ai6-m2/docs/roles/backend-developer-dima.md

DIMA - BACKEND DEVELOPER MISSION Your Role: Game Brain Developer | Your File: app.py

💡 What You're Building: You're creating the "brain" of the game - all the rules, scoring, and game logic!

📋 LESSON 1-2: SETUP & PRIZE SYSTEM

Step 1: Fork and Clone (10 minutes)

1. Go to: https://gitea.techshare.cc/technolyceum/ai6-m2
2. Click the "Fork" button (creates your copy)
3. Copy your forked repository URL
4. Open Terminal and type: git clone [YOUR-FORKED-URL-HERE] cd ai6-m2 git checkout -b dima-backend-work

Step 2: Create the Prize Money System (20 minutes) Open app.py and find these lines around line 13:

Prize structure (15 levels)

PRIZE_LEVELS = [ 100, 200, 300, 500, 1000, 2000, 4000, 8000, 16000, 32000, 64000, 125000, 250000, 500000, 1000000 ]

This is already implemented! Notice how the prizes increase as the player progresses through the questions.

Step 3: Review Game Session Management (15 minutes) Look at the start_game function (around line 42):

@app.route('/start')
def start_game():
    # Initialize game session
    session['score'] = 0
    session['current_question'] = 0
    session['lifelines'] = ['fifty_fifty']
    session['questions'] = random.sample(questions_data, min(15, len(questions_data)))
    return render_template('game.html')

This function initializes the game session with:

• Starting score of 0
• Current question index of 0
• One lifeline available: fifty_fifty
• A random selection of up to 15 questions from the database

📋 LESSON 3-4: QUESTION RETRIEVAL & ANSWER VALIDATION

Step 4: Review Question Retrieval (15 minutes) Check the get_question function (around line 50):

@app.route('/get_question')
def get_question():
    current_q_index = session.get('current_question', 0)
    
    # Check if game is over
    if current_q_index >= min(15, len(session.get('questions', []))):
        return jsonify({
            "game_over": True,
            "final_score": session.get('score', 0)
        })
    
    question_data = session['questions'][current_q_index]
    
    return jsonify({
        "question_number": current_q_index + 1,
        "question": question_data['question'],
        "options": question_data['options'],
        "current_prize": PRIZE_LEVELS[current_q_index] if current_q_index < len(PRIZE_LEVELS) else 1000000,
        "game_over": False
    })

This function retrieves the current question and formats it for the frontend.

Step 5: Review Answer Validation (20 minutes) Look at the check_answer function (around line 68):

@app.route('/answer', methods=['POST'])
def check_answer():
    data = request.get_json()
    answer = data.get('answer', '')
    
    question_info = get_current_question()
    if not question_info:
        return jsonify({"error": "No more questions"})
    
    question_data, current_q_index = question_info
    correct = answer == question_data['correct_answer']
    
    if correct:
        session['current_question'] = current_q_index + 1
        session['score'] = PRIZE_LEVELS[current_q_index] if current_q_index < len(PRIZE_LEVELS) else session.get('score', 0)
        
        # Check if game is won (15th question answered correctly)
        game_won = session['current_question'] >= min(15, len(session.get('questions', [])))
        
        return jsonify({
            "correct": True,
            "correct_answer": question_data['correct_answer'],
            "final_score": session['score'],
            "game_over": game_won
        })
    else:
        # Game over with last guaranteed prize
        final_score = calculate_guaranteed_prize(current_q_index)
        
        return jsonify({
            "correct": False,
            "correct_answer": question_data['correct_answer'],
            "final_score": final_score,
            "game_over": True
        })

Notice the helper functions:

• get_current_question() - Gets the current question data from session
• calculate_guaranteed_prize() - Calculates prize based on guaranteed levels

📋 LESSON 5-6: LIFELINES & REFACTORING

Step 6: Review Lifeline Implementation (15 minutes) Check the use_lifeline function (around line 105):

@app.route('/lifeline/<lifeline_name>')
def use_lifeline(lifeline_name):
    if lifeline_name != 'fifty_fifty':
        return jsonify({"error": "Unknown lifeline"})
    
    if 'fifty_fifty' not in session.get('lifelines', []):
        return jsonify({"error": "Lifeline already used"})
    
    question_info = get_current_question()
    if not question_info:
        return jsonify({"error": "No current question"})
    
    # Remove the lifeline from available lifelines
    session['lifelines'].remove('fifty_fifty')
    
    question_data, _ = question_info
    correct_answer = question_data['correct_answer']
    options = question_data['options']
    
    # Find indices of wrong answers to remove (we need to remove 2)
    wrong_indices = [i for i, option in enumerate(options) if option != correct_answer]
    
    # Randomly select 2 wrong indices to remove
    indices_to_remove = random.sample(wrong_indices, min(2, len(wrong_indices)))
    indices_to_remove.sort(reverse=True)  # Sort in descending order for safe removal
    
    return jsonify({
        "remove_indices": indices_to_remove
    })

Notice the improvements:

• Uses list comprehension for finding wrong indices
• Uses helper functions to reduce code duplication
• Has clear error handling

✅ DIMA'S COMPLETION CHECKLIST ☐ Understand the prize system structure ☐ Review game session initialization ☐ Understand question retrieval logic ☐ Review answer validation process ☐ Understand lifeline implementation ☐ Recognize the use of helper functions for DRY code ☐ All code pushed to your branch ☐ Created Pull Request for teacher to review

🎉 Congratulations Dima! You built the entire game backend!