MCP Resume Server Server

Fetches resume data from a GitHub gist to provide professional background context to LLMs.

GitHub

Documentation

🤖 AI Resume - Interactive Chat Interface

Chat with Michael Wybraniec's AI-powered resume using open-source LLMs - Deploy for FREE!

✨ Built with a clean, modular architecture for maximum maintainability and scalability.

🚀 Features

🎯 Core Functionality

📄 Interactive Resume Chat - Ask any question about Michael's background and experience
🎯 Smart Context Retrieval - Intelligent context selection based on user questions
📄 PDF Download - Generate and download professional CV on demand
🎯 Smart Matching - Job description analysis for recruiter insights

🤖 AI & LLM Integration

🤖 Multiple LLM Providers - OpenRouter (free models), OpenAI, Ollama (local)
⚡ Real-time Responses - Instant responses with intelligent context retrieval
🆓 Free Models Available - Use powerful open-source models at no cost
🔧 Flexible Configuration - Easy switching between different AI providers

🎨 User Experience

💬 Modern Chat UI - Beautiful Streamlit interface with quick action buttons
📱 Mobile Responsive - Works perfectly on all devices and screen sizes
✅ System Status - Real-time status indicator shows when all systems are ready
🔧 Auto-Configuration - Smart setup flow with contextual help
⚡ Quick Actions - One-click access to common tasks and insights

🏗️ Technical Excellence

🏗️ Purpose-Based Architecture - Professional project organization with logical file grouping
🆓 Free Deployment - Deploy on Streamlit Cloud for free
📦 Modular Design - Clean separation of concerns for maintainability
🔧 Easy Customization - Simple to adapt for your own resume and branding

⚖️ AI Act Compliance

✅ Full EU AI Act Compliance - Complete implementation of all high-risk AI system requirements
🔍 Advanced Monitoring - Real-time compliance monitoring with automated alerting
📋 Audit Procedures - Comprehensive audit framework with standardized checklists
📊 Performance Analytics - Advanced analytics with trend analysis and insights
🔍 Conformity Assessment - Systematic assessment procedures for certification readiness
📋 Certification Preparation - Complete document management for regulatory submission
✅ Compliance Validation - Automated and manual validation with certification eligibility

🎬 Live Demo

🚀 Try it now! Deploy your own instance on Streamlit Cloud or run locally.

📱 Screenshots

Coming soon: Interactive screenshots and demo GIFs

🎯 What You Can Do

💬 Chat with the AI - Ask questions about experience, skills, and projects
📄 Download CV - Generate professional PDF resumes
🎯 Smart Matching - Analyze job descriptions for fit
⚡ Quick Actions - Get instant insights with one-click buttons

🎯 Quick Start (Local)

Prerequisites

Python 3.11 or higher
Git

Installation Steps

# 1. Clone the repository
git clone https://github.com/michaelwybraniec/mcp-resume.git
cd mcp-resume

# 2. Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Run the application
streamlit run app.py

🚀 First Run

Open your browser to http://localhost:8501
Get a free API key from OpenRouter.ai
Add your API key in the sidebar
Look for "✅ All Systems Ready!" status
Start chatting! 🎉

💡 Pro Tip: Use the free model meta-llama/llama-3.1-8b-instruct:free for testing!

🌟 Deploy to Streamlit Cloud (FREE!)

1. Push to GitHub

git add .
git commit -m "Deploy AI Resume to Streamlit Cloud"
git push origin main

2. Deploy on Streamlit Cloud

Go to share.streamlit.io
Connect your GitHub account
Select your repository
Set main file path: app.py
Click "Deploy"!

3. Configure Secrets (Optional)

For enhanced LLM providers, add secrets in Streamlit Cloud dashboard:

[secrets]
OPENROUTER_API_KEY = "your_openrouter_api_key_here"
OPENAI_API_KEY = "your_openai_api_key_here"

4. Your app is live! 🎉

You'll get a URL like: https://your-username-mcp-resume-app-xyz.streamlit.app

🤖 LLM Provider Setup

Option 1: OpenRouter (Recommended)

Free models available!
Sign up at OpenRouter
Get API key and add via the sidebar or Streamlit secrets
Use free models like meta-llama/llama-3.1-8b-instruct:free

Option 2: Ollama (Local Development)

# Install Ollama
brew install ollama  # macOS
# or visit https://ollama.ai for other platforms

# Start Ollama
ollama serve

# Install models
ollama pull llama3.2
ollama pull llama3.1

Option 3: OpenAI (Paid)

Get API key from OpenAI
Add to Streamlit secrets or enter in the app

🏗️ Architecture

Purpose-Based Organization:

mcp-resume/
├── 🏠 app.py                     # Main entry point (Streamlit-ready)
├── 📋 requirements.txt           # Dependencies
│
├── 🔧 core/                      # Foundation & Configuration
│   ├── config.py                # Settings & constants
│   └── models.py                # Data models & types
│
├── ⚙️ services/                  # Business Logic & Integrations
│   ├── resume_service.py        # Resume data handling
│   ├── llm_providers.py         # AI/LLM integrations
│   ├── document_generator.py    # PDF generation
│   └── fallback_resume.py       # Data fallback service
│
├── 🎨 ui/                        # User Interface Layer
│   ├── ui_components.py         # UI components & styling
│   └── session_manager.py       # Session state management
│
├── 📊 data/                      # Data Files
│   ├── resume.json
│   ├── michael_wybraniec_resume.json
│   └── CV_Michael_Wybraniec_15_Jun_2025.pdf
│
└── 🔨 utils/                     # Utility Scripts
    └── create_gist.py           # GitHub Gist utilities

System Architecture Diagram:

┌─────────────────────────────────────────────────────────────────┐
│                        🌐 Streamlit App                        │
├─────────────────────────────────────────────────────────────────┤
│  🎨 UI Layer (ui/)                                              │
│  ├── ui_components.py    # Chat interface, modals, styling     │
│  └── session_manager.py  # State management & initialization   │
├─────────────────────────────────────────────────────────────────┤
│  ⚙️ Services Layer (services/)                                  │
│  ├── resume_service.py      # Data retrieval & context         │
│  ├── llm_providers.py       # AI/LLM integrations              │
│  ├── document_generator.py  # PDF generation                   │
│  └── fallback_resume.py     # Data fallback service            │
├─────────────────────────────────────────────────────────────────┤
│  🔧 Core Layer (core/)                                         │
│  ├── config.py           # Settings & constants                │
│  └── models.py           # Data models & types                 │
├─────────────────────────────────────────────────────────────────┤
│  📊 Data Layer (data/)                                         │
│  ├── resume.json              # Primary resume data            │
│  ├── michael_wybraniec_resume.json  # Backup data             │
│  └── CV_Michael_Wybraniec_15_Jun_2025.pdf  # Professional CV  │
└─────────────────────────────────────────────────────────────────┘
                                │
                                ▼
┌─────────────────────────────────────────────────────────────────┐
│                    🤖 External LLM Providers                   │
├─────────────────────────────────────────────────────────────────┤
│  🌐 OpenRouter API    │  🏠 Ollama Local    │  🔑 OpenAI API    │
│  • Free models        │  • llama3.2         │  • GPT models     │
│  • Paid models        │  • llama3.1         │  • Advanced AI    │
│  • Rate limiting      │  • Local processing │  • High quality   │
└─────────────────────────────────────────────────────────────────┘

Data Flow:

User Input → UI Components → Session Manager → Resume Service
     ↓              ↓              ↓              ↓
LLM Providers ← Message Processing ← Context Generation ← JSON Data
     ↓
AI Response → UI Components → User Interface

⚖️ AI Act Compliance

🎯 Overview

This AI Resume system is fully compliant with the EU AI Act regulations for high-risk AI systems. The system has been designed and implemented with comprehensive compliance measures across all required areas.

📊 Compliance Status

Compliance Area	Status	Implementation
Article 9: Risk Management	✅ COMPLIANT	Comprehensive risk management system with automated assessment
Article 10: Data Governance	✅ COMPLIANT	Data quality management and processing record systems
Article 11: Technical Documentation	✅ COMPLIANT	Complete system architecture and algorithm documentation
Article 12: Record Keeping	✅ COMPLIANT	Comprehensive audit trails and operation logging
Article 13: Transparency	✅ COMPLIANT	AI transparency notices and user information provision
Article 14: Human Oversight	✅ COMPLIANT	Human oversight mechanisms and response flagging
Article 15: Accuracy & Cybersecurity	✅ COMPLIANT	System accuracy monitoring and security measures

🔍 Compliance Features

Phase 1: Immediate Compliance Measures

✅ AI Transparency Notices - Clear notices about AI system usage and limitations
✅ Human Oversight Dashboard - Real-time monitoring of flagged responses
✅ Response Flagging System - Users can flag AI responses for human review
✅ User Rights Information - Comprehensive information about user rights and system capabilities

Phase 2: Core Compliance Implementation

✅ Risk Management System - Automated risk identification, assessment, and mitigation
✅ Data Governance Framework - Data quality assessment and processing record management
✅ Technical Documentation - Complete system architecture and compliance documentation
✅ Record Keeping System - Comprehensive user interaction logging and audit trails

Phase 3: Advanced Compliance

✅ Advanced Monitoring - Real-time compliance monitoring with automated alerting
✅ Audit Procedures - Comprehensive audit framework with standardized checklists
✅ Performance Analytics - Advanced analytics with trend analysis and insights
✅ Compliance Alerting - Intelligent alert system with threshold-based notifications

Phase 4: Conformity Assessment & Certification

✅ Conformity Assessment Framework - Systematic assessment procedures with standardized criteria
✅ Certification Preparation - Complete document management and application procedures
✅ Compliance Validation - Automated and manual validation with certification readiness
✅ Regulatory Documentation - Prepared documentation for regulatory submission

📋 Compliance Dashboard

The system includes a comprehensive compliance dashboard accessible through the sidebar that provides:

Real-time Compliance Status - Live monitoring of all compliance systems
Risk Management Metrics - Risk levels, mitigation status, and trend analysis
Data Governance Status - Data quality scores and processing compliance
Audit Trail Monitoring - System operation logs and audit trail integrity
Performance Analytics - Compliance KPIs and trend analysis
Certification Readiness - Real-time assessment of certification eligibility

🔧 Compliance Architecture

┌─────────────────────────────────────────────────────────────────┐
│                    ⚖️ AI Act Compliance Layer                  │
├─────────────────────────────────────────────────────────────────┤
│  🔍 Advanced Monitoring    │  📋 Audit Procedures              │
│  • Real-time monitoring    │  • Standardized checklists        │
│  • Automated alerting      │  • Comprehensive reporting        │
│  • Performance analytics   │  • Compliance verification        │
├─────────────────────────────────────────────────────────────────┤
│  🔍 Conformity Assessment  │  📋 Certification Preparation     │
│  • Systematic procedures   │  • Document management            │
│  • Standardized criteria   │  • Application workflows          │
│  • Certification readiness │  • Regulatory submission          │
├─────────────────────────────────────────────────────────────────┤
│  ⚠️ Risk Management        │  📊 Data Governance               │
│  • Risk identification     │  • Data quality management        │
│  • Assessment procedures   │  • Processing records             │
│  • Mitigation strategies   │  • Compliance monitoring          │
├─────────────────────────────────────────────────────────────────┤
│  📝 Record Keeping         │  ✅ Compliance Validation         │
│  • System operation logs   │  • Automated validation rules     │
│  • Audit trails           │  • Manual validation procedures    │
│  • Retention management    │  • Certification eligibility      │
└─────────────────────────────────────────────────────────────────┘

📄 Compliance Documentation

AI_ACT_COMPLIANCE.md - Comprehensive compliance documentation
services/risk_management.py - Risk management system implementation
services/data_governance.py - Data governance and quality management
services/record_keeping.py - Record keeping and audit trail system
services/compliance_monitoring.py - Advanced monitoring and alerting
services/audit_procedures.py - Audit procedures and protocols
services/performance_analytics.py - Performance analytics and insights
services/conformity_assessment.py - Conformity assessment framework
services/certification_preparation.py - Certification preparation system
services/compliance_validation.py - Compliance validation procedures

🎯 Compliance Benefits

✅ Regulatory Compliance - Full adherence to EU AI Act requirements
✅ Risk Mitigation - Comprehensive risk management and monitoring
✅ Transparency - Clear AI system information and user rights
✅ Human Oversight - Effective human control and review mechanisms
✅ Audit Readiness - Complete documentation and audit trails
✅ Certification Ready - Prepared for official compliance certification

Module Responsibilities:

🔧 Core Layer

core/config.py - Configuration, constants, and settings
core/models.py - Data models and type definitions

🎨 UI Layer

ui/ui_components.py - UI components, styling, and modals
ui/session_manager.py - Session state management and initialization

⚙️ Services Layer

services/resume_service.py - Resume data retrieval and context generation
services/llm_providers.py - LLM provider implementations and unified chat interface
services/document_generator.py - PDF generation and export functionality
services/fallback_resume.py - Resume data service and context management

📊 Data Layer

data/ - Resume JSON files and PDF assets
utils/ - Utility scripts and helper functions

💡 Usage Examples

💬 Chat Examples:

"Tell me about Michael's work experience"
"What are his technical skills?"
"Summarize his background for a recruiter"
"Find projects involving JavaScript"
"What's his experience with AI and machine learning?"
"How many years of Python experience does he have?"
"Has he worked with AI/ML technologies?"
"What industries has he worked in?"

⚡ Quick Actions:

👤 Summarize Profile - Get comprehensive candidate overview
📅 Years Experience - View career progression timeline
🛠️ Technical Skills - Analyze technical competencies
🎯 Smart Match - Job fit analysis with match scores
📄 Download CV - Professional PDF resume

🎯 For Recruiters & HR:

"Is this candidate suitable for a senior developer role?"
"What's their leadership experience?"
"Do they have experience with cloud platforms?"
"Rate their frontend vs backend skills"
"Analyze fit for this job description..." (paste job description)

🔧 Features & Functionality

📱 Modern Chat Interface

Clean, professional design with expanded sidebar
Quick action buttons for instant insights
Real-time system status indicator
Message timestamps and processing indicators
Mobile-responsive design

🎯 Smart Context Retrieval

Intelligent context selection based on user questions
Focused responses with relevant resume sections
Experience, skills, projects, and achievements matching
Job description analysis capabilities

📄 Professional CV Generation

Generate PDF CV on demand
Clean, professional formatting
Download directly from the interface

🔧 Enhanced User Experience

System Status: Real-time indicator showing "All Systems Ready!" when configured
Help & Tips: Comprehensive setup guide and sample questions
Quick Actions: Organized, expandable panel for common tasks
Auto-Setup: Smart configuration flow with contextual guidance

🌐 Deployment Options

1. Streamlit Cloud (Recommended - Free) ⭐

Free hosting for public repositories
Automatic deployments from GitHub
Built-in secrets management
Perfect for demos and portfolios

2. Local Development

pip install -r requirements.txt
streamlit run app.py

3. Other Cloud Platforms

Railway, Render, Heroku
Use provided requirements.txt and runtime.txt
Set startup command: streamlit run app.py --server.port $PORT

📁 Project Structure

mcp-resume/
├── 🏠 app.py                           # Main entry point (Streamlit-ready)
├── 📋 requirements.txt                 # Python dependencies
├── 🐍 runtime.txt                      # Python version for deployment
├── 📖 README.md                        # Documentation
├── 🚀 DEPLOYMENT.md                    # Deployment guide
├── 🔧 secrets.toml.example             # Example secrets configuration
├── ⚖️ AI_ACT_COMPLIANCE.md             # AI Act compliance documentation
├── 📋 agentic-sdlc/                    # Agentic SDLC project management
│   ├── tasks/                          # Task management and tracking
│   └── project-backlog.md              # Project backlog and progress
│
├── 🔧 core/                            # Foundation & Configuration
│   ├── __init__.py
│   ├── config.py                       # Settings & constants
│   └── models.py                       # Data models & types
│
├── ⚙️ services/                        # Business Logic & Integrations
│   ├── __init__.py
│   ├── resume_service.py               # Resume data handling
│   ├── llm_providers.py                # AI/LLM integrations
│   ├── document_generator.py           # PDF generation
│   ├── fallback_resume.py              # Data fallback service
│   ├── risk_management.py              # AI Act risk management system
│   ├── data_governance.py              # Data governance and quality management
│   ├── record_keeping.py               # Record keeping and audit trails
│   ├── compliance_monitoring.py        # Advanced compliance monitoring
│   ├── audit_procedures.py             # Audit procedures and protocols
│   ├── performance_analytics.py        # Performance analytics and insights
│   ├── conformity_assessment.py        # Conformity assessment framework
│   ├── certification_preparation.py    # Certification preparation system
│   └── compliance_validation.py        # Compliance validation procedures
│
├── 🎨 ui/                              # User Interface Layer
│   ├── __init__.py
│   ├── ui_components.py                # UI components & styling
│   └── session_manager.py              # Session state management
│
├── 📊 data/                            # Data Files
│   ├── resume.json                     # Primary resume data
│   ├── michael_wybraniec_resume.json   # Backup resume data
│   ├── CV_Michael_Wybraniec_15_Jun_2025.pdf # Professional CV
│   ├── risk_management_log.json        # Risk management data
│   ├── data_governance_log.json        # Data governance records
│   ├── system_records.json             # System operation logs
│   ├── audit_trails.json               # Audit trail data
│   ├── compliance_monitoring.json      # Compliance monitoring data
│   ├── audit_procedures.json           # Audit procedures data
│   ├── performance_analytics.json      # Performance analytics data
│   ├── conformity_assessment.json      # Conformity assessment data
│   ├── certification_preparation.json  # Certification preparation data
│   └── compliance_validation.json      # Compliance validation data
│
├── 🔨 utils/                           # Utility Scripts
│   ├── __init__.py
│   └── create_gist.py                  # GitHub Gist utilities
│
└── 🐍 venv/                            # Virtual environment

🏗️ Purpose-Based Architecture Benefits

🔧 For Developers:

Easy Navigation: Find files by purpose (UI, services, data, etc.)
Maintainable: Clear separation of concerns across directories
Scalable: Add new features without touching existing modules
Testable: Each layer can be tested independently
Professional: Industry-standard project organization

🚀 For Features:

New LLM Providers: Extend services/llm_providers.py
UI Changes: Modify ui/ui_components.py
Export Formats: Add to services/document_generator.py
Data Sources: Extend services/resume_service.py
Configuration: Update core/config.py

📁 Organization Highlights:

Streamlit Compatible: app.py stays in root for deployment
Logical Grouping: Files organized by functionality, not arbitrarily
Clean Root: No clutter - only essential files visible
Python Packages: Proper __init__.py files for clean imports

Architecture Highlights:

88% reduction in main file complexity (1,500 → 180 lines)
8 focused modules instead of monolithic structure
Clear dependencies and import relationships
Single responsibility principle throughout

🚀 Getting Started

Clone and Install

git clone <your-repo>
cd mcp-resume
pip install -r requirements.txt

Run Locally
```
streamlit run app.py
```
Configure & Chat
- Navigate to http://localhost:8501
- Get a free API key from OpenRouter.ai
- Add your API key in the sidebar
- When you see "✅ All Systems Ready!" you're good to go!
- Start chatting with the AI resume!

🎨 Customization

🏠 Adding Your Own Resume

Step 1: Prepare Your Resume Data

{
  "personal_info": {
    "name": "Your Name",
    "title": "Your Professional Title",
    "email": "[email protected]",
    "phone": "+1-234-567-8900",
    "location": "Your City, Country",
    "linkedin": "https://linkedin.com/in/yourprofile",
    "github": "https://github.com/yourusername",
    "website": "https://yourwebsite.com"
  },
  "summary": "Your professional summary...",
  "experience": [
    {
      "company": "Company Name",
      "position": "Your Position",
      "duration": "2020 - Present",
      "description": "Your role description...",
      "achievements": ["Achievement 1", "Achievement 2"]
    }
  ],
  "skills": {
    "technical": ["Python", "JavaScript", "React"],
    "soft": ["Leadership", "Communication", "Problem Solving"]
  },
  "education": [
    {
      "institution": "University Name",
      "degree": "Bachelor of Science",
      "field": "Computer Science",
      "year": "2020"
    }
  ]
}

Step 2: Update Files

Replace resume data: Update data/michael_wybraniec_resume.json with your data
Update branding: Modify header in ui/ui_components.py
Replace CV: Add your PDF to data/ folder
Update config: Modify constants in core/config.py

🤖 LLM Configuration

Adding New Providers

# In services/llm_providers.py
class CustomProvider(LLMProvider):
    def __init__(self):
        super().__init__("custom_provider")
    
    def chat_completion(self, messages, model, api_key):
        # Your custom implementation
        response = your_api_call(messages, model, api_key)
        return response.choices[0].message.content

Customizing System Prompts

# In services/llm_providers.py
SYSTEM_PROMPT = """
You are an AI assistant helping with resume analysis.
Customize this prompt for your specific needs.
"""

🎨 UI Customization

Branding & Colors

# In ui/ui_components.py
def render_header():
    st.markdown("""
    <div style="background: linear-gradient(90deg, #your-color-1, #your-color-2);">
        <h1>Your Name - AI Resume</h1>
    </div>
    """, unsafe_allow_html=True)

Custom Quick Actions

# In ui/ui_components.py
def render_quick_actions():
    col1, col2 = st.columns(2)
    with col1:
        if st.button("🎯 Your Custom Action"):
            # Your custom logic
            pass

Custom Styling

# In ui/ui_components.py
def apply_custom_css():
    st.markdown("""
    <style>
    .custom-chat-message {
        background-color: #your-color;
        border-radius: 10px;
        padding: 10px;
    }
    </style>
    """, unsafe_allow_html=True)

📄 Export Customization

Custom PDF Templates

# In services/document_generator.py
def generate_custom_cv(resume_data):
    # Custom PDF generation logic
    # Add your company logo, custom fonts, etc.
    pass

Multiple Export Formats

# Add new export methods
def export_to_docx(resume_data):
    # DOCX export implementation
    pass

def export_to_html(resume_data):
    # HTML export implementation
    pass

🔧 Advanced Customization

Custom Data Sources

# In services/resume_service.py
def get_custom_context(query):
    # Add integration with external APIs
    # LinkedIn API, GitHub API, etc.
    pass

Analytics Integration

# Add usage tracking
def track_interaction(user_query, response_time):
    # Google Analytics, Mixpanel, etc.
    pass

Multi-language Support

# In core/config.py
SUPPORTED_LANGUAGES = {
    'en': 'English',
    'es': 'Español',
    'fr': 'Français',
    'de': 'Deutsch'
}

🚀 Deployment Customization

Environment Variables

# .env file
RESUME_OWNER_NAME="Your Name"
RESUME_OWNER_TITLE="Your Title"
CUSTOM_BRAND_COLOR="#your-color"
ENABLE_ANALYTICS=true

Streamlit Configuration

# .streamlit/config.toml
[theme]
primaryColor = "#your-color"
backgroundColor = "#ffffff"
secondaryBackgroundColor = "#f0f2f6"
textColor = "#262730"

📚 Customization Examples

Example 1: Software Developer Resume

Technical skills emphasis
GitHub integration
Project portfolio showcase
Code snippet examples

Example 2: Marketing Professional Resume

Campaign metrics focus
Social media integration
Brand awareness metrics
Creative portfolio links

Example 3: Data Scientist Resume

ML model showcase
Research publications
Data visualization examples
Technical blog integration

🔧 Adding New Features

Feature Development Checklist

Plan the feature - Define requirements and scope
Choose the right layer - UI, Services, Core, or Data
Implement incrementally - Start with core functionality
Add configuration - Make it customizable
Update documentation - Document new features
Test thoroughly - Ensure it works with existing features
Consider deployment - Update deployment configs if needed

Common Extension Points

New Export Format: Extend document_generator.py
Additional Data Sources: Modify resume_service.py
New UI Components: Add to ui_components.py
Custom Session Logic: Extend session_manager.py
New LLM Providers: Add to llm_providers.py
Configuration Options: Update core/config.py

🛠️ Development

Development Setup

# Clone and setup
git clone https://github.com/michaelwybraniec/mcp-resume.git
cd mcp-resume

# Create virtual environment
python -m venv venv
source venv/bin/activate

# Install development dependencies
pip install -r requirements.txt

# Run in development mode
streamlit run app.py --server.runOnSave true

Project Structure Overview

mcp-resume/
├── 🏠 app.py                    # Main Streamlit application
├── 🔧 core/                     # Configuration and data models
├── ⚙️ services/                 # Business logic and integrations
├── 🎨 ui/                       # User interface components
├── 📊 data/                     # Resume data files
└── 🔨 utils/                    # Utility scripts

Key Development Files

File	Purpose	Key Functions
`app.py`	Main entry point	Application initialization, message processing
`core/config.py`	Configuration	App settings, constants, model configurations
`services/llm_providers.py`	AI Integration	LLM provider implementations
`services/resume_service.py`	Data handling	Resume context generation
`ui/ui_components.py`	UI Components	Chat interface, modals, styling
`ui/session_manager.py`	State management	Session initialization and management

Adding New LLM Providers

Extend the provider class in services/llm_providers.py:

class NewProvider(LLMProvider):
    def __init__(self):
        super().__init__("new_provider")
    
    def chat_completion(self, messages, model, api_key):
        # Implementation here
        pass

Add to provider registry in the same file
Update configuration in core/config.py

Testing

# Run the application
streamlit run app.py

# Test different providers
# - OpenRouter: Use free models
# - Ollama: Run locally with `ollama serve`
# - OpenAI: Use your API key

Code Style

Follow PEP 8 guidelines
Use type hints where possible
Add docstrings for functions and classes
Keep functions focused and single-purpose

❓ Troubleshooting

Common Issues

🔑 API Key Problems

# Issue: "API Key Required" error
# Solution: 
1. Get free API key from https://openrouter.ai
2. Add key in sidebar or Streamlit secrets
3. Ensure no extra spaces in the key

🐍 Python Version Issues

# Issue: Import errors or compatibility issues
# Solution:
python --version  # Should be 3.11+
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

🌐 Streamlit Connection Issues

# Issue: App won't start or connection refused
# Solution:
streamlit run app.py --server.port 8501
# Check if port 8501 is available
# Try different port: --server.port 8502

🤖 LLM Provider Issues

OpenRouter:

Ensure API key is valid and has credits
Try different free models
Check rate limits

Ollama:

# Ensure Ollama is running
ollama serve

# Install required models
ollama pull llama3.2
ollama pull llama3.1

OpenAI:

Verify API key is valid
Check billing and usage limits
Ensure model is available

📱 UI Issues

Clear browser cache
Try different browser
Check console for JavaScript errors
Ensure stable internet connection

Getting Help

Check the logs in Streamlit for error messages
Verify configuration in the sidebar
Test with different providers to isolate issues
Check system status indicator in the app

📞 Contact & Support

Developer: Michael Wybraniec
Website: One-Front.com
LinkedIn: Connect with Michael
Custom AI Solutions: Contact through One-Front for enterprise implementations

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

What this means:

✅ Free to use for personal and commercial projects
✅ Modify and distribute as needed
✅ Private use allowed
✅ Attribution appreciated but not required

Usage Guidelines:

Feel free to fork and customize for your own resume
Commercial use is permitted
Please consider giving credit if you find it helpful
Report issues or contribute improvements via GitHub

🛠️ Recent Updates

✅ Modular Architecture: Refactored into 8 focused modules for better maintainability
🎯 88% Code Reduction: Main app now just 180 lines vs original 1,500
📚 Improved Documentation: Clear module responsibilities and architecture overview
🔧 Enhanced Testability: Each module can be tested independently
🚀 Better Scalability: Easy to add features without touching existing code
📱 Maintained All Features: Full functionality preserved in cleaner structure
🆕 Enhanced README: Modern documentation with comprehensive guides
⚖️ AI Act Compliance: Complete implementation of EU AI Act compliance framework
🔍 Advanced Monitoring: Real-time compliance monitoring with automated alerting
📋 Audit Procedures: Comprehensive audit framework with standardized checklists
📊 Performance Analytics: Advanced analytics with trend analysis and insights
🔍 Conformity Assessment: Systematic assessment procedures for certification readiness
📋 Certification Preparation: Complete document management for regulatory submission
✅ Compliance Validation: Automated and manual validation with certification eligibility

🎯 Roadmap

Planned Features

Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

🚀 Ready to revolutionize resume interactions?

Deploy your AI-powered resume today with professional-grade architecture and full EU AI Act compliance!

🎯 Why Choose This AI Resume?

✅ Fully Compliant - Complete EU AI Act compliance implementation
🏗️ Professional Architecture - Enterprise-grade modular design
🔍 Advanced Monitoring - Real-time compliance and performance monitoring
📋 Audit Ready - Comprehensive documentation and audit trails
🚀 Easy Deployment - Deploy to Streamlit Cloud in minutes
🆓 Free to Use - Open source with MIT license

Built with ❤️ by Michael Wybraniec