TalentScout - Enhanced Modular AI Hiring Assistant

🆕 New Features in Version 2.0

Advanced Question System

• Structured Assessment: 2 MCQs → 1 Pseudo-code → 1 Complete Code
• Time Management: Individual time limits for each question type
• Question Types:
  • 📝 Multiple Choice (2 minutes each) - Medium difficulty with instant feedback
  • 🧠 Pseudo-code Problems (5 minutes) - Algorithm design and logic
  • 💻 Complete Code Challenges (10 minutes) - Real-world implementation

Enhanced User Experience

• Real-time Timer: Visual countdown for each question
• Progress Tracking: Assessment progress bar and completion status
• Smart Input: Button-based MCQ selection + text input options
• Instant Feedback: Immediate scoring for multiple choice questions
• Auto-submission: Time-expired question handling

Comprehensive Analytics

• Performance Metrics: MCQ accuracy, average time per question
• Detailed Reporting: Question-by-question breakdown with timing data
• Smart Recommendations: AI-powered candidate assessment and suggestions
• Export Options: Detailed JSON reports with performance analysis

🚀 Enhanced Setup Instructions

1. Create Project Structure:

mkdir talentscout-v2
cd talentscout-v2
mkdir config models services handlers ui utils
touch config/__init__.py models/__init__.py services/__init__.py
touch handlers/__init__.py ui/__init__.py utils/__init__.py

2. Install Dependencies:

pip install streamlit>=1.28.0 openai>=0.28.0 python-dotenv>=1.0.0

3. Environment Setup:

# Create .env file
echo "OPENAI_API_KEY=your_api_key_here" > .env

4. File Deployment:

   • Copy each module to its respective directory
   • Ensure all __init__.py files are present
   • Place main.py in the root directory

5. Run Application:

streamlit run main.py

🔧 Key Technical Improvements

Modular Architecture Benefits

• Single Responsibility: Each module handles one specific aspect
• Easy Testing: Individual components can be unit tested
• Scalable: Add new question types or LLM providers easily
• Maintainable: Clear separation between UI, business logic, and data

Advanced Question Generation

• AI-Powered: LLM generates personalized questions based on tech stack
• Fallback System: Pre-built questions when AI is unavailable
• Type-Specific: Different prompts for MCQ, pseudo-code, and coding questions
• Difficulty Scaling: Questions adapt to candidate experience level

Enhanced State Management

• Timer Integration: Real-time question timing and auto-submission
• Progress Tracking: Complete assessment flow monitoring
• Data Persistence: Comprehensive response storage and analytics
• Session Recovery: Robust state handling with error recovery

Professional Reporting

• Multi-format Data: JSON export with detailed metrics
• Performance Analysis: Automated strengths/weaknesses identification
• Recommendation Engine: AI-powered hiring recommendations
• Visual Feedback: Charts and progress indicators

📊 Question Structure

Question Type	Count	Time Limit	Purpose
Multiple Choice	2	2 min each	Test theoretical knowledge
Pseudo-code	1	5 minutes	Assess algorithmic thinking
Complete Code	1	10 minutes	Evaluate implementation skills

🎯 Assessment Flow

1. Personal Info Collection (2-3 minutes)
2. Technical Skills Identification (1-2 minutes)
3. Structured Technical Assessment (15-17 minutes)
4. AI-Generated Summary & Next Steps (1 minute)

Total Time: ~20 minutes for comprehensive technical screening

🔄 Extensibility

Adding New Question Types

# In config/settings.py
QUESTION_STRUCTURE.append({
    "type": QuestionType.NEW_TYPE, 
    "count": 1, 
    "time_limit": 480
})

Custom LLM Providers

# In services/llm_service.py
def setup_custom_llm(self):
    # Add your custom LLM integration
    pass

Enhanced Analytics

# In models/candidate.py
def get_custom_metrics(self):
    # Add your custom performance metrics
    pass

This enhanced modular structure provides a professional, scalable foundation for technical hiring assessments with sophisticated question management, real-time interaction, and comprehensive candidate evaluation.