"question": "In functional programming, what is a closure?",
|
||||
"incorrect_answers": ["A loop structure", "A data type", "A compilation step"],
|
||||
"correct_answer": "A function that captures variables from its scope",
|
||||
"difficulty": "hard",
|
||||
"category": "Programming Concepts"
|
||||
},
|
||||
{
|
||||
"id": "hard_6",
|
||||
"question": "What is the purpose of hash table collision resolution?",
|
||||
"incorrect_answers": ["Increase memory usage", "Slow down operations", "Reduce security"],
|
||||
"correct_answer": "Handle multiple keys mapping to the same slot",
|
||||
"difficulty": "hard",
|
||||
"category": "Data Structures"
|
||||
},
|
||||
{
|
||||
"id": "hard_7",
|
||||
"question": "In microservices architecture, what is service discovery?",
|
||||
"incorrect_answers": ["Database replication", "Load balancing", "Code deployment"],
|
||||
"correct_answer": "Mechanism for services to find and communicate with each other",
|
||||
"difficulty": "hard",
|
||||
"category": "Architecture"
|
||||
},
|
||||
{
|
||||
"id": "hard_8",
|
||||
"question": "What is the difference between TCP and UDP?",
|
||||
"incorrect_answers": ["UDP is faster but unreliable", "TCP is for web only", "No significant difference"],
|
||||
"correct_answer": "TCP is reliable and connection-oriented, UDP is fast but unreliable",
|
||||
"difficulty": "hard",
|
||||
"category": "Networking"
|
||||
},
|
||||
{
|
||||
"id": "hard_9",
|
||||
"question": "In machine learning, what is the curse of dimensionality?",
|
||||
"incorrect_answers": ["Too much training data", "Overly complex models", "Hardware limitations"],
|
||||
"correct_answer": "Performance degradation as feature dimensions increase",
|
||||
"difficulty": "hard",
|
||||
"category": "Machine Learning"
|
||||
},
|
||||
{
|
||||
"id": "hard_10",
|
||||
"question": "What is eventual consistency in distributed databases?",
|
||||
"incorrect_answers": ["Data is always consistent", "Consistency is never achieved", "Only one node has data"],
|
||||
"correct_answer": "System will become consistent over time without continuous input",
|
||||
"difficulty": "hard",
|
||||
"category": "Distributed Systems"
|
||||
}
|
||||
]
|
||||
|
||||
def _distribute_fallback_questions(self):
|
||||
"""Distribute fallback questions into difficulty levels"""
|
||||
fallback_data = self._get_enhanced_fallback_questions()
|
||||
self.quiz_data = fallback_data
|
||||
|
||||
self.questions_by_difficulty = {
|
||||
'easy': [q for q in fallback_data if q.get('difficulty') == 'easy'],
|
||||
'medium': [q for q in fallback_data if q.get('difficulty') == 'medium'],
|
||||
'hard': [q for q in fallback_data if q.get('difficulty') == 'hard']
|
||||
}
|
||||
|
||||
def generate_quiz(self, topic=None, difficulty=None, num_questions=5):
|
||||
"""
|
||||
Generate a quiz compatible with room-based quiz system - FIXED VERSION
|
||||
"""
|
||||
print(f"🤖 Generating quiz: topic={topic}, difficulty={difficulty}, num_questions={num_questions}")
|
||||
|
||||
# Filter questions based on topic and difficulty
|
||||
filtered = self.quiz_data.copy()
|
||||
|
||||
if topic and topic.lower() != 'general':
|
||||
filtered = [q for q in filtered if
|
||||
topic.lower() in q.get('question', '').lower() or
|
||||
topic.lower() in q.get('category', '').lower()]
|
||||
print(f"📝 Filtered by topic '{topic}': {len(filtered)} questions")
|
||||
|
||||
if difficulty:
|
||||
filtered = [q for q in filtered if q.get('difficulty', 'medium') == difficulty]
|
||||
print(f"📝 Filtered by difficulty '{difficulty}': {len(filtered)} questions")
|
||||
|
||||
# Ensure we have questions to select from
|
||||
if not filtered:
|
||||
print("⚠️ No questions match criteria, using all available questions")
|
||||
filtered = self.quiz_data[:10] # Use first 10 as fallback
|
||||
|
||||
# Select random questions
|
||||
selected = random.sample(filtered, min(num_questions, len(filtered)))
|
||||
print(f"📝 Selected {len(selected)} questions from {len(filtered)} filtered questions")
|
||||
|
||||
questions = []
|
||||
for i, q_data in enumerate(selected):
|
||||
choices = q_data['incorrect_answers'] + [q_data['correct_answer']]
|
||||
random.shuffle(choices)
|
||||
correct_idx = choices.index(q_data['correct_answer'])
|
||||
|
||||
questions.append({
|
||||
"id": str(uuid.uuid4()),
|
||||
"question_number": i + 1,
|
||||
"question_text": q_data['question'],
|
||||
"options": choices,
|
||||
"correct_answer": chr(65 + correct_idx), # A, B, C, D
|
||||
"points": 10 if q_data.get('difficulty') == 'easy' else 15 if q_data.get('difficulty') == 'medium' else 20,
|
||||
"explanation": f"The correct answer is {q_data['correct_answer']}.",
|
||||
"difficulty": q_data.get('difficulty', 'medium'),
|
||||
"category": q_data.get('category', 'General')
|
||||
})
|
||||
|
||||
quiz_result = {
|
||||
"id": str(uuid.uuid4()),
|
||||
"title": f"AI Generated Quiz{(' - ' + topic) if topic and topic.lower() != 'general' else ''}",
|
||||
"description": f"Quiz generated by AI. Topic: {topic or 'General'}, Difficulty: {difficulty or 'Mixed'}",
|
||||
"difficulty": difficulty or "mixed",
|
||||
"questions": questions,
|
||||
"created_at": datetime.now().isoformat(),
|
||||
"generated_by": "AI",
|
||||
"total_points": sum(q['points'] for q in questions)
|
||||
}
|
||||
|
||||
print(f"✅ Quiz generated successfully: {len(questions)} questions, {quiz_result['total_points']} total points")
|
||||
return quiz_result
|
||||
|
||||
def create_session(self, user_id):
|
||||
"""Create new adaptive quiz session"""
|
||||
session_id = str(ObjectId())
|
||||
session_data = {
|
||||
'session_id': session_id,
|
||||
'user_id': user_id,
|
||||
'current_difficulty': 'easy', # Always start with easy
|
||||
'consecutive_correct': {'easy': 0, 'medium': 0, 'hard': 0},
|
||||
'total_questions': 0,
|
||||
'total_correct': 0,
|
||||
'question_history': [],
|
||||
'created_at': datetime.utcnow(),
|
||||
'status': 'active'
|
||||
}
|
||||
return session_data
|
||||
|
||||
def get_adaptive_question(self, session_data):
|
||||
"""Get next question based on current difficulty level"""
|
||||
current_difficulty = session_data['current_difficulty']
|
||||
available_questions = self.questions_by_difficulty[current_difficulty].copy()
|
||||
|
||||
# Avoid repeating questions
|
||||
asked_questions = [q['question_id'] for q in session_data.get('question_history', [])]
|
||||
available_questions = [q for q in available_questions
|
||||
if q.get('id', str(hash(q['question']))) not in asked_questions]
|
||||
|
||||
if not available_questions:
|
||||
# Fallback to any difficulty if current level exhausted
|
||||
all_available = [q for q in self.quiz_data
|
||||
if q.get('id', str(hash(q['question']))) not in asked_questions]
|
||||
available_questions = all_available[:10] if all_available else self.quiz_data[:5]
|
||||
|
||||
# Select random question
|
||||
question_data = random.choice(available_questions)
|
||||
|
||||
# Create formatted question with shuffled choices
|
||||
choices = question_data['incorrect_answers'] + [question_data['correct_answer']]
|
||||
random.shuffle(choices)
|
||||
|
||||
# Find correct answer position
|
||||
correct_position = choices.index(question_data['correct_answer'])
|
||||
correct_letter = chr(65 + correct_position)
|
||||
|
||||
question_obj = {
|
||||
'question_id': question_data.get('id', str(hash(question_data['question']))),
|
||||
'question_text': question_data['question'],
|
||||
'choices': {
|
||||
'A': choices[0],
|
||||
'B': choices[1],
|
||||
'C': choices[2],
|
||||
'D': choices[3]
|
||||
},
|
||||
'correct_answer': correct_letter,
|
||||
'correct_answer_text': question_data['correct_answer'],
|
||||
'difficulty': current_difficulty,
|
||||
'category': question_data.get('category', 'General'),
|
||||
'explanation': f"The correct answer is {question_data['correct_answer']}."
|
||||
}
|
||||
|
||||
return question_obj
|
||||
|
||||
def get_llm_prediction(self, question_text, choices):
|
||||
"""Use trained model to predict answer (with intelligent fallback)"""
|
||||
if not self.model_available or not self.model:
|
||||
# Intelligent fallback with pattern matching
|
||||
question_lower = question_text.lower()
|
||||
choice_keys = list(choices.keys()) if isinstance(choices, dict) else ['A', 'B', 'C', 'D']
|
||||
choice_texts = [choices[key].lower() if isinstance(choices, dict) else choices[i].lower()
|
||||
for i, key in enumerate(choice_keys)]
|
||||
|
||||
# Enhanced pattern matching
|
||||
if 'capital' in question_lower and 'france' in question_lower:
|
||||
for i, choice in enumerate(choice_texts):
|
||||
if 'paris' in choice:
|
||||
return {
|
||||
'llm_prediction': choice_keys[i],
|
||||
'confidence': 0.9,
|
||||
'model_accuracy': 90.0,
|
||||
'fallback_mode': True,
|
||||
'reason': 'Pattern matching - France capital'
|
||||
}
|
||||
|
||||
if 'html' in question_lower and 'stand' in question_lower:
|
||||
for i, choice in enumerate(choice_texts):
|
||||
if 'hypertext markup' in choice:
|
||||
return {
|
||||
'llm_prediction': choice_keys[i],
|
||||
'confidence': 0.85,
|
||||
'model_accuracy': 85.0,
|
||||
'fallback_mode': True,
|
||||
'reason': 'Pattern matching - HTML definition'
|
||||
}
|
||||
|
||||
# Default random fallback
|
||||
fallback_prediction = random.choice(choice_keys)
|
||||
return {
|
||||
'llm_prediction': fallback_prediction,
|
||||
'confidence': 0.25,
|
||||
'model_accuracy': 25.0,
|
||||
'fallback_mode': True,
|
||||
'reason': 'Random selection'
|
||||
}
|
||||
|
||||
try:
|
||||
# Format question for model prediction
|
||||
formatted_question = f"Question: {question_text}\n"
|
||||
if isinstance(choices, dict):
|
||||
for key, choice in choices.items():
|
||||
formatted_question += f"{key}) {choice}\n"
|
||||
else:
|
||||
for i, choice in enumerate(choices):
|
||||
formatted_question += f"{chr(65+i)}) {choice}\n"
|
||||
|
||||
# Tokenize and predict using trained model
|
||||
sequence = self.tokenizer.texts_to_sequences([formatted_question])
|
||||
padded = pad_sequences(sequence, maxlen=400, padding='post')
|
||||
|
||||
prediction = self.model.predict(padded, verbose=0)
|
||||
predicted_class = np.argmax(prediction[0])
|
||||
predicted_letter = self.label_encoder.inverse_transform([predicted_class])[0]
|
||||
confidence = float(prediction[0][predicted_class])
|
||||
|
||||
return {
|
||||
'llm_prediction': predicted_letter,
|
||||
'confidence': confidence,
|
||||
'model_accuracy': 33.1, # Your model's test accuracy
|
||||
'fallback_mode': False,
|
||||
'reason': 'CNN model prediction'
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
print(f"⚠️ Model prediction error: {e}")
|
||||
# Fallback on error
|
||||
fallback_prediction = random.choice(['A', 'B', 'C', 'D'])
|
||||
return {
|
||||
'llm_prediction': fallback_prediction,
|
||||
'confidence': 0.25,
|
||||
'model_accuracy': 25.0,
|
||||
'fallback_mode': True,
|
||||
'error': str(e),
|
||||
'reason': 'Error fallback'
|
||||
}
|
||||
|
||||
def evaluate_answer(self, session_data, question_data, user_answer):
|
||||
"""Evaluate user answer and adjust difficulty"""
|
||||
is_correct = (user_answer.upper() == question_data['correct_answer'])
|
||||
current_difficulty = session_data['current_difficulty']
|
||||
|
||||
# Update session stats
|
||||
session_data['total_questions'] += 1
|
||||
if is_correct:
|
||||
session_data['total_correct'] += 1
|
||||
session_data['consecutive_correct'][current_difficulty] += 1
|
||||
else:
|
||||
# Reset consecutive count for current difficulty
|
||||
session_data['consecutive_correct'][current_difficulty] = 0
|
||||
|
||||
# Apply difficulty adjustment rules
|
||||
new_difficulty = self._adjust_difficulty(session_data, is_correct)
|
||||
|
||||
# Record question in history
|
||||
question_record = {
|
||||
'question_id': question_data['question_id'],
|
||||
'question_text': question_data['question_text'],
|
||||
'user_answer': user_answer,
|
||||
'correct_answer': question_data['correct_answer'],
|
||||
'correct_answer_text': question_data['correct_answer_text'],
|
||||
'is_correct': is_correct,
|
||||
'difficulty': current_difficulty,
|
||||
'category': question_data.get('category', 'General'),
|
||||
'timestamp': datetime.utcnow()
|
||||
}
|
||||
session_data['question_history'].append(question_record)
|
||||
|
||||
# Get LLM prediction for comparison
|
||||
llm_result = self.get_llm_prediction(question_data['question_text'], question_data['choices'])
|
||||
|
||||
result = {
|
||||
'is_correct': is_correct,
|
||||
'correct_answer': question_data['correct_answer'],
|
||||
'correct_answer_text': question_data['correct_answer_text'],
|
||||
'explanation': question_data['explanation'],
|
||||
'difficulty_changed': new_difficulty != current_difficulty,
|
||||
'previous_difficulty': current_difficulty,
|
||||
'new_difficulty': new_difficulty,
|
||||
'consecutive_correct': session_data['consecutive_correct'][current_difficulty],
|
||||
'llm_prediction': llm_result,
|
||||
'llm_agrees': llm_result['llm_prediction'] == question_data['correct_answer'],
|
||||
'session_stats': {
|
||||
'total_questions': session_data['total_questions'],
|
||||
'total_correct': session_data['total_correct'],
|
||||
'accuracy': round((session_data['total_correct'] / session_data['total_questions']) * 100, 1)
|
||||
}
|
||||
}
|
||||
|
||||
session_data['current_difficulty'] = new_difficulty
|
||||
return result
|
||||
|
||||
def _adjust_difficulty(self, session_data, is_correct):
|
||||
"""Difficulty adjustment rules: 3 correct up, 1 wrong down"""
|
||||
current_difficulty = session_data['current_difficulty']
|
||||
consecutive = session_data['consecutive_correct']
|
||||
|
||||
if is_correct:
|
||||
# Move up after 3 consecutive correct answers
|
||||
if consecutive[current_difficulty] >= 3:
|
||||
if current_difficulty == 'easy':
|
||||
session_data['consecutive_correct']['easy'] = 0
|
||||
return 'medium'
|
||||
elif current_difficulty == 'medium':
|
||||
session_data['consecutive_correct']['medium'] = 0
|
||||
return 'hard'
|
||||
else:
|
||||
# Move down immediately after 1 wrong answer
|
||||
if current_difficulty == 'hard':
|
||||
return 'medium'
|
||||
elif current_difficulty == 'medium':
|
||||
return 'easy'
|
||||
|
||||
return current_difficulty
|
||||
|
||||
def get_session_stats(self, session_data):
|
||||
"""Get comprehensive session statistics"""
|
||||
total_questions = session_data['total_questions']
|
||||
total_correct = session_data['total_correct']
|
||||
accuracy = (total_correct / total_questions * 100) if total_questions > 0 else 0
|
||||
|
||||
difficulty_stats = {}
|
||||
for difficulty in ['easy', 'medium', 'hard']:
|
||||
questions_at_level = [q for q in session_data['question_history'] if q['difficulty'] == difficulty]
|
||||
correct_at_level = sum(1 for q in questions_at_level if q['is_correct'])
|
||||
difficulty_stats[difficulty] = {
|
||||
'questions': len(questions_at_level),
|
||||
'correct': correct_at_level,
|
||||
'accuracy': round((correct_at_level / len(questions_at_level) * 100), 1) if questions_at_level else 0
|
||||
}
|
||||
|
||||
return {
|
||||
'session_id': session_data['session_id'],
|
||||
'current_difficulty': session_data['current_difficulty'],
|
||||
'total_questions': total_questions,
|
||||
'total_correct': total_correct,
|
||||
'overall_accuracy': round(accuracy, 1),
|
||||
'consecutive_correct': session_data['consecutive_correct'],
|
||||
'difficulty_breakdown': difficulty_stats,
|
||||
'status': session_data['status'],
|
||||
'model_available': self.model_available
|
||||
}
|
||||
|
||||
# Export the class for backward compatibility
|
||||
AIQuizService = AdaptiveQuizMasterLLM
|
||||
Reference in New Issue
Block a user