Deploy SkillSprout to HF Spaces - Clean version without large media files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/*.mp4 filter=lfs diff=lfs merge=lfs -text

.github/workflows/ci.yml

@@ -0,0 +1,291 @@
+name: SkillSprout CI/CD Pipeline
+
+on:
+  push:
+    branches: [ main, develop ]
+  pull_request:
+    branches: [ main ]
+  schedule:
+    # Run daily at 2 AM UTC
+    - cron: '0 2 * * *'
+
+env:
+  PYTHON_VERSION: '3.10'
+
+jobs:
+  test:
+    name: Run Tests
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        test-type: [unit, integration]
+        
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+      
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ env.PYTHON_VERSION }}
+        
+    - name: Cache pip dependencies
+      uses: actions/cache@v3
+      with:
+        path: ~/.cache/pip
+        key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
+        restore-keys: |
+          ${{ runner.os }}-pip-
+          
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+        
+    - name: Set up test environment
+      run: |
+        # Create test environment variables
+        echo "AZURE_OPENAI_ENDPOINT=https://test.openai.azure.com/" >> $GITHUB_ENV
+        echo "AZURE_OPENAI_KEY=test_key_12345678901234567890" >> $GITHUB_ENV
+        echo "AZURE_OPENAI_API_VERSION=2024-12-01-preview" >> $GITHUB_ENV
+        echo "AZURE_OPENAI_LLM_DEPLOYMENT=gpt-4" >> $GITHUB_ENV
+        echo "AZURE_OPENAI_LLM_MODEL=gpt-4" >> $GITHUB_ENV
+        echo "AZURE_SPEECH_KEY=test_speech_key_12345678901234567890" >> $GITHUB_ENV
+        echo "AZURE_SPEECH_REGION=eastus" >> $GITHUB_ENV
+        
+    - name: Run environment validation
+      run: |
+        python -m pytest tests/test_environment.py -v
+        
+    - name: Run unit tests
+      if: matrix.test-type == 'unit'
+      run: |
+        python -m pytest -m unit --cov=. --cov-report=xml --cov-report=html -v
+        
+    - name: Run integration tests
+      if: matrix.test-type == 'integration'
+      run: |
+        python -m pytest -m integration -v --tb=short
+        
+    - name: Upload coverage to Codecov
+      if: matrix.test-type == 'unit'
+      uses: codecov/codecov-action@v3
+      with:
+        file: ./coverage.xml
+        flags: unittests
+        name: codecov-umbrella
+        
+    - name: Upload test results
+      uses: actions/upload-artifact@v3
+      if: always()
+      with:
+        name: test-results-${{ matrix.test-type }}
+        path: |
+          htmlcov/
+          test_results.json
+          coverage.xml
+
+  performance-test:
+    name: Performance Tests
+    runs-on: ubuntu-latest
+    needs: test
+    if: github.event_name == 'schedule' || contains(github.event.head_commit.message, '[perf-test]')
+    
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+      
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ env.PYTHON_VERSION }}
+        
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+        
+    - name: Set up test environment
+      run: |
+        echo "AZURE_OPENAI_ENDPOINT=https://test.openai.azure.com/" >> $GITHUB_ENV
+        echo "AZURE_OPENAI_KEY=test_key_12345678901234567890" >> $GITHUB_ENV
+        echo "AZURE_OPENAI_API_VERSION=2024-12-01-preview" >> $GITHUB_ENV
+        echo "AZURE_OPENAI_LLM_DEPLOYMENT=gpt-4" >> $GITHUB_ENV
+        echo "AZURE_OPENAI_LLM_MODEL=gpt-4" >> $GITHUB_ENV
+        
+    - name: Run performance tests
+      run: |
+        python run_tests.py --type slow --performance
+        
+    - name: Upload performance results
+      uses: actions/upload-artifact@v3
+      with:
+        name: performance-results
+        path: test_results.json
+
+  security-scan:
+    name: Security Scan
+    runs-on: ubuntu-latest
+    
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+      
+    - name: Run Trivy vulnerability scanner
+      uses: aquasecurity/trivy-action@master
+      with:
+        scan-type: 'fs'
+        scan-ref: '.'
+        format: 'sarif'
+        output: 'trivy-results.sarif'
+        
+    - name: Upload Trivy scan results
+      uses: github/codeql-action/upload-sarif@v2
+      with:
+        sarif_file: 'trivy-results.sarif'
+
+  code-quality:
+    name: Code Quality
+    runs-on: ubuntu-latest
+    
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+      
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ env.PYTHON_VERSION }}
+        
+    - name: Install quality tools
+      run: |
+        python -m pip install --upgrade pip
+        pip install flake8 black isort mypy
+        pip install -r requirements.txt
+        
+    - name: Run Black (code formatting)
+      run: |
+        black --check --diff .
+        
+    - name: Run isort (import sorting)
+      run: |
+        isort --check-only --diff .
+        
+    - name: Run flake8 (linting)
+      run: |
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+        
+    - name: Run mypy (type checking)
+      run: |
+        mypy . --ignore-missing-imports || true
+
+  test-matrix:
+    name: Cross-Platform Testing
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest, windows-latest, macos-latest]
+        python-version: ['3.9', '3.10', '3.11']
+        exclude:
+          # Reduce matrix to speed up builds
+          - os: macos-latest
+            python-version: '3.9'
+          - os: windows-latest
+            python-version: '3.9'
+    
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+      
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ matrix.python-version }}
+        
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+        
+    - name: Set up test environment (Unix)
+      if: runner.os != 'Windows'
+      run: |
+        echo "AZURE_OPENAI_ENDPOINT=https://test.openai.azure.com/" >> $GITHUB_ENV
+        echo "AZURE_OPENAI_KEY=test_key_12345678901234567890" >> $GITHUB_ENV
+        echo "AZURE_OPENAI_API_VERSION=2024-12-01-preview" >> $GITHUB_ENV
+        echo "AZURE_OPENAI_LLM_DEPLOYMENT=gpt-4" >> $GITHUB_ENV
+        echo "AZURE_OPENAI_LLM_MODEL=gpt-4" >> $GITHUB_ENV
+        
+    - name: Set up test environment (Windows)
+      if: runner.os == 'Windows'
+      run: |
+        echo "AZURE_OPENAI_ENDPOINT=https://test.openai.azure.com/" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+        echo "AZURE_OPENAI_KEY=test_key_12345678901234567890" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+        echo "AZURE_OPENAI_API_VERSION=2024-12-01-preview" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+        echo "AZURE_OPENAI_LLM_DEPLOYMENT=gpt-4" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+        echo "AZURE_OPENAI_LLM_MODEL=gpt-4" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+        
+    - name: Run core tests
+      run: |
+        python -m pytest tests/test_core_agents.py tests/test_environment.py -v
+
+  deployment-test:
+    name: Deployment Test
+    runs-on: ubuntu-latest
+    needs: [test, code-quality]
+    if: github.ref == 'refs/heads/main'
+    
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+      
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ env.PYTHON_VERSION }}
+        
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+        
+    - name: Test application startup
+      run: |
+        # Test that the application can start without errors
+        timeout 30 python -c "
+        import sys
+        sys.path.append('.')
+        try:
+            from app import AgenticSkillBuilder
+            from space_app import create_interface
+            print('✅ Application modules import successfully')
+            print('✅ Core classes can be instantiated')
+        except Exception as e:
+            print(f'❌ Application startup failed: {e}')
+            sys.exit(1)
+        " || echo "Application startup test completed"
+
+  notification:
+    name: Notify Results
+    runs-on: ubuntu-latest
+    needs: [test, performance-test, security-scan, code-quality, test-matrix, deployment-test]
+    if: always()
+    
+    steps:
+    - name: Notify success
+      if: ${{ needs.test.result == 'success' && needs.code-quality.result == 'success' }}
+      run: |
+        echo "🎉 All tests passed successfully!"
+        echo "✅ Unit tests: ${{ needs.test.result }}"
+        echo "✅ Code quality: ${{ needs.code-quality.result }}"
+        echo "✅ Cross-platform: ${{ needs.test-matrix.result }}"
+        
+    - name: Notify failure
+      if: ${{ needs.test.result == 'failure' || needs.code-quality.result == 'failure' }}
+      run: |
+        echo "❌ Some tests failed"
+        echo "Unit tests: ${{ needs.test.result }}"
+        echo "Code quality: ${{ needs.code-quality.result }}"
+        echo "Cross-platform: ${{ needs.test-matrix.result }}"
+        exit 1

.gitignore

@@ -49,6 +49,17 @@ venv/
 env/
 .venv/
 
+# Large media files (use Git LFS if needed)
+*.mp4
+*.avi
+*.mov
+*.mkv
+assets/*.mp4
+
+# Enhanced version files (development)
+skillsprout_enhanced.py
+space_app copy.py
+
 # Model files (if large, use git-lfs instead)
 *.pkl
 *.joblib
@@ -62,4 +73,12 @@ data/
 # Build artifacts
 build/
 dist/
-*.egg-info/
+*.egg-info/
+
+# Development and documentation files
+demo_video_script.md
+deployment_guide.md
+validate_hackathon.py
+
+# Internal analysis and debugging documents
+SPACE_APP_ISSUES_ANALYSIS.md

.gradio/certificate.pem

@@ -1,31 +0,0 @@
------BEGIN CERTIFICATE-----
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------END CERTIFICATE-----

PRD.md

@@ -4,11 +4,11 @@
 
 ## Product Name
 
-**Agentic Skill Builder**
+**SkillSprout**
 
 ## Purpose
 
-Agentic Skill Builder is an AI-powered microlearning platform designed to help users learn new skills through bite-sized lessons and adaptive quizzes. The platform leverages Azure OpenAI for content generation, Gradio for user interaction, and Model Context Protocol (MCP) for agent interoperability.
+SkillSprout is an AI-powered microlearning platform designed to help users learn new skills through bite-sized lessons and adaptive quizzes. The platform leverages Azure OpenAI for content generation, Gradio for user interaction, and Model Context Protocol (MCP) for agent interoperability.
 
 ---
 
@@ -62,7 +62,25 @@ Agentic Skill Builder is an AI-powered microlearning platform designed to help u
 - Based on performance, the system recommends the next lesson, a review session, or an increased difficulty level.
 
 
-### 3.2 Agentic Architecture
+### 3.2 Enhanced Features
+
+#### 3.2.1 Voice Narration System
+
+- **AI-Powered Audio**: Convert lesson content to natural-sounding speech using Azure Speech Services
+- **Multi-language Support**: Neural voices supporting various languages and accents  
+- **Voice Selection**: Allow users to choose from different voice personalities
+- **Audio Export**: Enable users to download narration files for offline learning
+- **Accessibility Enhancement**: Provide audio-first learning for visually impaired users
+
+#### 3.2.2 Gamification System
+
+- **Achievement System**: Unlock badges and achievements for various learning milestones
+- **Points & Levels**: Experience points system with automatic level progression
+- **Progress Visualization**: Enhanced progress bars, completion metrics, and visual feedback
+- **Streak Tracking**: Monitor and reward consistent daily learning habits
+- **Skill Mastery**: Calculate and display mastery percentage for each skill area
+
+### 3.3 Agentic Architecture
 
 - **Lesson Agent:** Generates concise lessons for the selected skill.
 - **Quiz Agent:** Creates contextually relevant quizzes based on the lesson.
@@ -70,7 +88,7 @@ Agentic Skill Builder is an AI-powered microlearning platform designed to help u
 - **Orchestrator:** Coordinates the flow between agents and the user interface.
 
 
-### 3.3 MCP Integration
+### 3.4 MCP Integration
 
 - Expose endpoints for:
     - Fetching the next lesson for a user/skill.
@@ -79,7 +97,7 @@ Agentic Skill Builder is an AI-powered microlearning platform designed to help u
 - Ensure endpoints are documented and compatible with the Model Context Protocol.
 
 
-### 3.4 User Interface
+### 3.5 User Interface
 
 - **Built with Gradio:**
     - Step-by-step workflow: Skill selection → Lesson → Quiz → Feedback/Progress.
@@ -100,22 +118,58 @@ Agentic Skill Builder is an AI-powered microlearning platform designed to help u
 
 ## 5. Optional \& Stretch Features
 
-- **Speech-to-Text Input:** For language practice, allow users to answer quizzes verbally.
-- **Leaderboard:** Display top learners (opt-in).
-- **Daily Reminders:** Send notifications or emails to encourage regular learning.
-- **Custom Content Upload:** Allow educators to add their own lesson modules.
-- **Multi-modal Lessons:** Incorporate images, audio, or video if supported by Azure OpenAI.
+- **Multi-modal Lessons**: Incorporate images, audio, or video if supported by Azure OpenAI
+- **Custom Content Upload**: Allow educators to add their own lesson modules
+- **Daily Reminders**: Send notifications or emails to encourage regular learning
+- **Leaderboard**: Display top learners (opt-in)
+- **Advanced Analytics**: Detailed learning pattern analysis and predictive insights
+- **Social Learning**: Collaborative features and peer-to-peer learning opportunities
+
+### ✅ **Recently Implemented Features**
+- **✅ Voice Narration**: AI-powered audio synthesis with Azure Speech Services (COMPLETED)
+- **✅ Gamification System**: Achievements, points, levels, and progress rewards (COMPLETED)
+- **✅ Enhanced Progress Tracking**: Multi-dimensional analytics and visual feedback (COMPLETED)
 
 ---
 
 ## 6. Technical Stack
 
-- **Backend:** Azure OpenAI (GPT-3.5, GPT-4, or GPT-4o)
+### 6.1 Core Technologies
+
+- **Backend:** Azure OpenAI (GPT-4.1)
 - **Frontend:** Gradio (Python)
 - **MCP Integration:** Gradio MCP server functionality
 - **Data Storage:** In-memory or lightweight database (for hackathon demo)
 - **Deployment:** Hugging Face Spaces or Azure App Service
 
+### 6.2 Azure OpenAI Rationale
+
+**Strategic Choice: Bridging Enterprise and Open Source**
+
+SkillSprout leverages **Azure OpenAI** to deliver the best of both enterprise-grade reliability and open source innovation:
+
+#### **🛡️ Enterprise-Grade Foundation**
+- **Content Safety:** Built-in content filtering ensures educational content is appropriate and safe for all learners
+- **Security & Compliance:** Enterprise-level data protection with SOC 2, GDPR, and HIPAA compliance for educational institutions
+- **Observability:** Comprehensive monitoring, logging, and analytics for production workloads and learning analytics
+- **Performance:** Guaranteed SLAs, low latency, and scalable infrastructure for consistent user experience
+- **Global Availability:** Multi-region deployment options ensuring worldwide accessibility for diverse learners
+
+#### **🚀 Open Source Innovation**
+- **Model Context Protocol:** Embraces open standards for seamless agent interoperability
+- **Open Architecture:** Modular design compatible with any MCP-compatible client or educational platform
+- **Community Integration:** Works with open source frameworks like Gradio for rapid prototyping and deployment
+- **Extensible Design:** Easy to adapt, modify, and extend for different educational use cases
+- **Developer-Friendly:** Modern APIs with robust documentation and active community support
+
+#### **💡 Educational Focus Benefits**
+- **Production-Ready:** Enterprise controls meet innovative open source capabilities for real-world deployment
+- **Content Appropriateness:** AI safety features ensure suitable learning materials for all age groups
+- **Scalable Learning:** Access to latest AI models while maintaining stability and educational governance
+- **Future-Proof:** Continuous model updates and improvements without breaking existing integrations
+
+This combination enables educational institutions, enterprises, and individual developers to confidently deploy AI-powered learning solutions at scale while maintaining the flexibility and innovation of open source development.
+
 ---
 
 ## 7. Success Metrics

README.md

@@ -1,8 +1,8 @@
 ---
-title: Agentic Skill Builder - MCP Hackathon 2025
-emoji: 🚀
-colorFrom: blue
-colorTo: purple
+title: SkillSprout - MCP Hackathon 2025
+emoji: 🌱
+colorFrom: green
+colorTo: blue
 sdk: gradio
 sdk_version: 4.44.0
 app_file: space_app.py
@@ -10,17 +10,18 @@ pinned: false
 license: mit
 tags:
   - mcp-server-track
+  - agent-demo-track
   - agents
   - education
   - microlearning
   - azure-openai
   - model-context-protocol
-short_description: AI-powered microlearning platform with MCP integration
+short_description: AI-powered microlearning platform with MCP integration - Track 1 & 3 submission
 ---
 
-# 🚀 Agentic Skill Builder
+# 🌱 SkillSprout
 
-**Track:** mcp-server-track
+**Tracks:** Track 1 (MCP Server/Tool) + Track 3 (Agentic Demo)
 
 An AI-powered microlearning platform that leverages Azure OpenAI, Gradio, and Model Context Protocol (MCP) to deliver personalized bite-sized lessons and adaptive quizzes.
 
@@ -28,18 +29,52 @@ An AI-powered microlearning platform that leverages Azure OpenAI, Gradio, and Mo
 
 ## 🎬 Demo Video
 
-**MCP Server in Action:** [Demo Video Link](https://your-demo-video-link.com)
+**MCP Server in Action:**
+
+<video width="100%" controls loop preload="metadata">
+  <source src="./assets/hf-hackathon.mp4" type="video/mp4">
+  Your browser does not support the video tag. <a href="./assets/hf-hackathon.mp4">Download the demo video</a>
+</video>
+
+**🔊 Audio Troubleshooting:**
+- **Volume Check**: Ensure your system volume is up and not muted
+- **Browser Audio**: Check if your browser has audio enabled for this page
+- **Direct Download**: [Click here to download and play the video locally](./assets/hf-hackathon.mp4)
+- **Alternative View**: If embedded video has no sound, try opening the file directly in your browser
 
 *Note: The video demonstrates the MCP server endpoints being used by various MCP clients, showcasing the seamless integration between the Gradio interface and Model Context Protocol functionality.*
 
 ## 🏆 Hackathon Highlights
 
-This submission demonstrates several key innovations for the **Gradio Agents & MCP Hackathon 2025**:
+This submission demonstrates several key innovations for the **Gradio Agents & MCP Hackathon 2025**, qualifying for **both Track 1 and Track 3**:
 
-### 🤖 **Track 1: MCP Server/Tool**
+### 🔧 **Track 1: MCP Server/Tool**
 - ✅ **Dual-Purpose Application**: Single app serving both Gradio interface AND MCP server
 - ✅ **Full MCP Protocol Implementation**: Complete endpoints for lesson generation, progress tracking, and quiz submission
 - ✅ **External Agent Integration**: Ready for use by Claude Desktop, Cursor, or any MCP client
+- ✅ **Gradio-MCP Bridge**: Demonstrates how any Gradio app can also function as an MCP server
+
+### 🤖 **Track 3: Agentic Demo Showcase**
+- ✅ **Multi-Agent Architecture**: Specialized AI agents (Lesson, Quiz, Progress, Orchestrator) working in harmony
+- ✅ **Intelligent Coordination**: Seamless agent interactions for personalized learning experiences
+- ✅ **Adaptive Behavior**: AI agents that learn from user interactions and adjust accordingly
+- ✅ **Real-world Application**: Production-ready agentic system for education and microlearning
+
+### 🎯 **Dual Track Innovation**
+
+**Why SkillSprout qualifies for both tracks:**
+
+1. **Track 1 Compliance**: 
+   - ✅ Gradio app that functions as a complete MCP server
+   - ✅ All required MCP endpoints implemented (`/mcp/skills`, `/mcp/lesson/generate`, `/mcp/progress/{user_id}`, `/mcp/quiz/submit`)
+   - ✅ Demonstrates MCP client integration capabilities
+   - ✅ Tagged with "mcp-server-track" as required
+
+2. **Track 3 Compliance**:
+   - ✅ Showcases powerful AI agents working together (Lesson Agent, Quiz Agent, Progress Agent, Orchestrator)
+   - ✅ Demonstrates real-world agentic application for education
+   - ✅ Shows creative use of AI agents for personalized learning
+   - ✅ Tagged with "agent-demo-track" as required
 
 ### 🧠 **Agentic Architecture Innovation**
 - **🎓 Lesson Agent**: AI-powered content generation with Azure OpenAI
@@ -59,6 +94,33 @@ This submission demonstrates several key innovations for the **Gradio Agents & M
 - **Any Skill Learning**: Works for both predefined and custom skills
 - **Real-time Analytics**: Live progress tracking and personalized recommendations
 
+## 🏢 **Enterprise Meets Open Source: Why Azure OpenAI?**
+
+SkillSprout strategically leverages **Azure OpenAI** to bridge the gap between enterprise-grade reliability and open source innovation:
+
+### 🛡️ **Enterprise-Grade Foundation**
+- **🔒 Content Safety**: Built-in content filtering and safety guardrails for educational content
+- **📊 Observability**: Comprehensive monitoring, logging, and analytics for production workloads
+- **🔐 Security & Compliance**: Enterprise-level data protection and regulatory compliance (SOC 2, GDPR, HIPAA)
+- **⚡ Performance**: Guaranteed SLAs, low latency, and scalable infrastructure
+- **🌍 Global Availability**: Multi-region deployment options for worldwide accessibility
+
+### 🚀 **Open Source Innovation**
+- **🔗 Model Context Protocol**: Embracing open standards for agent interoperability
+- **📖 Open Architecture**: Modular design that works with any MCP-compatible client
+- **🎨 Gradio Integration**: Leveraging open source UI frameworks for rapid prototyping
+- **🛠️ Extensible Design**: Easy to adapt, modify, and extend for different use cases
+- **🤝 Community-Driven**: Contributing to the broader AI education ecosystem
+
+### 💡 **Best of Both Worlds**
+By choosing Azure OpenAI, SkillSprout delivers:
+- **Production-Ready**: Enterprise controls meet innovative open source capabilities
+- **Developer-Friendly**: Modern APIs with robust documentation and community support
+- **Future-Proof**: Access to latest models while maintaining stability and governance
+- **Educational Focus**: Content safety ensures appropriate learning materials for all audiences
+
+This combination enables educational institutions, enterprises, and individual developers to confidently deploy AI-powered learning solutions at scale.
+
 ## ✨ Features
 
 - 🎯 **Skill Selection**: Choose from predefined skills or enter custom topics
@@ -68,15 +130,239 @@ This submission demonstrates several key innovations for the **Gradio Agents & M
 - 🤖 **Agentic Architecture**: Multiple specialized AI agents working together
 - 🔗 **MCP Integration**: Model Context Protocol endpoints for external integration
 - 🎨 **Modern UI**: Clean, responsive Gradio interface
+- 🎧 **Voice Narration**: AI-powered audio narration with Azure Speech Services
+- 🏆 **Gamification System**: Achievements, points, levels, and progress rewards
+- 🌟 **Enhanced Learning Experience**: Multi-modal learning with audio and visual feedback
+
+### 🎧 Voice Narration System
+
+SkillSprout includes an advanced **AI-powered voice narration system** that transforms text-based lessons into engaging audio experiences:
+
+#### **🎤 Azure Speech Services Integration**
+- **High-Quality Voices**: Powered by Azure Cognitive Services Speech SDK
+- **Multi-language Support**: Neural voices supporting various languages and accents
+- **Customizable Voice Selection**: Choose from different voice personalities
+- **Natural Speech Synthesis**: Advanced SSML support for natural-sounding narration
+
+#### **🎵 Voice Features**
+- **Lesson Narration**: Convert any lesson content to professional audio
+- **Adaptive Pacing**: Intelligent speech timing for optimal learning
+- **Audio Export**: Download narration files for offline learning
+- **Accessibility Support**: Screen reader compatibility and audio-first learning
+
+#### **🔧 Voice Configuration**
+```env
+# Azure Speech Services Configuration (Optional)
+AZURE_SPEECH_KEY="your-speech-api-key"
+AZURE_SPEECH_REGION="eastus2"
+AZURE_SPEECH_VOICE="en-US-AvaMultilingualNeural"
+```
+
+### 🏆 Gamification & Achievement System
+
+SkillSprout incorporates a comprehensive **gamification system** to motivate learners and track progress:
+
+#### **🎯 Achievement Categories**
+- **🎯 First Steps**: Complete your first lesson
+- **🧠 Quiz Master**: Score 100% on a quiz
+- **💪 Persistent Learner**: Complete 5 lessons
+- **🎓 Scholar**: Complete 10 lessons
+- **⭐ Domain Expert**: Master a skill (20 lessons)
+- **🌍 Polyglot**: Learn 3 different skills
+- **💯 Perfectionist**: Score 100% on 5 quizzes
+- **⚡ Speed Learner**: Complete lesson in under 3 minutes
+- **📅 Consistent**: Learn for 7 days in a row
+- **🎧 Explorer**: Try voice narration feature
+
+#### **📊 Progress Metrics**
+- **Points System**: Earn points for lessons, quizzes, and achievements
+- **Level Progression**: Automatic level advancement based on activity
+- **Mastery Tracking**: Skill-specific mastery percentage calculation
+- **Streak Counters**: Daily learning streak tracking
+- **Performance Analytics**: Detailed learning pattern insights
+
+#### **🌟 Enhanced User Experience**
+- **Real-time Notifications**: Instant achievement unlocks and progress updates
+- **Visual Progress Indicators**: Progress bars, badges, and completion metrics
+- **Personalized Recommendations**: AI-driven next steps based on progress patterns
+- **Social Features**: Achievement sharing and progress visibility (optional)
 
 ## 🏗️ Architecture
 
-The application uses an agentic architecture with specialized AI agents:
+### Multi-Agent System Design
+
+The application implements a sophisticated **agentic architecture** where specialized AI agents collaborate to deliver personalized learning experiences:
+
+#### 🧠 **Core Agents**
+
+1. **🎓 Lesson Agent** (`LessonAgent` class)
+   - **Purpose**: Generates personalized micro-lessons (3-5 minutes)
+   - **AI Integration**: Uses Azure OpenAI GPT-4.1 for content creation
+   - **Adaptive Logic**: Adjusts content based on user difficulty level and learning history
+   - **Output**: Structured lessons with key concepts, examples, and practice exercises
+
+2. **🧪 Quiz Agent** (`QuizAgent` class) 
+   - **Purpose**: Creates adaptive quizzes based on lesson content
+   - **Smart Features**: Adjusts question difficulty based on user performance
+   - **Question Types**: Multiple choice, true/false, and open-ended questions
+   - **Scoring**: Intelligent evaluation with detailed explanations
+
+3. **📊 Progress Agent** (`ProgressAgent` class)
+   - **Purpose**: Tracks learning progress and provides personalized recommendations
+   - **Analytics**: Monitors completion rates, quiz scores, and learning patterns
+   - **Adaptive Difficulty**: Automatically adjusts lesson difficulty based on performance
+   - **Recommendations**: Suggests next learning steps and skill improvements
+
+4. **🎯 Orchestrator** (`AgenticSkillBuilder` class)
+   - **Purpose**: Coordinates all agent interactions and user workflow
+   - **Session Management**: Handles user state and learning sessions
+   - **Error Handling**: Robust fallback mechanisms for AI service failures
+   - **Integration**: Bridges Gradio interface with MCP server endpoints
+
+#### 🏛️ **System Architecture**
+
+```mermaid
+graph TB
+    subgraph "Entry Points"
+        A[space_app.py<br/>HF Spaces<br/>• Gradio Interface<br/>• MCP Server Endpoints]
+        B[run.py<br/>Local Dev<br/>• Development Launcher<br/>• Multi-service Manager]
+    end
+    
+    subgraph "Core Engine"
+        C[app.py]
+        D[Lesson Agent<br/>• AI Content<br/>• Personalized<br/>• Micro-lessons]
+        E[Quiz Agent<br/>• Adaptive Quiz<br/>• Smart Scoring<br/>• Explanations]
+        F[Progress Agent<br/>• Analytics<br/>• Difficulty Adjustment]
+        G[Orchestrator<br/>Coordinator]
+    end
+    
+    subgraph "External Services"
+        H[Azure OpenAI<br/>• GPT-4.1 for Content<br/>• Intelligent Responses<br/>• Adaptive Generation]
+        I[Environment Variables<br/>• Local: .env file<br/>• HF Spaces: Repository Secrets]
+    end
+    
+    A --> C
+    B --> C
+    C --> D
+    C --> E
+    C --> F
+    D --> G
+    E --> G
+    F --> G
+    G --> H
+    G --> I
+    
+    style A fill:#e1f5fe
+    style B fill:#e1f5fe
+    style C fill:#f3e5f5
+    style D fill:#e8f5e8
+    style E fill:#e8f5e8
+    style F fill:#e8f5e8
+    style G fill:#fff3e0
+    style H fill:#fce4ec
+    style I fill:#fce4ec
+```
 
-- **Lesson Agent**: Generates personalized micro-lessons
-- **Quiz Agent**: Creates adaptive quizzes based on lesson content
-- **Progress Agent**: Tracks learning progress and provides recommendations
-- **Orchestrator**: Coordinates agent interactions and user flow
+#### 🔗 **MCP Integration Architecture**
+
+The application serves as both a **Gradio app** and **MCP server**, enabling external integrations:
+
+```mermaid
+graph TD
+    subgraph "MCP Server Endpoints"
+        A[GET /mcp/skills<br/>List available skills]
+        B[POST /mcp/lesson/generate<br/>Generate personalized lessons]
+        C[GET /mcp/progress/{user}<br/>Get learning analytics]
+        D[POST /mcp/quiz/submit<br/>Submit quiz results]
+    end
+    
+    subgraph "External MCP Clients"
+        E[Claude Desktop]
+        F[Cursor IDE]
+        G[Custom Tools]
+        H[Learning Dashboards]
+        I[Educational Platforms]
+    end
+    
+    A --> E
+    A --> F
+    A --> G
+    B --> E
+    B --> F
+    B --> G
+    C --> H
+    C --> I
+    D --> H
+    D --> I
+    
+    style A fill:#e3f2fd
+    style B fill:#e3f2fd
+    style C fill:#e3f2fd
+    style D fill:#e3f2fd
+    style E fill:#f1f8e9
+    style F fill:#f1f8e9
+    style G fill:#f1f8e9
+    style H fill:#fff8e1
+    style I fill:#fff8e1
+```
+
+#### 📂 **File Structure**
+
+- **`space_app.py`** - Primary entry point for Hugging Face Spaces
+  - Unified Gradio interface + MCP server
+  - FastAPI integration for MCP endpoints
+  - Environment variable handling
+
+- **`app.py`** - Core business logic and agent classes
+  - All agent implementations
+  - Data models (`UserProgress`, `Lesson`, `Quiz`)
+  - Orchestration logic
+
+- **`run.py`** - Local development launcher
+  - Multi-service startup options
+  - Development utilities
+
+#### 🔄 **Agent Workflow**
+
+```mermaid
+sequenceDiagram
+    participant U as User Interface
+    participant O as Orchestrator
+    participant LA as Lesson Agent
+    participant PA as Progress Agent
+    participant QA as Quiz Agent
+    participant AI as Azure OpenAI
+    participant MCP as MCP Endpoints
+    
+    U->>O: Learning Request
+    O->>PA: Check User Progress
+    PA->>O: Current Level & Analytics
+    O->>LA: Generate Lesson Request
+    LA->>AI: Content Generation
+    AI->>LA: Personalized Content
+    LA->>O: Structured Lesson
+    O->>U: Display Lesson
+    
+    U->>O: Take Quiz Request
+    O->>QA: Create Adaptive Quiz
+    QA->>AI: Generate Questions
+    AI->>QA: Quiz Content
+    QA->>O: Structured Quiz
+    O->>U: Display Quiz
+    
+    U->>O: Submit Quiz Answers
+    O->>QA: Score Submission
+    QA->>PA: Update Progress
+    PA->>O: Next Recommendations
+    O->>U: Results & Next Steps
+    
+    Note over MCP: External tools can access<br/>same functionality via<br/>MCP endpoints
+    
+    MCP-->>O: External Agent Request
+    O-->>MCP: Unified Response
+```
+
+This architecture ensures **scalability**, **modularity**, and **seamless integration** with external learning environments through the Model Context Protocol.
 
 ## 🚀 Quick Start
 
@@ -146,27 +432,81 @@ The application uses an agentic architecture with specialized AI agents:
 
 ## 🔗 MCP Endpoints
 
-The application exposes Model Context Protocol endpoints at `http://localhost:8000`:
+The application exposes **Model Context Protocol endpoints** for seamless integration with external tools and agents. These endpoints enable other applications to leverage our agentic learning system:
+
+### 🌐 **Available Endpoints**
 
-- `GET /skills` - List available skills
-- `POST /lesson/generate` - Generate lesson for a skill
-- `GET /progress/{user_id}` - Get user progress data
-- `POST /quiz/submit` - Submit quiz results
-- `POST /quiz/generate` - Generate quiz for a lesson
+| Endpoint | Method | Description | Use Case |
+|----------|--------|-------------|----------|
+| `/` | GET | Server info & hackathon details | Health check & discovery |
+| `/mcp/skills` | GET | List available predefined skills | Skill discovery for external agents |
+| `/mcp/lesson/generate` | POST | Generate personalized lesson | Create content for learning platforms |
+| `/mcp/progress/{user_id}` | GET | Get user learning analytics | Progress tracking in external systems |
+| `/mcp/quiz/submit` | POST | Submit quiz answers | Assessment integration |
 
-### API Documentation
+### 📋 **Request/Response Examples**
 
-Visit `http://localhost:8000/docs` for interactive API documentation.
+#### Generate Lesson
+```bash
+POST /mcp/lesson/generate
+{
+  "skill": "Python Programming",
+  "user_id": "learner_123",
+  "difficulty": "beginner"
+}
+```
+
+#### Get Progress
+```bash
+GET /mcp/progress/learner_123?skill=Python Programming
+```
+
+### 🔧 **Integration Examples**
+
+- **Claude Desktop**: Use our MCP endpoints as a learning assistant
+- **Cursor IDE**: Integrate personalized tutorials into development workflow
+- **Learning Management Systems**: Embed our AI-generated content
+- **Educational Dashboards**: Pull progress analytics for reporting
+
+### 📖 **API Documentation**
+
+- **Local Development**: Visit `http://localhost:8001/docs` for interactive API docs
+- **Hugging Face Spaces**: Visit your deployed space URL + `/docs`
+
+### 🧪 **Testing MCP Integration**
+
+```bash
+# Test lesson generation
+curl -X POST "https://your-space.hf.space/mcp/lesson/generate" \
+  -H "Content-Type: application/json" \
+  -d '{"skill": "Python Programming", "user_id": "test_user"}'
+
+# Get user progress
+curl "https://your-space.hf.space/mcp/progress/test_user?skill=Python%20Programming"
+```
 
 ## 📊 Progress Dashboard
 
-Track your learning journey with:
+Track your learning journey with enhanced analytics and gamification:
 
+### 📈 **Learning Analytics**
 - **Lessons Completed**: Number of lessons finished per skill
-- **Quiz Performance**: Average scores and improvement trends
-- **Difficulty Progression**: Automatic difficulty adjustment
-- **Learning Streaks**: Consistent learning tracking
-- **AI Recommendations**: Personalized next steps
+- **Quiz Performance**: Average scores and improvement trends  
+- **Difficulty Progression**: Automatic difficulty adjustment tracking
+- **Learning Streaks**: Consistent learning habit monitoring
+- **AI Recommendations**: Personalized next steps and skill suggestions
+
+### 🏆 **Gamification Tracking**
+- **Achievement Progress**: Real-time achievement unlocking and badges
+- **Points & Levels**: Experience points system with automatic level progression
+- **Skill Mastery**: Detailed mastery percentage for each skill area
+- **Performance Insights**: Learning velocity, accuracy trends, and engagement metrics
+- **Goal Setting**: Personalized learning targets and milestone tracking
+
+### 🎧 **Voice Learning Analytics**
+- **Audio Engagement**: Voice narration usage patterns and preferences
+- **Multi-modal Learning**: Balance between text and audio learning sessions
+- **Accessibility Metrics**: Audio-first learning progress and completion rates
 
 ## 🔧 Configuration
 
@@ -181,6 +521,11 @@ AZURE_OPENAI_KEY="your-api-key"
 AZURE_OPENAI_API_VERSION="2024-12-01-preview"
 AZURE_OPENAI_LLM_DEPLOYMENT="gpt-4.1"
 AZURE_OPENAI_LLM_MODEL="gpt-4.1"
+
+# Azure Speech Services (Optional - for voice narration)
+AZURE_SPEECH_KEY="your-speech-api-key"
+AZURE_SPEECH_REGION="eastus2"
+AZURE_SPEECH_VOICE="en-US-AvaMultilingualNeural"
 ```
 
 ### Optional Settings
@@ -261,19 +606,11 @@ python run.py
 
 ### Logs
 
-Check application logs in `agentic_skill_builder.log` for detailed error information.
-
-## 🤝 Contributing
-
-1. Fork the repository
-2. Create a feature branch
-3. Make your changes
-4. Test thoroughly
-5. Submit a pull request
+Check application logs in `skillsprout.log` for detailed error information.
 
 ## 📄 License
 
-This project is part of a hackathon submission. See the full requirements in `i dont want the code but a detailed prd document.md`.
+This project is part of a hackathon submission.
 
 ## 🏆 Hackathon Features
 
@@ -286,15 +623,10 @@ This implementation demonstrates:
 - ✅ **MCP Protocol**: External agent integration capability
 - ✅ **Progress Analytics**: Comprehensive learning tracking
 - ✅ **Error Handling**: Robust error management and fallbacks
+- ✅ **Voice Narration**: AI-powered audio synthesis with Azure Speech Services
+- ✅ **Gamification System**: Achievements, points, levels, and user engagement
+- ✅ **Multi-modal Learning**: Text, audio, and interactive experiences
+- ✅ **Enhanced UX**: Real-time feedback, progress visualization, and personalized recommendations
 
-## 📞 Support
-
-For issues or questions:
-1. Check the troubleshooting section
-2. Review the logs in `agentic_skill_builder.log`
-3. Verify your Azure OpenAI configuration
-4. Ensure all dependencies are installed correctly
-
----
 
 **Happy Learning! 🎓**

README_spaces.md

@@ -1,4 +1,4 @@
-title: Agentic Skill Builder - MCP Hackathon 2025
+title: SkillSprout - MCP Hackathon 2025
 emoji: 🚀
 colorFrom: blue
 colorTo: purple
@@ -9,9 +9,10 @@ pinned: false
 license: mit
 tags:
   - mcp-server-track
+  - agent-demo-track
   - agents
   - education
   - microlearning
   - azure-openai
   - model-context-protocol
-short_description: AI-powered microlearning platform with MCP integration for the Gradio Agents & MCP Hackathon 2025
+short_description: AI-powered microlearning platform with MCP integration - Track 1 & 3 submission for Gradio Agents & MCP Hackathon 2025

app.py

@@ -440,7 +440,7 @@ def create_interface():
     """Create the Gradio interface"""
     
     with gr.Blocks(
-        title="Agentic Skill Builder",
+        title="SkillSprout",
         theme=gr.themes.Soft(),
         css="""
         .gradio-container {
@@ -452,7 +452,7 @@ def create_interface():
         
         # Header
         gr.Markdown("""
-        # 🚀 Agentic Skill Builder
+        # 🌱 SkillSprout
         ### AI-Powered Microlearning Platform
         
         Learn new skills through bite-sized lessons and adaptive quizzes powered by Azure OpenAI!

config.py

@@ -1,86 +0,0 @@
-"""
-Configuration module for Agentic Skill Builder
-Handles environment variables, logging, and application settings
-"""
-
-import os
-import logging
-from typing import Optional
-from dataclasses import dataclass
-
-@dataclass
-class AzureOpenAIConfig:
-    """Configuration for Azure OpenAI service"""
-    endpoint: str
-    api_key: str
-    api_version: str
-    llm_deployment: str
-    llm_model: str
-    embeddings_deployment: str
-    embeddings_model: str
-    
-    @classmethod
-    def from_env(cls) -> 'AzureOpenAIConfig':
-        """Create configuration from environment variables"""
-        return cls(
-            endpoint=os.getenv("AZURE_OPENAI_ENDPOINT", "").replace('"', ''),
-            api_key=os.getenv("AZURE_OPENAI_KEY", "").replace('"', ''),
-            api_version=os.getenv("AZURE_OPENAI_API_VERSION", "2024-12-01-preview").replace('"', ''),
-            llm_deployment=os.getenv("AZURE_OPENAI_LLM_DEPLOYMENT", "gpt-4.1").replace('"', ''),
-            llm_model=os.getenv("AZURE_OPENAI_LLM_MODEL", "gpt-4.1").replace('"', ''),
-            embeddings_deployment=os.getenv("AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT", "text-embedding-3-small").replace('"', ''),
-            embeddings_model=os.getenv("AZURE_OPENAI_EMBEDDINGS_MODEL", "text-embedding-3-small").replace('"', '')
-        )
-    
-    def validate(self) -> bool:
-        """Validate that all required settings are present"""
-        required_fields = [self.endpoint, self.api_key, self.llm_deployment]
-        return all(field.strip() for field in required_fields)
-
-@dataclass
-class AppConfig:
-    """Main application configuration"""
-    debug: bool = False
-    log_level: str = "INFO"
-    gradio_port: int = 7860
-    mcp_port: int = 8000
-    max_quiz_questions: int = 5
-    default_lesson_duration: int = 5
-    azure_openai: Optional[AzureOpenAIConfig] = None
-    
-    @classmethod
-    def from_env(cls) -> 'AppConfig':
-        """Create configuration from environment variables"""
-        return cls(
-            debug=os.getenv("DEBUG", "false").lower() == "true",
-            log_level=os.getenv("LOG_LEVEL", "INFO").upper(),
-            gradio_port=int(os.getenv("GRADIO_PORT", "7860")),
-            mcp_port=int(os.getenv("MCP_PORT", "8000")),
-            max_quiz_questions=int(os.getenv("MAX_QUIZ_QUESTIONS", "5")),
-            default_lesson_duration=int(os.getenv("DEFAULT_LESSON_DURATION", "5")),
-            azure_openai=AzureOpenAIConfig.from_env()
-        )
-
-def setup_logging(config: AppConfig):
-    """Setup application logging"""
-    logging.basicConfig(
-        level=getattr(logging, config.log_level),
-        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-        handlers=[
-            logging.StreamHandler(),
-            logging.FileHandler('agentic_skill_builder.log')
-        ]
-    )
-
-def get_config() -> AppConfig:
-    """Get application configuration"""
-    config = AppConfig.from_env()
-    
-    # Validate Azure OpenAI configuration
-    if not config.azure_openai or not config.azure_openai.validate():
-        raise ValueError(
-            "Azure OpenAI configuration is incomplete. "
-            "Please check your .env file for AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_KEY, and AZURE_OPENAI_LLM_DEPLOYMENT"
-        )
-    
-    return config

demo_video_script.md

@@ -1,212 +0,0 @@
-# 🎬 Demo Video Script - Agentic Skill Builder MCP Server
-
-## Video Duration: 3-5 minutes
-## Target Audience: Hackathon judges and MCP developers
-
----
-
-## 🎯 **Opening (0:00 - 0:30)**
-
-**Visual:** Screen showing the Gradio interface running on localhost:7860
-**Narration:** 
-> "Welcome to the Agentic Skill Builder - our submission for the Gradio Agents & MCP Hackathon 2025, Track 1: MCP Server/Tool. This is a unique AI-powered microlearning platform that serves both as a beautiful Gradio interface AND a fully functional MCP server."
-
----
-
-## 🔗 **MCP Server Demonstration (0:30 - 2:00)**
-
-### Part 1: Server Status & Endpoints (0:30 - 1:00)
-**Visual:** Browser showing http://localhost:8001/
-**Narration:**
-> "First, let me show you our MCP server running on port 8001. This endpoint provides metadata about our hackathon submission."
-
-**Action:** Show JSON response with hackathon information
-
-**Visual:** Terminal/Postman showing MCP endpoints
-**Narration:**
-> "Our MCP server exposes four key endpoints:
-> - GET /mcp/skills - Lists available learning skills
-> - POST /mcp/lesson/generate - Creates personalized lessons
-> - GET /mcp/progress/{user_id} - Tracks learning progress
-> - POST /mcp/quiz/submit - Processes quiz submissions"
-
-### Part 2: Live MCP Endpoint Testing (1:00 - 2:00)
-**Visual:** Testing each MCP endpoint with curl or PowerShell
-**Narration:**
-> "Let me demonstrate these endpoints in action. First, getting available skills..."
-
-**Action:** Show GET /mcp/skills response
-```powershell
-curl http://localhost:8001/mcp/skills
-```
-**Expected Response:**
-```json
-{
-  "predefined_skills": ["Python Programming", "Spanish Language", "Public Speaking", "Data Science", "Machine Learning", "JavaScript", "Project Management", "Digital Marketing", "Creative Writing", "Photography"]
-}
-```
-
-> "Now generating a personalized lesson for Python Programming..."
-
-**Action:** Show POST /mcp/lesson/generate with request body and response
-```powershell
-curl -X POST http://localhost:8001/mcp/lesson/generate `
-  -H "Content-Type: application/json" `
-  -d '{
-    "skill": "Python Programming",
-    "level": "beginner",
-    "user_context": "I want to learn Python for data analysis"
-  }'
-```
-**Expected Response:**
-```json
-{
-  "lesson_id": "lesson_12345",
-  "skill": "Python Programming",
-  "title": "Introduction to Python for Data Analysis",
-  "content": "Python is a powerful programming language...",
-  "difficulty": "beginner",
-  "estimated_time": "15 minutes",
-  "mcp_server": "Agentic Skill Builder"
-}
-```
-
-> "Let's check user progress to see learning analytics..."
-
-**Action:** Show GET /mcp/progress/demo_user response
-```powershell
-curl http://localhost:8001/mcp/progress/demo_user
-```
-**Expected Response:**
-```json
-{
-  "user_id": "demo_user",
-  "skills_progress": {
-    "Python Programming": {
-      "lessons_completed": 2,
-      "quiz_scores": [85, 92],
-      "current_level": "intermediate"
-    }
-  },
-  "total_skills_learning": 1,
-  "mcp_server": "Agentic Skill Builder",
-  "timestamp": "2025-06-07T04:48:36.691517"
-}
-```
-
-> "Finally, let's submit a quiz answer to show the interactive capabilities..."
-
-**Action:** Show POST /mcp/quiz/submit with request body and response
-```powershell
-curl -X POST http://localhost:8001/mcp/quiz/submit `
-  -H "Content-Type: application/json" `
-  -d '{
-    "user_id": "demo_user",
-    "quiz_id": "quiz_python_001",
-    "answers": ["list", "dictionary", "tuple"],
-    "skill": "Python Programming"
-  }'
-```
-**Expected Response:**
-```json
-{
-  "quiz_id": "quiz_python_001",
-  "user_id": "demo_user",
-  "score": 85,
-  "feedback": "Great job! You correctly identified Python data structures.",
-  "passed": true,
-  "mcp_server": "Agentic Skill Builder"
-}
-```
-
----
-
-## 🎨 **Gradio Interface Demo (2:00 - 3:30)**
-
-### Part 1: Learning Flow (2:00 - 3:00)
-**Visual:** Gradio interface at localhost:7860
-**Narration:**
-> "Now let's see the beautiful Gradio interface in action. The same AI agents that power our MCP endpoints create this seamless learning experience."
-
-**Action:** 
-1. Select "Data Science" skill
-2. Click "Start Learning"
-3. Show generated lesson content
-4. Complete lesson and start quiz
-5. Answer quiz questions
-6. Show results and progress
-
-### Part 2: MCP Testing Interface (3:00 - 3:30)
-**Visual:** Built-in MCP endpoint testing section in Gradio
-**Narration:**
-> "What makes this special is that we've built MCP endpoint testing directly into our Gradio interface. You can test all our MCP endpoints without leaving the app."
-
-**Action:** Show the MCP endpoint testing interface working
-
----
-
-## 🏆 **Hackathon Highlights (3:30 - 4:30)**
-
-**Visual:** Split screen showing both Gradio UI and MCP endpoints
-**Narration:**
-> "This submission perfectly demonstrates the agentic architecture we've built:
-> - Three specialized AI agents working together
-> - Lesson Agent generates personalized content
-> - Quiz Agent creates adaptive assessments  
-> - Progress Agent tracks learning analytics
-> - All coordinated by our main orchestrator"
-
-**Visual:** Show the architecture diagram or code structure
-**Narration:**
-> "The same agents power both the beautiful user interface AND the MCP protocol endpoints, making our platform ready for integration with Claude Desktop, Cursor, or any MCP client."
-
----
-
-## 🚀 **Call to Action (4:30 - 5:00)**
-
-**Visual:** README.md showing deployment instructions
-**Narration:**
-> "You can deploy this immediately to Hugging Face Spaces using our space_app.py file, or run it locally following our comprehensive documentation. This represents the future of AI-powered education - agentic, interoperable, and ready for the Model Context Protocol ecosystem."
-
-**Visual:** Final shot of both servers running simultaneously
-**Narration:**
-> "Thank you for watching our Agentic Skill Builder demo. We're excited to contribute to the MCP ecosystem and the future of AI agents working together!"
-
----
-
-## 📝 **Recording Checklist**
-
-### Before Recording:
-- [ ] Ensure both servers are running (Gradio on 7860, MCP on 8001)
-- [ ] Prepare browser tabs for all endpoints
-- [ ] Have curl commands or Postman collection ready
-- [ ] Test the complete learning flow works
-- [ ] Check audio/screen recording quality
-
-### During Recording:
-- [ ] Speak clearly and at moderate pace
-- [ ] Show actual JSON responses from MCP endpoints
-- [ ] Demonstrate real-time lesson generation
-- [ ] Highlight the dual-purpose architecture
-- [ ] Keep within 5-minute time limit
-
-### After Recording:
-- [ ] Upload to YouTube/Vimeo
-- [ ] Update README.md with video link
-- [ ] Add video link to HF Spaces README
-- [ ] Verify video is publicly accessible
-
----
-
-## 🎥 **Technical Recording Tips**
-
-1. **Use OBS Studio or similar** for high-quality screen recording
-2. **Record at 1080p** for clear text visibility
-3. **Use good microphone** for clear narration
-4. **Multiple takes OK** - edit together the best parts
-5. **Add captions** for accessibility
-6. **Include timestamps** in video description
-
----
-
-**Ready to showcase the future of agentic learning with MCP integration! 🚀**

deployment_guide.md

@@ -1,138 +0,0 @@
-# 🚀 Hugging Face Spaces Deployment Guide
-
-## 📋 Pre-Deployment Checklist
-
-### ✅ **Files Ready for Upload:**
-- `space_app.py` - Main application file (Gradio + MCP server)
-- `requirements.txt` - HF Spaces optimized dependencies  
-- `README_spaces.md` - Spaces configuration (rename to README.md)
-- `config.py` - Configuration utilities
-- `.env` - Environment variables (handle separately)
-
-### ✅ **Configuration Complete:**
-- App file specified: `space_app.py`
-- SDK: `gradio 4.44.0`
-- Tags include: `mcp-server-track`
-- License: MIT
-
----
-
-## 🔧 **Deployment Steps**
-
-### **Step 1: Create Hugging Face Space**
-
-1. Go to [Hugging Face Spaces](https://huggingface.co/spaces)
-2. Click "Create new Space"
-3. **Important:** Create under the `Agents-MCP-Hackathon` organization
-4. Space name: `agentic-skill-builder`
-5. Choose "Gradio" as SDK
-6. Set to Public
-7. Click "Create Space"
-
-### **Step 2: Upload Files**
-
-Upload these files to your new Space:
-
-```
-📁 agentic-skill-builder/
-├── 📄 README.md (renamed from README_spaces.md)
-├── 📄 space_app.py
-├── 📄 requirements.txt
-├── 📄 config.py
-└── 📄 .env (create in Spaces settings)
-```
-
-### **Step 3: Configure Environment Variables**
-
-In your HF Space settings, add these environment variables:
-
-```env
-AZURE_OPENAI_ENDPOINT=your-azure-endpoint
-AZURE_OPENAI_KEY=your-api-key
-AZURE_OPENAI_API_VERSION=2024-12-01-preview
-AZURE_OPENAI_LLM_DEPLOYMENT=gpt-4.1
-AZURE_OPENAI_LLM_MODEL=gpt-4.1
-```
-
-**⚠️ Important:** Never commit your `.env` file with real credentials to a public repo!
-
-### **Step 4: Verify Deployment**
-
-Once deployed, your Space should:
-- ✅ Show the Gradio interface
-- ✅ Respond to MCP endpoints at `https://your-space-name.hf.space/mcp/skills`
-- ✅ Generate lessons and quizzes
-- ✅ Include hackathon branding and MCP testing interface
-
----
-
-## 🔍 **Testing Your Deployed Space**
-
-### **Gradio Interface Test:**
-1. Visit your Space URL
-2. Select a skill (e.g., "Python Programming")
-3. Complete the full learning flow
-4. Verify AI generates lessons and quizzes
-
-### **MCP Endpoints Test:**
-```bash
-# Test from anywhere on the internet
-curl https://your-space-name.hf.space/mcp/skills
-curl https://your-space-name.hf.space/mcp/progress/test_user
-```
-
----
-
-## 📝 **Post-Deployment Updates**
-
-### **Update README.md with:**
-1. **Live demo link:** Replace local URLs with your HF Space URL
-2. **Video link:** Add your demo video URL
-3. **Deployment status:** Confirm it's live and working
-
-### **Example Updates:**
-```markdown
-## 🎬 Demo Video
-**MCP Server in Action:** [https://youtu.be/your-video-id](https://youtu.be/your-video-id)
-
-## 🌐 Live Demo
-**Try it now:** [https://huggingface.co/spaces/Agents-MCP-Hackathon/agentic-skill-builder](https://huggingface.co/spaces/Agents-MCP-Hackathon/agentic-skill-builder)
-
-## 🔗 MCP Endpoints
-Test our live MCP server at: `https://your-space-name.hf.space/mcp/`
-```
-
----
-
-## 🏆 **Final Hackathon Submission Checklist**
-
-- [ ] ✅ Space deployed under Agents-MCP-Hackathon organization
-- [ ] ✅ "mcp-server-track" tag in README
-- [ ] ✅ Demo video uploaded and linked
-- [ ] ✅ Live MCP endpoints working
-- [ ] ✅ Gradio interface fully functional
-- [ ] ✅ All dependencies working in cloud environment
-- [ ] ✅ Documentation updated with live links
-
----
-
-## 🚨 **Troubleshooting Common Issues**
-
-### **Build Failures:**
-- Check `requirements.txt` for incompatible versions
-- Verify all imports work in `space_app.py`
-- Ensure environment variables are set correctly
-
-### **MCP Endpoints Not Working:**
-- Verify FastAPI routes are properly configured
-- Check if app.mount() is correctly set up
-- Test endpoints locally first
-
-### **Azure OpenAI Errors:**
-- Confirm environment variables are set in Spaces settings
-- Check Azure OpenAI quota and model availability
-- Verify API key permissions
-
----
-
-**Ready to deploy! Your agentic skill builder will be live for the world to see! 🌍✨**

mcp_server.py

@@ -1,5 +1,5 @@
 """
-MCP Server Integration for Agentic Skill Builder
+MCP Server Integration for SkillSprout
 This module provides Model Context Protocol endpoints for external agent integration.
 """
 
@@ -18,7 +18,7 @@ from app import AgenticSkillBuilder, UserProgress
 
 # FastAPI app for MCP endpoints
 mcp_app = FastAPI(
-    title="Agentic Skill Builder MCP Server",
+    title="SkillSprout MCP Server",
     description="Model Context Protocol endpoints for microlearning integration",
     version="1.0.0"
 )
@@ -50,7 +50,7 @@ class ProgressResponse(BaseModel):
 async def root():
     """Root endpoint with API information"""
     return {
-        "name": "Agentic Skill Builder MCP Server",
+        "name": "SkillSprout MCP Server",
         "version": "1.0.0",
         "description": "MCP endpoints for AI-powered microlearning",
         "endpoints": {
@@ -249,7 +249,7 @@ async def health_check():
     return {
         "status": "healthy",
         "timestamp": datetime.now().isoformat(),
-        "service": "Agentic Skill Builder MCP Server"
+        "service": "SkillSprout MCP Server"
     }
 
 def run_mcp_server():
@@ -263,7 +263,7 @@ def run_mcp_server():
     )
 
 if __name__ == "__main__":
-    print("🚀 Starting Agentic Skill Builder MCP Server...")
+    print("🚀 Starting SkillSprout MCP Server...")
     print("📚 MCP endpoints will be available at http://localhost:8000")
     print("📖 API documentation at http://localhost:8000/docs")
     run_mcp_server()

requirements.txt

@@ -7,3 +7,17 @@ fastapi>=0.104.0
 pydantic>=2.0.0
 aiohttp>=3.9.0
 requests>=2.31.0
+azure-cognitiveservices-speech>=1.34.0
+plotly>=5.17.0
+kaleido>=0.2.1
+
+# Testing dependencies
+pytest>=7.4.0
+pytest-asyncio>=0.23.0
+pytest-cov>=4.1.0
+pytest-mock>=3.12.0
+pytest-html>=4.1.0
+pytest-json-report>=1.5.0
+pytest-xdist>=3.3.0
+coverage>=7.3.0
+mock>=5.1.0

run.py

@@ -1,5 +1,5 @@
 """
-Launcher script for Agentic Skill Builder
+Launcher script for SkillSprout
 Runs both the Gradio interface and MCP server
 """
 
@@ -23,7 +23,7 @@ def run_mcp_server():
 def main():
     """Main launcher function"""
     print("=" * 60)
-    print("🎓 AGENTIC SKILL BUILDER")
+    print("🌱 SKILLSPROUT")
     print("   AI-Powered Microlearning Platform")
     print("=" * 60)
     print()
@@ -76,7 +76,7 @@ if __name__ == "__main__":
     try:
         main()
     except KeyboardInterrupt:
-        print("\n\n👋 Goodbye! Thanks for using Agentic Skill Builder!")
+        print("\n\n👋 Goodbye! Thanks for using SkillSprout!")
     except Exception as e:
         print(f"\n❌ Error: {e}")
         print("Please check your configuration and try again.")

simple_test.py

@@ -0,0 +1,32 @@
+#!/usr/bin/env python3
+"""
+Simple test runner for SkillSprout - streamlined for hackathon
+"""
+import subprocess
+import sys
+
+def run_tests():
+    """Run the simplified test suite"""
+    print("🌱 SkillSprout - Running Tests")
+    print("=" * 40)
+    
+    # Run the tests we actually have
+    cmd = [sys.executable, "-m", "pytest", "tests/", "-v", "--tb=short"]
+    
+    try:
+        result = subprocess.run(cmd, check=False)
+        
+        if result.returncode == 0:
+            print("\n✅ All tests passed!")
+        else:
+            print("\n⚠️  Some tests failed (this is OK for development)")
+        
+        return result.returncode
+        
+    except Exception as e:
+        print(f"\n❌ Error running tests: {e}")
+        return 1
+
+if __name__ == "__main__":
+    exit_code = run_tests()
+    sys.exit(exit_code)

space_app.py

@@ -1,5 +1,5 @@
 """
-Agentic Skill Builder - Hackathon Submission
+SkillSprout - Hackathon Submission
 A unified app.py that serves both Gradio interface and MCP server endpoints
 for the Gradio Agents & MCP Hackathon 2025
 """
@@ -9,10 +9,13 @@ import json
 import asyncio
 import threading
 import time
-from datetime import datetime
-from typing import Dict, List, Optional, Tuple
-from dataclasses import dataclass, asdict
+from datetime import datetime, timedelta
+from typing import Dict, List, Optional, Tuple, Any
+from dataclasses import dataclass, asdict, field
 import logging
+import math
+import base64
+from io import BytesIO
 
 from dotenv import load_dotenv
 import gradio as gr
@@ -22,11 +25,19 @@ from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 import uvicorn
 
+# Voice narration imports
+try:
+    import azure.cognitiveservices.speech as speechsdk
+    SPEECH_SDK_AVAILABLE = True
+except ImportError:
+    SPEECH_SDK_AVAILABLE = False
+    print("⚠️ Azure Speech SDK not available. Voice narration will be disabled.")
+
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Load environment variables
+# Load environment variables (works locally with .env, in Spaces with secrets)
 load_dotenv()
 
 # Azure OpenAI client configuration
@@ -40,20 +51,186 @@ client = AzureOpenAI(
 LLM_DEPLOYMENT = os.getenv("AZURE_OPENAI_LLM_DEPLOYMENT", "gpt-4").replace('"', '')
 LLM_MODEL = os.getenv("AZURE_OPENAI_LLM_MODEL", "gpt-4").replace('"', '')
 
+# Voice configuration
+VOICE_KEY = os.getenv("AZURE_SPEECH_KEY", "").replace('"', '')
+VOICE_REGION = os.getenv("AZURE_SPEECH_REGION", "eastus2").replace('"', '')
+VOICE_NAME = os.getenv("AZURE_SPEECH_VOICE", "en-US-AvaMultilingualNeural").replace('"', '')
+
 # Import all classes from the main app
 from app import (
     UserProgress, Lesson, Quiz, LessonAgent, QuizAgent, 
     ProgressAgent, AgenticSkillBuilder
 )
 
+# Gamification System Classes
+@dataclass
+class Achievement:
+    """Achievement system for gamification"""
+    id: str
+    name: str
+    description: str
+    icon: str
+    unlocked: bool = False
+    unlock_condition: str = ""
+
+@dataclass
+class UserStats:
+    """Enhanced user statistics for gamification"""
+    user_id: str
+    total_points: int = 0
+    level: int = 1
+    achievements: List[str] = field(default_factory=list)
+    streak_days: int = 0
+    total_lessons: int = 0
+    total_quizzes: int = 0
+    correct_answers: int = 0
+    
+    def add_points(self, points: int):
+        """Add points and check for level up"""
+        self.total_points += points
+        new_level = min(10, (self.total_points // 100) + 1)
+        if new_level > self.level:
+            self.level = new_level
+    
+    def get_accuracy(self) -> float:
+        """Calculate quiz accuracy"""
+        if self.total_quizzes == 0:
+            return 0.0
+        return (self.correct_answers / self.total_quizzes) * 100
+
+@dataclass 
+class EnhancedUserProgress:
+    """Enhanced progress tracking with detailed analytics"""
+    user_id: str
+    skill: str
+    lessons_completed: int = 0
+    quiz_scores: List[float] = field(default_factory=list)
+    time_spent: List[float] = field(default_factory=list)
+    mastery_level: float = 0.0
+    last_activity: datetime = field(default_factory=datetime.now)
+    
+    def calculate_mastery(self) -> float:
+        """Calculate skill mastery based on performance"""
+        if not self.quiz_scores:
+            return 0.0
+        
+        avg_score = sum(self.quiz_scores) / len(self.quiz_scores)
+        consistency_bonus = min(len(self.quiz_scores) * 5, 20)  # Max 20% bonus
+        lesson_bonus = min(self.lessons_completed * 2, 10)  # Max 10% bonus
+        
+        self.mastery_level = min(100, avg_score + consistency_bonus + lesson_bonus)
+        return self.mastery_level
+    
+    def update_mastery(self):
+        """Update mastery level"""
+        self.calculate_mastery()
+
+class GamificationManager:
+    """Manages achievements and gamification"""
+    
+    def __init__(self):
+        self.user_stats: Dict[str, UserStats] = {}
+        self.achievements = {
+            "first_steps": Achievement("first_steps", "First Steps", "Complete your first lesson", "🎯"),
+            "quiz_master": Achievement("quiz_master", "Quiz Master", "Score 100% on a quiz", "🧠"),
+            "persistent": Achievement("persistent", "Persistent Learner", "Complete 5 lessons", "💪"),
+            "scholar": Achievement("scholar", "Scholar", "Complete 10 lessons", "🎓"),
+            "expert": Achievement("expert", "Domain Expert", "Master a skill (20 lessons)", "⭐"),
+            "polyglot": Achievement("polyglot", "Polyglot", "Learn 3 different skills", "🌍"),
+            "perfectionist": Achievement("perfectionist", "Perfectionist", "Score 100% on 5 quizzes", "💯"),
+            "speed": Achievement("speed", "Speed Learner", "Complete lesson in under 3 minutes", "⚡"),
+            "consistent": Achievement("consistent", "Consistent", "Learn for 7 days in a row", "📅"),
+            "explorer": Achievement("explorer", "Explorer", "Try voice narration feature", "🎧"),
+        }
+    
+    def get_user_stats(self, user_id: str) -> UserStats:
+        """Get or create user stats"""
+        if user_id not in self.user_stats:
+            self.user_stats[user_id] = UserStats(user_id=user_id)
+        return self.user_stats[user_id]
+    
+    def check_achievements(self, user_id: str, progress: EnhancedUserProgress) -> List[Achievement]:
+        """Check and unlock achievements"""
+        stats = self.get_user_stats(user_id)
+        newly_unlocked = []
+        
+        # Check each achievement
+        achievements_to_check = [
+            ("first_steps", stats.total_lessons >= 1),
+            ("quiz_master", any(score == 100 for score in progress.quiz_scores)),
+            ("persistent", stats.total_lessons >= 5),
+            ("scholar", stats.total_lessons >= 10),
+            ("expert", stats.total_lessons >= 20),
+            ("perfectionist", sum(1 for score in progress.quiz_scores if score == 100) >= 5),
+            ("consistent", stats.streak_days >= 7),
+        ]
+        
+        for achievement_id, condition in achievements_to_check:
+            if condition and achievement_id not in stats.achievements:
+                stats.achievements.append(achievement_id)
+                newly_unlocked.append(self.achievements[achievement_id])
+                stats.add_points(50)  # Bonus points for achievements
+        
+        return newly_unlocked
+
 # Create global instances
 app_instance = AgenticSkillBuilder()
+gamification = GamificationManager()
+
+def generate_voice_narration(text: str, voice_name: str = VOICE_NAME) -> Optional[str]:
+    """Generate voice narration using Azure Speech Services"""
+    if not SPEECH_SDK_AVAILABLE or not VOICE_KEY:
+        logger.warning("Voice narration not available - missing Speech SDK or API key")
+        return None
+    
+    try:
+        # Configure speech service
+        speech_config = speechsdk.SpeechConfig(subscription=VOICE_KEY, region=VOICE_REGION)
+        speech_config.speech_synthesis_voice_name = voice_name
+        
+        # Generate filename
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        audio_filename = f"narration_{timestamp}.wav"
+        
+        # Configure audio output
+        audio_config = speechsdk.audio.AudioOutputConfig(filename=audio_filename)
+        
+        # Create synthesizer
+        speech_synthesizer = speechsdk.SpeechSynthesizer(
+            speech_config=speech_config,
+            audio_config=audio_config
+        )
+        
+        # Create SSML for educational content
+        ssml_text = f"""
+        <speak version="1.0" xmlns="http://www.w3.org/2001/10/synthesis" xml:lang="en-US">
+            <voice name="{voice_name}">
+                <prosody rate="0.9" pitch="medium">
+                    {text}
+                </prosody>
+            </voice>
+        </speak>
+        """
+        
+        # Synthesize speech
+        result = speech_synthesizer.speak_ssml_async(ssml_text).get()
+        
+        if result.reason == speechsdk.ResultReason.SynthesizingAudioCompleted:
+            logger.info(f"Voice narration generated: {audio_filename}")
+            return audio_filename
+        else:
+            logger.error(f"Speech synthesis failed: {result.reason}")
+            return None
+            
+    except Exception as e:
+        logger.error(f"Error generating voice narration: {e}")
+        return None
 
 # ===== MCP SERVER INTEGRATION =====
 
 # FastAPI app for MCP endpoints
 mcp_app = FastAPI(
-    title="Agentic Skill Builder MCP Server",
+    title="SkillSprout MCP Server",
     description="Model Context Protocol endpoints for microlearning integration - Hackathon 2025",
     version="1.0.0"
 )
@@ -74,7 +251,7 @@ class QuizSubmission(BaseModel):
 async def root():
     """Root endpoint with hackathon information"""
     return {
-        "name": "Agentic Skill Builder MCP Server",
+        "name": "SkillSprout MCP Server",
         "version": "1.0.0",
         "hackathon": "Gradio Agents & MCP Hackathon 2025",
         "track": "mcp-server-track",
@@ -121,9 +298,8 @@ async def generate_lesson_mcp(request: LessonRequest):
             "user_context": {
                 "user_id": request.user_id,
                 "current_difficulty": progress.current_difficulty,
-                "lessons_completed": progress.lessons_completed
-            },
-            "mcp_server": "Agentic Skill Builder",
+                "lessons_completed": progress.lessons_completed            },
+            "mcp_server": "SkillSprout",
             "timestamp": datetime.now().isoformat()
         }
         
@@ -142,10 +318,9 @@ async def get_user_progress_mcp(user_id: str, skill: str = None):
                 "user_id": progress.user_id,
                 "skill": progress.skill,
                 "lessons_completed": progress.lessons_completed,
-                "average_score": progress.get_average_score(),
-                "current_difficulty": progress.current_difficulty,
+                "average_score": progress.get_average_score(),                "current_difficulty": progress.current_difficulty,
                 "recommendations": recommendation,
-                "mcp_server": "Agentic Skill Builder"
+                "mcp_server": "SkillSprout"
             }
         else:
             user_progress_data = {}
@@ -161,9 +336,8 @@ async def get_user_progress_mcp(user_id: str, skill: str = None):
             
             return {
                 "user_id": user_id,
-                "skills_progress": user_progress_data,
-                "total_skills_learning": len(user_progress_data),
-                "mcp_server": "Agentic Skill Builder",
+                "skills_progress": user_progress_data,                "total_skills_learning": len(user_progress_data),
+                "mcp_server": "SkillSprout",
                 "timestamp": datetime.now().isoformat()
             }
             
@@ -210,10 +384,9 @@ async def submit_quiz_results_mcp(submission: QuizSubmission):
             "updated_progress": {
                 "lessons_completed": progress.lessons_completed,
                 "average_score": progress.get_average_score(),
-                "current_difficulty": progress.current_difficulty
-            },
+                "current_difficulty": progress.current_difficulty            },
             "recommendation": recommendation,
-            "mcp_server": "Agentic Skill Builder",
+            "mcp_server": "SkillSprout",
             "timestamp": datetime.now().isoformat()
         }
         
@@ -226,7 +399,7 @@ def create_interface():
     """Create the Gradio interface with enhanced hackathon features"""
     
     with gr.Blocks(
-        title="Agentic Skill Builder - MCP Hackathon 2025",
+        title="SkillSprout - MCP Hackathon 2025",
         theme=gr.themes.Soft(),
         css="""
         .gradio-container {
@@ -246,7 +419,7 @@ def create_interface():
         # Enhanced Header for Hackathon
         gr.HTML("""
         <div class="hackathon-header">
-            <h1>🚀 Agentic Skill Builder</h1>
+            <h1>🌱 SkillSprout</h1>
             <h3>AI-Powered Microlearning with MCP Integration</h3>
             <p><strong>🏆 Gradio Agents & MCP Hackathon 2025 Submission</strong></p>
             <p>Track: MCP Server/Tool • Demonstrating Agentic AI Workflows</p>
@@ -276,9 +449,14 @@ def create_interface():
                     )
                     
                     start_btn = gr.Button("🚀 Start Learning", variant="primary", size="lg")
-            
-            # Learning content areas
+              # Learning content areas
             lesson_output = gr.Markdown(visible=False)
+            
+            # Voice narration controls
+            with gr.Row(visible=False) as voice_controls:
+                voice_btn = gr.Button("🎧 Generate Voice Narration", variant="secondary")
+                voice_audio = gr.Audio(label="Lesson Audio", visible=False)
+            
             lesson_btn = gr.Button("Complete Lesson", visible=False)
             
             quiz_output = gr.Markdown(visible=False)
@@ -345,71 +523,142 @@ def create_interface():
                 
                 with gr.Column():
                     mcp_output = gr.JSON(label="MCP Server Response")
-        
-        # Event handlers (same as original app.py)
-        async def handle_start_learning(skill_choice, custom_skill_input):
+          # Event handlers with gamification integration
+        def handle_start_learning(skill_choice, custom_skill_input, user_id="default"):
+            """Enhanced learning session handler with gamification"""
             skill = custom_skill_input.strip() if custom_skill_input.strip() else skill_choice
-            if not skill:
-                return [
+            if not skill:                return [
                     gr.update(value="⚠️ Please select or enter a skill to continue."),
+                    gr.update(visible=False),  # voice_controls
                     gr.update(visible=False),
                     gr.update(visible=False),
                     skill
                 ] + [gr.update(visible=False, value="") for _ in range(5)]
             
-            lesson_content, btn_text, _ = await app_instance.start_lesson(skill)
-            
-            return [
-                gr.update(value=lesson_content),
-                gr.update(value=btn_text, visible=True),
-                gr.update(visible=False),
-                skill
-            ] + [gr.update(visible=False, value="") for _ in range(5)]
-        
-        async def handle_complete_lesson():
-            quiz_content, btn_text, _ = await app_instance.complete_lesson_and_start_quiz()
-            
-            quiz_updates = []
-            if app_instance.current_quiz:
-                for i, question in enumerate(app_instance.current_quiz.questions):
-                    if i < len(quiz_inputs):
-                        label = f"Q{i+1}: {question['question'][:50]}..."
-                        quiz_updates.append(gr.update(label=label, visible=True))
-                    else:
+            try:
+                # Start lesson using the app instance (sync call)
+                loop = asyncio.new_event_loop()
+                asyncio.set_event_loop(loop)
+                lesson_content, btn_text, _ = loop.run_until_complete(app_instance.start_lesson(skill))
+                app_instance.current_user = user_id
+                
+                # Update user stats
+                stats = gamification.get_user_stats(user_id)
+                stats.total_lessons += 1
+                stats.add_points(10)  # Points for starting lesson
+                
+                # Check for achievements
+                progress = EnhancedUserProgress(user_id=user_id, skill=skill)
+                progress.lessons_completed = stats.total_lessons
+                newly_unlocked = gamification.check_achievements(user_id, progress)
+                return [
+                    gr.update(value=lesson_content),
+                    gr.update(visible=True),  # voice_controls
+                    gr.update(value=btn_text, visible=True),
+                    gr.update(visible=False),
+                    skill
+                ] + [gr.update(visible=False, value="") for _ in range(5)]
+                
+            except Exception as e:
+                logger.error(f"Error starting lesson: {e}")                
+                return [
+                    gr.update(value=f"❌ Error starting lesson: {str(e)}"),
+                    gr.update(visible=False),  # voice_controls
+                    gr.update(visible=False),
+                    gr.update(visible=False),
+                    skill
+                ] + [gr.update(visible=False, value="") for _ in range(5)]
+
+        def handle_complete_lesson(user_id="default"):
+            """Handle lesson completion and start quiz with gamification"""
+            try:
+                # Complete lesson and generate quiz (sync call)
+                loop = asyncio.new_event_loop()
+                asyncio.set_event_loop(loop)
+                quiz_content, btn_text, _ = loop.run_until_complete(app_instance.complete_lesson_and_start_quiz())
+                
+                # Update user stats - lesson completed
+                stats = gamification.get_user_stats(user_id)
+                stats.add_points(20)  # Points for completing lesson
+                
+                quiz_updates = []
+                if app_instance.current_quiz:
+                    for i, question in enumerate(app_instance.current_quiz.questions):
+                        if i < len(quiz_inputs):
+                            label = f"Q{i+1}: {question['question'][:50]}..."
+                            quiz_updates.append(gr.update(label=label, visible=True))
+                        else:
+                            quiz_updates.append(gr.update(visible=False))
+                    for i in range(len(app_instance.current_quiz.questions), len(quiz_inputs)):
                         quiz_updates.append(gr.update(visible=False))
-                for i in range(len(app_instance.current_quiz.questions), len(quiz_inputs)):
-                    quiz_updates.append(gr.update(visible=False))
-            else:
-                quiz_updates = [gr.update(visible=False) for _ in range(len(quiz_inputs))]
-            
-            return [
-                gr.update(visible=False),
-                gr.update(value=quiz_content, visible=True),
-                gr.update(value=btn_text, visible=True),
-                gr.update(visible=False)
-            ] + quiz_updates
-        
-        def handle_submit_quiz(*answers):
-            valid_answers = [ans for ans in answers if ans is not None and ans != ""]
-            results_content, btn_text, _ = app_instance.submit_quiz(*valid_answers)
-            
-            return [
-                gr.update(visible=False),
-                gr.update(value=results_content, visible=True),
-                gr.update(value=btn_text, visible=True),
-                gr.update(visible=False)
-            ] + [gr.update(visible=False) for _ in range(len(quiz_inputs))]
-        
+                else:
+                    quiz_updates = [gr.update(visible=False) for _ in range(len(quiz_inputs))]
+                
+                return [
+                    gr.update(visible=False),
+                    gr.update(value=quiz_content, visible=True),
+                    gr.update(value=btn_text, visible=True),
+                    gr.update(visible=False)
+                ] + quiz_updates
+            except Exception as e:
+                logger.error(f"Error completing lesson: {e}")
+                return [
+                    gr.update(visible=False),
+                    gr.update(value=f"❌ Error completing lesson: {str(e)}", visible=True),
+                    gr.update(visible=False),
+                    gr.update(visible=False)
+                ] + [gr.update(visible=False) for _ in range(len(quiz_inputs))]
+
+        def handle_submit_quiz(*answers, user_id="default"):
+            """Handle quiz submission with gamification"""
+            try:
+                valid_answers = [ans for ans in answers if ans is not None and ans != ""]
+                results_content, btn_text, _ = app_instance.submit_quiz(*valid_answers)
+                
+                # Update user stats for quiz completion
+                stats = gamification.get_user_stats(user_id)
+                stats.total_quizzes += 1
+                
+                # Calculate quiz score and update stats
+                if app_instance.current_quiz and app_instance.current_quiz.questions:
+                    total_questions = len(app_instance.current_quiz.questions)
+                    # Simple scoring: assume each correct answer is worth points
+                    score_points = len(valid_answers) * 20  # Base points per answer
+                    stats.add_points(score_points)
+                    
+                    # Check if perfect score (simplified check)
+                    if "100%" in results_content or "Perfect" in results_content:
+                        stats.correct_answers += total_questions
+                        stats.add_points(50)  # Bonus for perfect score
+                    else:
+                        # Estimate correct answers based on content (simplified)
+                        stats.correct_answers += max(1, len(valid_answers) // 2)
+                
+                return [
+                    gr.update(visible=False),
+                    gr.update(value=results_content, visible=True),
+                    gr.update(value=btn_text, visible=True),
+                    gr.update(visible=False)
+                ] + [gr.update(visible=False) for _ in range(len(quiz_inputs))]
+                
+            except Exception as e:
+                logger.error(f"Error submitting quiz: {e}")
+                return [
+                    gr.update(visible=False),
+                    gr.update(value=f"❌ Error submitting quiz: {str(e)}", visible=True),
+                    gr.update(visible=False),
+                    gr.update(visible=False)
+                ] + [gr.update(visible=False) for _ in range(len(quiz_inputs))]
         def handle_restart():
             return [
                 gr.update(visible=False),
+                gr.update(visible=False),  # voice_controls
                 gr.update(visible=False),
                 gr.update(visible=False),
                 gr.update(visible=False),
                 gr.update(visible=False),
                 ""
             ] + [gr.update(visible=False, value="") for _ in range(len(quiz_inputs))]
-        
         def update_progress_display():
             if not app_instance.progress_agent.user_data:
                 return "**No learning data yet.** Complete some lessons to see your progress!"
@@ -426,6 +675,40 @@ def create_interface():
                 """
             return progress_content
         
+        def handle_voice_generation(lesson_content, user_id="default"):
+            """Generate voice narration for lesson content"""
+            if not lesson_content or lesson_content == "":
+                return gr.update(value=None, visible=False), "❌ No lesson content to narrate"
+            
+            try:
+                # Extract text content from markdown
+                import re
+                # Remove markdown formatting for better speech
+                text_content = re.sub(r'[#*`]', '', lesson_content)
+                text_content = text_content.replace('\n', ' ').strip()
+                
+                # Limit text length for better narration
+                if len(text_content) > 1000:
+                    text_content = text_content[:1000] + "..."
+                
+                # Generate voice narration
+                audio_file = generate_voice_narration(text_content)
+                
+                if audio_file:
+                    # Award achievement for using voice feature
+                    stats = gamification.get_user_stats(user_id)
+                    if "explorer" not in stats.achievements:
+                        stats.achievements.append("explorer")
+                        stats.add_points(25)
+                    
+                    return gr.update(value=audio_file, visible=True), "🎧 Voice narration generated!"
+                else:
+                    return gr.update(value=None, visible=False), "❌ Voice narration not available"
+                    
+            except Exception as e:
+                logger.error(f"Error generating voice: {e}")
+                return gr.update(value=None, visible=False), f"❌ Error: {str(e)}"
+        
         async def test_mcp_endpoint(skill, user_id):
             """Test MCP endpoint directly from the interface"""
             try:
@@ -456,9 +739,8 @@ def create_interface():
                         "user_context": {
                             "user_id": user_id,
                             "current_difficulty": progress.current_difficulty,
-                            "lessons_completed": progress.lessons_completed
-                        },
-                        "mcp_server": "Agentic Skill Builder",
+                            "lessons_completed": progress.lessons_completed                        },
+                        "mcp_server": "SkillSprout",
                         "status": "success"
                     }
                 }
@@ -471,12 +753,17 @@ def create_interface():
                     "error": str(e),
                     "status": "error"
                 }
-        
-        # Wire up events
+          # Wire up events
         start_btn.click(
             handle_start_learning,
             inputs=[skill_dropdown, custom_skill],
-            outputs=[lesson_output, lesson_btn, quiz_output, current_skill] + quiz_inputs[:5]
+            outputs=[lesson_output, voice_controls, lesson_btn, quiz_output, current_skill] + quiz_inputs[:5]
+        )
+        
+        voice_btn.click(
+            handle_voice_generation,
+            inputs=[lesson_output],
+            outputs=[voice_audio, lesson_output]
         )
         
         lesson_btn.click(
@@ -489,10 +776,9 @@ def create_interface():
             inputs=quiz_inputs,
             outputs=[quiz_submit_btn, results_output, restart_btn, quiz_output] + quiz_inputs
         )
-        
         restart_btn.click(
             handle_restart,
-            outputs=[lesson_output, quiz_output, results_output, lesson_btn, restart_btn, current_skill] + quiz_inputs
+            outputs=[lesson_output, voice_controls, quiz_output, results_output, lesson_btn, restart_btn, current_skill] + quiz_inputs
         )
         
         refresh_progress_btn.click(

test_env.py

@@ -0,0 +1,42 @@
+"""
+Quick test to verify environment variables are loaded correctly
+"""
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+def test_env_vars():
+    """Test if required environment variables are available"""
+    required_vars = [
+        "AZURE_OPENAI_ENDPOINT",
+        "AZURE_OPENAI_KEY", 
+        "AZURE_OPENAI_API_VERSION",
+        "AZURE_OPENAI_LLM_DEPLOYMENT",
+        "AZURE_OPENAI_LLM_MODEL"
+    ]
+    
+    print("🔍 Environment Variables Check")
+    print("=" * 40)
+    
+    all_present = True
+    for var in required_vars:
+        value = os.getenv(var)
+        if value and value.strip():
+            print(f"✅ {var}: Present")
+        else:
+            print(f"❌ {var}: Missing or empty")
+            all_present = False
+    
+    print("=" * 40)
+    if all_present:
+        print("🎉 All environment variables are properly configured!")
+        print("✅ Your app should work in Hugging Face Spaces")
+    else:
+        print("⚠️  Some environment variables are missing")
+        print("🔧 Set them in Hugging Face Spaces Repository Secrets")
+    
+    return all_present
+
+if __name__ == "__main__":
+    test_env_vars()

tests/conftest.py

@@ -0,0 +1,34 @@
+"""
+Simple pytest configuration for SkillSprout tests
+"""
+import pytest
+import os
+import sys
+
+# Add parent directory to path
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+@pytest.fixture
+def enhanced_user_progress():
+    """Enhanced user progress for testing"""
+    from space_app import EnhancedUserProgress
+    return EnhancedUserProgress(
+        user_id="test_user",
+        skill="Python Programming",
+        lessons_completed=3,
+        quiz_scores=[80.0, 90.0, 70.0],
+        mastery_level=75.0
+    )
+
+@pytest.fixture
+def gamification_manager():
+    """Gamification manager instance for testing"""
+    from space_app import GamificationManager
+    return GamificationManager()
+
+# Pytest configuration
+def pytest_configure(config):
+    """Configure pytest settings"""
+    config.addinivalue_line("markers", "unit: marks tests as unit tests")
+    config.addinivalue_line("markers", "integration: marks tests as integration tests")
+    config.addinivalue_line("markers", "slow: marks tests as slow")

tests/test_environment.py

@@ -0,0 +1,505 @@
+"""
+Enhanced Environment Validation Tests for SkillSprout
+"""
+import pytest
+import os
+import tempfile
+import json
+from unittest.mock import patch, Mock
+from dataclasses import dataclass
+from typing import Dict, List, Optional
+
+
+@dataclass
+class EnvironmentCheck:
+    """Data class for environment validation results"""
+    name: str
+    required: bool
+    present: bool
+    value: Optional[str] = None
+    validation_result: bool = True
+    error_message: Optional[str] = None
+
+
+class EnvironmentValidator:
+    """Enhanced environment validation utility"""
+    
+    REQUIRED_VARS = {
+        'AZURE_OPENAI_ENDPOINT': {
+            'required': True,
+            'pattern': r'https://.*\.openai\.azure\.com/',
+            'description': 'Azure OpenAI endpoint URL'
+        },
+        'AZURE_OPENAI_KEY': {
+            'required': True,
+            'min_length': 20,
+            'description': 'Azure OpenAI API key'
+        },
+        'AZURE_OPENAI_API_VERSION': {
+            'required': True,
+            'pattern': r'\d{4}-\d{2}-\d{2}(-preview)?',
+            'description': 'Azure OpenAI API version'
+        },
+        'AZURE_OPENAI_LLM_DEPLOYMENT': {
+            'required': True,
+            'description': 'Azure OpenAI LLM deployment name'
+        },
+        'AZURE_OPENAI_LLM_MODEL': {
+            'required': True,
+            'description': 'Azure OpenAI LLM model name'
+        }
+    }
+    
+    OPTIONAL_VARS = {
+        'AZURE_SPEECH_KEY': {
+            'required': False,
+            'min_length': 20,
+            'description': 'Azure Speech Services key (for voice narration)'
+        },
+        'AZURE_SPEECH_REGION': {
+            'required': False,
+            'description': 'Azure Speech Services region'
+        },
+        'GRADIO_ANALYTICS_ENABLED': {
+            'required': False,
+            'valid_values': ['true', 'false', '1', '0'],
+            'description': 'Enable/disable Gradio analytics'
+        },
+        'PYTHONPATH': {
+            'required': False,
+            'description': 'Python path for module resolution'
+        }
+    }
+    
+    def validate_all(self) -> List[EnvironmentCheck]:
+        """Validate all environment variables"""
+        results = []
+        
+        # Check required variables
+        for var_name, config in self.REQUIRED_VARS.items():
+            result = self._validate_variable(var_name, config, required=True)
+            results.append(result)
+        
+        # Check optional variables
+        for var_name, config in self.OPTIONAL_VARS.items():
+            result = self._validate_variable(var_name, config, required=False)
+            results.append(result)
+        
+        return results
+    
+    def _validate_variable(self, var_name: str, config: Dict, required: bool) -> EnvironmentCheck:
+        """Validate a single environment variable"""
+        import re
+        
+        value = os.getenv(var_name)
+        present = value is not None
+        validation_result = True
+        error_message = None
+        
+        if required and not present:
+            validation_result = False
+            error_message = f"Required environment variable {var_name} is not set"
+        elif present:
+            # Validate pattern if specified
+            if 'pattern' in config:
+                if not re.match(config['pattern'], value):
+                    validation_result = False
+                    error_message = f"{var_name} does not match expected pattern"
+            
+            # Validate minimum length if specified
+            if 'min_length' in config:
+                if len(value) < config['min_length']:
+                    validation_result = False
+                    error_message = f"{var_name} is too short (minimum {config['min_length']} characters)"
+            
+            # Validate against allowed values if specified
+            if 'valid_values' in config:
+                if value.lower() not in config['valid_values']:
+                    validation_result = False
+                    error_message = f"{var_name} must be one of: {config['valid_values']}"
+        
+        return EnvironmentCheck(
+            name=var_name,
+            required=required,
+            present=present,
+            value=value if present else None,
+            validation_result=validation_result,
+            error_message=error_message
+        )
+    
+    def get_validation_report(self) -> Dict:
+        """Get a comprehensive validation report"""
+        results = self.validate_all()
+        
+        report = {
+            'overall_status': 'PASS',
+            'total_checks': len(results),
+            'passed': 0,
+            'failed': 0,
+            'warnings': 0,
+            'details': [],
+            'errors': [],
+            'warnings_list': []
+        }
+        
+        for result in results:
+            detail = {
+                'name': result.name,
+                'required': result.required,
+                'present': result.present,
+                'status': 'PASS' if result.validation_result else 'FAIL',
+                'value_length': len(result.value) if result.value else 0,
+                'description': self._get_description(result.name)
+            }
+            
+            if result.error_message:
+                detail['error'] = result.error_message
+            
+            report['details'].append(detail)
+            
+            if result.validation_result:
+                report['passed'] += 1
+            else:
+                report['failed'] += 1
+                if result.required:
+                    report['overall_status'] = 'FAIL'
+                    report['errors'].append(result.error_message)
+                else:
+                    report['warnings'] += 1
+                    report['warnings_list'].append(result.error_message)
+        
+        return report
+    
+    def _get_description(self, var_name: str) -> str:
+        """Get description for a variable"""
+        all_vars = {**self.REQUIRED_VARS, **self.OPTIONAL_VARS}
+        return all_vars.get(var_name, {}).get('description', 'No description available')
+
+
+@pytest.mark.unit
+class TestEnvironmentValidator:
+    """Test the environment validator utility"""
+    
+    def test_validator_initialization(self):
+        """Test environment validator creates correctly"""
+        validator = EnvironmentValidator()
+        assert hasattr(validator, 'REQUIRED_VARS')
+        assert hasattr(validator, 'OPTIONAL_VARS')
+        assert len(validator.REQUIRED_VARS) > 0
+    
+    def test_validate_required_variable_present(self):
+        """Test validation of present required variable"""
+        validator = EnvironmentValidator()
+        
+        with patch.dict(os.environ, {'AZURE_OPENAI_ENDPOINT': 'https://test.openai.azure.com/'}):
+            result = validator._validate_variable(
+                'AZURE_OPENAI_ENDPOINT',
+                validator.REQUIRED_VARS['AZURE_OPENAI_ENDPOINT'],
+                required=True
+            )
+            
+            assert result.name == 'AZURE_OPENAI_ENDPOINT'
+            assert result.required is True
+            assert result.present is True
+            assert result.validation_result is True
+            assert result.error_message is None
+    
+    def test_validate_required_variable_missing(self):
+        """Test validation of missing required variable"""
+        validator = EnvironmentValidator()
+        
+        with patch.dict(os.environ, {}, clear=True):
+            result = validator._validate_variable(
+                'AZURE_OPENAI_KEY',
+                validator.REQUIRED_VARS['AZURE_OPENAI_KEY'],
+                required=True
+            )
+            
+            assert result.name == 'AZURE_OPENAI_KEY'
+            assert result.required is True
+            assert result.present is False
+            assert result.validation_result is False
+            assert "Required environment variable" in result.error_message
+    
+    def test_validate_variable_pattern_match(self):
+        """Test variable validation with pattern matching"""
+        validator = EnvironmentValidator()
+        
+        # Valid pattern
+        with patch.dict(os.environ, {'AZURE_OPENAI_API_VERSION': '2024-12-01-preview'}):
+            result = validator._validate_variable(
+                'AZURE_OPENAI_API_VERSION',
+                validator.REQUIRED_VARS['AZURE_OPENAI_API_VERSION'],
+                required=True
+            )
+            
+            assert result.validation_result is True
+        
+        # Invalid pattern
+        with patch.dict(os.environ, {'AZURE_OPENAI_API_VERSION': 'invalid-version'}):
+            result = validator._validate_variable(
+                'AZURE_OPENAI_API_VERSION',
+                validator.REQUIRED_VARS['AZURE_OPENAI_API_VERSION'],
+                required=True
+            )
+            
+            assert result.validation_result is False
+            assert "does not match expected pattern" in result.error_message
+    
+    def test_validate_variable_min_length(self):
+        """Test variable validation with minimum length"""
+        validator = EnvironmentValidator()
+        
+        # Valid length
+        with patch.dict(os.environ, {'AZURE_OPENAI_KEY': 'a' * 25}):
+            result = validator._validate_variable(
+                'AZURE_OPENAI_KEY',
+                validator.REQUIRED_VARS['AZURE_OPENAI_KEY'],
+                required=True
+            )
+            
+            assert result.validation_result is True
+        
+        # Too short
+        with patch.dict(os.environ, {'AZURE_OPENAI_KEY': 'short'}):
+            result = validator._validate_variable(
+                'AZURE_OPENAI_KEY',
+                validator.REQUIRED_VARS['AZURE_OPENAI_KEY'],
+                required=True
+            )
+            
+            assert result.validation_result is False
+            assert "is too short" in result.error_message
+
+
+@pytest.mark.integration
+class TestEnvironmentValidation:
+    """Integration tests for environment validation"""
+    
+    def test_complete_validation_all_present(self):
+        """Test complete validation with all variables present"""
+        validator = EnvironmentValidator()
+        
+        complete_env = {
+            'AZURE_OPENAI_ENDPOINT': 'https://test.openai.azure.com/',
+            'AZURE_OPENAI_KEY': 'a' * 30,
+            'AZURE_OPENAI_API_VERSION': '2024-12-01-preview',
+            'AZURE_OPENAI_LLM_DEPLOYMENT': 'gpt-4',
+            'AZURE_OPENAI_LLM_MODEL': 'gpt-4',
+            'AZURE_SPEECH_KEY': 'b' * 25,
+            'AZURE_SPEECH_REGION': 'eastus'
+        }
+        
+        with patch.dict(os.environ, complete_env):
+            results = validator.validate_all()
+            
+            # Should have results for all defined variables
+            assert len(results) >= 5  # At least the required ones
+            
+            # All required variables should pass
+            required_results = [r for r in results if r.required]
+            assert all(r.validation_result for r in required_results)
+    
+    def test_validation_report_generation(self):
+        """Test generation of validation report"""
+        validator = EnvironmentValidator()
+        
+        test_env = {
+            'AZURE_OPENAI_ENDPOINT': 'https://test.openai.azure.com/',
+            'AZURE_OPENAI_KEY': 'a' * 30,
+            'AZURE_OPENAI_API_VERSION': '2024-12-01-preview',
+            'AZURE_OPENAI_LLM_DEPLOYMENT': 'gpt-4',
+            'AZURE_OPENAI_LLM_MODEL': 'gpt-4'
+        }
+        
+        with patch.dict(os.environ, test_env):
+            report = validator.get_validation_report()
+            
+            assert 'overall_status' in report
+            assert 'total_checks' in report
+            assert 'passed' in report
+            assert 'failed' in report
+            assert 'details' in report
+            
+            # Should pass with all required vars present
+            assert report['overall_status'] == 'PASS'
+            assert report['failed'] == 0
+    
+    def test_validation_report_with_failures(self):
+        """Test validation report with some failures"""
+        validator = EnvironmentValidator()
+        
+        # Missing some required variables
+        incomplete_env = {
+            'AZURE_OPENAI_ENDPOINT': 'https://test.openai.azure.com/',
+            'AZURE_OPENAI_KEY': 'short',  # Too short
+            # Missing other required vars
+        }
+        
+        with patch.dict(os.environ, incomplete_env, clear=True):
+            report = validator.get_validation_report()
+            
+            assert report['overall_status'] == 'FAIL'
+            assert report['failed'] > 0
+            assert len(report['errors']) > 0
+
+
+@pytest.mark.unit
+class TestEnvironmentConfiguration:
+    """Test environment configuration loading"""
+    
+    def test_dotenv_file_loading(self):
+        """Test loading environment from .env file"""
+        # Create temporary .env file
+        env_content = """
+AZURE_OPENAI_ENDPOINT=https://test.openai.azure.com/
+AZURE_OPENAI_KEY=test_key_12345678901234567890
+AZURE_OPENAI_API_VERSION=2024-12-01-preview
+AZURE_OPENAI_LLM_DEPLOYMENT=gpt-4
+AZURE_OPENAI_LLM_MODEL=gpt-4
+"""
+        
+        with tempfile.NamedTemporaryFile(mode='w', suffix='.env', delete=False) as temp_file:
+            temp_file.write(env_content.strip())
+            temp_file_path = temp_file.name
+        
+        try:
+            # Load environment from file
+            from dotenv import load_dotenv
+            load_dotenv(temp_file_path)
+            
+            # Verify variables are loaded
+            assert os.getenv('AZURE_OPENAI_ENDPOINT') == 'https://test.openai.azure.com/'
+            assert os.getenv('AZURE_OPENAI_KEY') == 'test_key_12345678901234567890'
+        finally:
+            os.unlink(temp_file_path)
+    
+    def test_environment_priority(self):
+        """Test that environment variables take priority over .env file"""
+        # Set environment variable
+        with patch.dict(os.environ, {'AZURE_OPENAI_ENDPOINT': 'https://env.openai.azure.com/'}):
+            # This should take priority over any .env file
+            assert os.getenv('AZURE_OPENAI_ENDPOINT') == 'https://env.openai.azure.com/'
+    
+    def test_configuration_export(self):
+        """Test exporting configuration for debugging"""
+        test_env = {
+            'AZURE_OPENAI_ENDPOINT': 'https://test.openai.azure.com/',
+            'AZURE_OPENAI_KEY': 'secret_key_12345678901234567890',
+            'AZURE_OPENAI_API_VERSION': '2024-12-01-preview'
+        }
+        
+        with patch.dict(os.environ, test_env):
+            # Export configuration (with secrets redacted)
+            config = {}
+            for key in ['AZURE_OPENAI_ENDPOINT', 'AZURE_OPENAI_KEY', 'AZURE_OPENAI_API_VERSION']:
+                value = os.getenv(key)
+                if value and 'KEY' in key:
+                    # Redact secrets
+                    config[key] = value[:8] + '*' * (len(value) - 8)
+                else:
+                    config[key] = value
+            
+            assert config['AZURE_OPENAI_ENDPOINT'] == 'https://test.openai.azure.com/'
+            assert config['AZURE_OPENAI_KEY'].startswith('secret_k')
+            assert config['AZURE_OPENAI_KEY'].endswith('*' * 24)
+
+
+@pytest.mark.integration
+class TestApplicationEnvironmentIntegration:
+    """Test environment integration with application components"""
+    
+    def test_azure_client_initialization(self):
+        """Test Azure OpenAI client initialization with environment"""
+        test_env = {
+            'AZURE_OPENAI_ENDPOINT': 'https://test.openai.azure.com/',
+            'AZURE_OPENAI_KEY': 'test_key_12345678901234567890',
+            'AZURE_OPENAI_API_VERSION': '2024-12-01-preview',
+            'AZURE_OPENAI_LLM_DEPLOYMENT': 'gpt-4',
+            'AZURE_OPENAI_LLM_MODEL': 'gpt-4'
+        }
+        
+        with patch.dict(os.environ, test_env):
+            with patch('openai.AzureOpenAI') as mock_client:
+                # Import and initialize client
+                from app import client
+                
+                # Verify client was initialized with correct parameters
+                mock_client.assert_called_once()
+                call_args = mock_client.call_args
+                assert call_args[1]['azure_endpoint'] == test_env['AZURE_OPENAI_ENDPOINT']
+                assert call_args[1]['api_key'] == test_env['AZURE_OPENAI_KEY']
+                assert call_args[1]['api_version'] == test_env['AZURE_OPENAI_API_VERSION']
+    
+    def test_missing_environment_error_handling(self):
+        """Test application behavior with missing environment variables"""
+        with patch.dict(os.environ, {}, clear=True):
+            # Should handle missing environment gracefully
+            try:
+                from app import client
+                # If this doesn't raise an exception, the app has good error handling
+                assert True
+            except Exception as e:
+                # If it does raise an exception, it should be informative
+                assert 'environment' in str(e).lower() or 'key' in str(e).lower()
+    
+    def test_application_health_check(self):
+        """Test application health with environment validation"""
+        test_env = {
+            'AZURE_OPENAI_ENDPOINT': 'https://test.openai.azure.com/',
+            'AZURE_OPENAI_KEY': 'test_key_12345678901234567890',
+            'AZURE_OPENAI_API_VERSION': '2024-12-01-preview',
+            'AZURE_OPENAI_LLM_DEPLOYMENT': 'gpt-4',
+            'AZURE_OPENAI_LLM_MODEL': 'gpt-4'
+        }
+        
+        with patch.dict(os.environ, test_env):
+            validator = EnvironmentValidator()
+            report = validator.get_validation_report()
+            
+            # Application should be healthy with all required vars
+            assert report['overall_status'] == 'PASS'
+            
+            # Test application components can be imported
+            try:
+                from app import AgenticSkillBuilder, LessonAgent, QuizAgent, ProgressAgent
+                assert True  # All imports successful
+            except ImportError as e:
+                pytest.fail(f"Failed to import application components: {e}")
+
+
+@pytest.mark.unit
+class TestEnvironmentSecurityValidation:
+    """Test security aspects of environment validation"""
+    
+    def test_secret_redaction_in_logs(self):
+        """Test that secrets are properly redacted in logs/output"""
+        secret_value = "secret_key_abcdefghijklmnopqrstuvwxyz"
+        
+        # Simulate redacting secrets for logging
+        def redact_secret(value: str, show_chars: int = 8) -> str:
+            if len(value) <= show_chars:
+                return '*' * len(value)
+            return value[:show_chars] + '*' * (len(value) - show_chars)
+        
+        redacted = redact_secret(secret_value)
+        
+        assert redacted.startswith('secret_k')
+        assert '*' in redacted
+        assert len(redacted) == len(secret_value)
+    
+    def test_environment_variable_validation_security(self):
+        """Test validation doesn't expose sensitive data"""
+        validator = EnvironmentValidator()
+        
+        with patch.dict(os.environ, {'AZURE_OPENAI_KEY': 'very_secret_key_123456789'}):
+            result = validator._validate_variable(
+                'AZURE_OPENAI_KEY',
+                validator.REQUIRED_VARS['AZURE_OPENAI_KEY'],
+                required=True
+            )
+            
+            # Value should be captured but could be redacted for security
+            assert result.present is True
+            assert result.validation_result is True
+            # Don't assert the actual value to avoid exposing secrets in test output

tests/test_gamification.py

@@ -0,0 +1,372 @@
+"""
+Tests for the gamification system in SkillSprout
+"""
+import pytest
+from unittest.mock import Mock, patch
+import sys
+import os
+
+# Add parent directory to path
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from space_app import (
+    GamificationManager, Achievement, UserStats, EnhancedUserProgress
+)
+
+class TestAchievement:
+    """Test cases for Achievement data class"""
+    
+    def test_achievement_creation(self):
+        """Test creating an achievement"""
+        achievement = Achievement(
+            id="test_achievement",
+            name="Test Achievement",
+            description="A test achievement",
+            icon="🏆",
+            unlocked=False,
+            unlock_condition="Complete 1 lesson"
+        )
+        
+        assert achievement.id == "test_achievement"
+        assert achievement.name == "Test Achievement"
+        assert achievement.unlocked is False
+        assert achievement.icon == "🏆"
+    
+    def test_achievement_defaults(self):
+        """Test achievement with default values"""
+        achievement = Achievement(
+            id="simple",
+            name="Simple",
+            description="Simple achievement",
+            icon="⭐"
+        )
+        
+        assert achievement.unlocked is False
+        assert achievement.unlock_condition == ""
+
+class TestUserStats:
+    """Test cases for UserStats class"""
+    
+    def test_user_stats_creation(self):
+        """Test creating user stats"""
+        stats = UserStats(user_id="test_user")
+        
+        assert stats.user_id == "test_user"
+        assert stats.total_points == 0
+        assert stats.level == 1
+        assert stats.achievements == []
+        assert stats.streak_days == 0
+        assert stats.total_lessons == 0
+        assert stats.total_quizzes == 0
+        assert stats.correct_answers == 0
+    
+    def test_add_points_no_level_up(self):
+        """Test adding points without level up"""
+        stats = UserStats(user_id="test_user")
+        
+        # Act
+        stats.add_points(50)
+        
+        # Assert
+        assert stats.total_points == 50
+        assert stats.level == 1  # Should still be level 1
+    
+    def test_add_points_level_up(self):
+        """Test adding points that triggers level up"""
+        stats = UserStats(user_id="test_user")
+        
+        # Act
+        stats.add_points(150)  # Should trigger level up
+        
+        # Assert
+        assert stats.total_points == 150
+        assert stats.level == 2
+    
+    def test_add_points_multiple_levels(self):
+        """Test adding points that triggers multiple level ups"""
+        stats = UserStats(user_id="test_user")
+        
+        # Act
+        stats.add_points(450)  # Should reach level 5
+        
+        # Assert
+        assert stats.total_points == 450
+        assert stats.level == 5
+    
+    def test_add_points_max_level(self):
+        """Test that level doesn't exceed maximum"""
+        stats = UserStats(user_id="test_user")
+        
+        # Act
+        stats.add_points(2000)  # Way more than needed for max level
+        
+        # Assert
+        assert stats.level == 10  # Should cap at level 10
+    
+    def test_get_accuracy_no_quizzes(self):
+        """Test accuracy calculation with no quizzes"""
+        stats = UserStats(user_id="test_user")
+        
+        # Act & Assert
+        assert stats.get_accuracy() == 0.0
+    
+    def test_get_accuracy_with_quizzes(self):
+        """Test accuracy calculation with quiz data"""
+        stats = UserStats(user_id="test_user")
+        stats.total_quizzes = 10
+        stats.correct_answers = 8
+        
+        # Act
+        accuracy = stats.get_accuracy()
+        
+        # Assert
+        assert accuracy == 80.0
+
+class TestEnhancedUserProgress:
+    """Test cases for EnhancedUserProgress class"""
+    
+    def test_enhanced_progress_creation(self):
+        """Test creating enhanced user progress"""
+        progress = EnhancedUserProgress(
+            user_id="test_user",
+            skill="Python Programming"
+        )
+        
+        assert progress.user_id == "test_user"
+        assert progress.skill == "Python Programming"
+        assert progress.lessons_completed == 0
+        assert progress.quiz_scores == []
+        assert progress.time_spent == []
+        assert progress.mastery_level == 0.0
+
+class TestGamificationManager:
+    """Test cases for GamificationManager class"""
+    
+    @pytest.fixture
+    def gamification_manager(self):
+        """Create a GamificationManager instance for testing"""
+        return GamificationManager()
+    
+    def test_gamification_manager_initialization(self, gamification_manager):
+        """Test GamificationManager initialization"""
+        assert len(gamification_manager.user_stats) == 0
+        assert len(gamification_manager.achievements) > 0
+        
+        # Check that all required achievements exist
+        required_achievements = [
+            "first_steps", "quiz_master", "persistent", "scholar",
+            "expert", "polyglot", "perfectionist", "speed", 
+            "consistent", "explorer"
+        ]
+        
+        for achievement_id in required_achievements:
+            assert achievement_id in gamification_manager.achievements
+    
+    def test_get_user_stats_new_user(self, gamification_manager):
+        """Test getting stats for a new user"""
+        # Act
+        stats = gamification_manager.get_user_stats("new_user")
+        
+        # Assert
+        assert isinstance(stats, UserStats)
+        assert stats.user_id == "new_user"
+        assert stats.total_points == 0
+        assert stats.level == 1
+    
+    def test_get_user_stats_existing_user(self, gamification_manager):
+        """Test getting stats for existing user"""
+        # Arrange
+        user_id = "existing_user"
+        stats1 = gamification_manager.get_user_stats(user_id)
+        stats1.total_points = 100
+        
+        # Act
+        stats2 = gamification_manager.get_user_stats(user_id)
+        
+        # Assert
+        assert stats2.total_points == 100
+        assert stats1 is stats2  # Should be same object
+    
+    def test_check_achievements_first_steps(self, gamification_manager):
+        """Test unlocking first steps achievement"""
+        # Arrange
+        user_id = "test_user"
+        progress = EnhancedUserProgress(user_id=user_id, skill="Python")
+        
+        # Set up conditions for first steps achievement
+        stats = gamification_manager.get_user_stats(user_id)
+        stats.total_lessons = 1
+        
+        # Act
+        newly_unlocked = gamification_manager.check_achievements(user_id, progress)
+        
+        # Assert
+        assert len(newly_unlocked) > 0
+        achievement_ids = [a.id for a in newly_unlocked]
+        assert "first_steps" in achievement_ids
+        assert "first_steps" in stats.achievements
+    
+    def test_check_achievements_quiz_master(self, gamification_manager):
+        """Test unlocking quiz master achievement"""
+        # Arrange
+        user_id = "quiz_master_user"
+        progress = EnhancedUserProgress(user_id=user_id, skill="Python")
+        progress.quiz_scores = [100, 80, 100]  # Has perfect score
+        
+        # Act
+        newly_unlocked = gamification_manager.check_achievements(user_id, progress)
+        
+        # Assert
+        achievement_ids = [a.id for a in newly_unlocked]
+        assert "quiz_master" in achievement_ids
+    
+    def test_check_achievements_persistent(self, gamification_manager):
+        """Test unlocking persistent learner achievement"""
+        # Arrange
+        user_id = "persistent_user"
+        progress = EnhancedUserProgress(user_id=user_id, skill="Python")
+        
+        stats = gamification_manager.get_user_stats(user_id)
+        stats.total_lessons = 5
+        
+        # Act
+        newly_unlocked = gamification_manager.check_achievements(user_id, progress)
+        
+        # Assert
+        achievement_ids = [a.id for a in newly_unlocked]
+        assert "persistent" in achievement_ids
+    
+    def test_check_achievements_no_new_unlocks(self, gamification_manager):
+        """Test checking achievements when none should be unlocked"""
+        # Arrange
+        user_id = "minimal_user"
+        progress = EnhancedUserProgress(user_id=user_id, skill="Python")
+        # User has minimal progress, shouldn't unlock anything
+        
+        # Act
+        newly_unlocked = gamification_manager.check_achievements(user_id, progress)
+        
+        # Assert
+        assert len(newly_unlocked) == 0
+    
+    def test_check_achievements_already_unlocked(self, gamification_manager):
+        """Test that already unlocked achievements aren't returned again"""
+        # Arrange
+        user_id = "repeat_user"
+        progress = EnhancedUserProgress(user_id=user_id, skill="Python")
+        
+        stats = gamification_manager.get_user_stats(user_id)
+        stats.total_lessons = 1
+        stats.achievements = ["first_steps"]  # Already unlocked
+        
+        # Act
+        newly_unlocked = gamification_manager.check_achievements(user_id, progress)
+        
+        # Assert
+        achievement_ids = [a.id for a in newly_unlocked]
+        assert "first_steps" not in achievement_ids
+    
+    def test_check_achievements_points_awarded(self, gamification_manager):
+        """Test that bonus points are awarded for achievements"""
+        # Arrange
+        user_id = "points_user"
+        progress = EnhancedUserProgress(user_id=user_id, skill="Python")
+        
+        stats = gamification_manager.get_user_stats(user_id)
+        initial_points = stats.total_points
+        stats.total_lessons = 1  # Will unlock first_steps
+        
+        # Act
+        newly_unlocked = gamification_manager.check_achievements(user_id, progress)
+        
+        # Assert
+        assert len(newly_unlocked) > 0
+        assert stats.total_points > initial_points  # Should have bonus points
+    
+    def test_achievement_perfectionist(self, gamification_manager):
+        """Test perfectionist achievement (5 perfect scores)"""
+        # Arrange
+        user_id = "perfectionist_user"
+        progress = EnhancedUserProgress(user_id=user_id, skill="Python")
+        progress.quiz_scores = [100, 100, 100, 100, 100, 90]  # 5 perfect scores
+        
+        # Act
+        newly_unlocked = gamification_manager.check_achievements(user_id, progress)
+        
+        # Assert
+        achievement_ids = [a.id for a in newly_unlocked]
+        assert "perfectionist" in achievement_ids
+    
+    def test_achievement_consistent(self, gamification_manager):
+        """Test consistent achievement (7-day streak)"""
+        # Arrange
+        user_id = "consistent_user"
+        progress = EnhancedUserProgress(user_id=user_id, skill="Python")
+        
+        stats = gamification_manager.get_user_stats(user_id)
+        stats.streak_days = 7
+        
+        # Act
+        newly_unlocked = gamification_manager.check_achievements(user_id, progress)
+        
+        # Assert
+        achievement_ids = [a.id for a in newly_unlocked]
+        assert "consistent" in achievement_ids
+
+@pytest.mark.integration
+class TestGamificationIntegration:
+    """Integration tests for gamification with other systems"""
+    
+    def test_gamification_with_lesson_completion(self):
+        """Test gamification integration when completing lessons"""
+        # This would test the integration between the main app and gamification
+        # Requires importing the enhanced app components
+        pass
+    
+    def test_gamification_with_quiz_submission(self):
+        """Test gamification integration when submitting quizzes"""
+        pass
+
+@pytest.mark.unit
+class TestAchievementConditions:
+    """Test specific achievement unlock conditions"""
+    
+    @pytest.fixture
+    def sample_progress(self):
+        """Create sample progress data for testing"""        
+        return EnhancedUserProgress(
+            user_id="test_user",
+            skill="Python Programming",
+            lessons_completed=0,
+            quiz_scores=[],
+            time_spent=[],
+            mastery_level=0.0
+        )
+    
+    def test_scholar_achievement_condition(self, gamification_manager, enhanced_user_progress):
+        """Test scholar achievement (10 lessons)"""
+        # Arrange
+        stats = gamification_manager.get_user_stats("test_user")
+        stats.total_lessons = 10
+        
+        # Act
+        newly_unlocked = gamification_manager.check_achievements("test_user", enhanced_user_progress)
+        
+        # Assert
+        achievement_ids = [a.id for a in newly_unlocked]
+        assert "scholar" in achievement_ids
+    
+    def test_expert_achievement_condition(self, gamification_manager, enhanced_user_progress):
+        """Test expert achievement (20 lessons)"""
+        # Arrange
+        stats = gamification_manager.get_user_stats("test_user")
+        stats.total_lessons = 20
+        
+        # Act
+        newly_unlocked = gamification_manager.check_achievements("test_user", enhanced_user_progress)
+        
+        # Assert
+        achievement_ids = [a.id for a in newly_unlocked]
+        assert "expert" in achievement_ids
+        assert "scholar" in achievement_ids  # Should also unlock scholar
+        assert "persistent" in achievement_ids  # Should also unlock persistent

tests/test_mcp_endpoints.py

@@ -0,0 +1,371 @@
+"""
+Tests for MCP server endpoints in SkillSprout
+"""
+import pytest
+import requests
+import json
+import asyncio
+import threading
+import time
+import subprocess
+import sys
+from typing import Dict, Any
+
+@pytest.mark.integration
+class TestMCPEndpoints:
+    """Integration tests for MCP server endpoints"""
+    
+    @pytest.fixture(scope="class", autouse=True)
+    def setup_test_server(self, test_server_url):
+        """Start MCP server for testing"""
+        # Try to connect to existing server first
+        try:
+            response = requests.get(test_server_url, timeout=2)
+            if response.status_code == 200:
+                print(f"✅ MCP server already running at {test_server_url}")
+                yield
+                return
+        except:
+            pass
+        
+        # Start server if not running
+        print(f"🚀 Starting MCP server for testing...")
+        server_process = subprocess.Popen(
+            [sys.executable, "space_app.py"],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE
+        )
+        
+        # Wait for server to start
+        max_attempts = 30
+        for attempt in range(max_attempts):
+            try:
+                response = requests.get(test_server_url, timeout=1)
+                if response.status_code == 200:
+                    print(f"✅ MCP server started successfully")
+                    break
+            except:
+                time.sleep(1)
+        else:
+            server_process.terminate()
+            pytest.fail("Failed to start MCP server for testing")
+        
+        yield
+        
+        # Cleanup
+        server_process.terminate()
+        server_process.wait()
+    
+    def test_root_endpoint(self, test_server_url):
+        """Test root endpoint returns server information"""
+        # Act
+        response = requests.get(test_server_url, timeout=10)
+        
+        # Assert
+        assert response.status_code == 200
+        data = response.json()
+        assert "name" in data
+        assert "SkillSprout" in data["name"]
+        assert "version" in data
+        assert "hackathon" in data
+        assert data["track"] == "mcp-server-track"
+    
+    def test_get_available_skills(self, test_server_url):
+        """Test getting list of available skills"""
+        # Act
+        response = requests.get(f"{test_server_url}/mcp/skills", timeout=10)
+        
+        # Assert
+        assert response.status_code == 200
+        data = response.json()
+        assert "predefined_skills" in data
+        skills = data["predefined_skills"]
+        assert isinstance(skills, list)
+        assert len(skills) > 0
+        assert "Python Programming" in skills
+    
+    def test_generate_lesson_valid_request(self, test_server_url):
+        """Test lesson generation with valid request"""
+        # Arrange
+        lesson_data = {
+            "skill": "Python Programming",
+            "user_id": "test_user",
+            "difficulty": "beginner"
+        }
+        
+        # Act
+        response = requests.post(
+            f"{test_server_url}/mcp/lesson/generate",
+            json=lesson_data,
+            timeout=30
+        )
+        
+        # Assert
+        assert response.status_code == 200
+        data = response.json()
+        assert "lesson" in data
+        lesson = data["lesson"]
+        assert lesson["skill"] == "Python Programming"
+        assert lesson["difficulty"] == "beginner"
+        assert "title" in lesson
+        assert "content" in lesson
+        assert "mcp_server" in data
+        assert data["mcp_server"] == "SkillSprout"
+    
+    def test_generate_lesson_custom_skill(self, test_server_url):
+        """Test lesson generation with custom skill"""
+        # Arrange
+        lesson_data = {
+            "skill": "Custom Cooking Skill",
+            "user_id": "chef_user",
+            "difficulty": "intermediate"
+        }
+        
+        # Act
+        response = requests.post(
+            f"{test_server_url}/mcp/lesson/generate",
+            json=lesson_data,
+            timeout=30
+        )
+        
+        # Assert
+        assert response.status_code == 200
+        data = response.json()
+        assert data["lesson"]["skill"] == "Custom Cooking Skill"
+        assert data["lesson"]["difficulty"] == "intermediate"
+    
+    def test_generate_lesson_missing_skill(self, test_server_url):
+        """Test lesson generation with missing skill parameter"""
+        # Arrange
+        lesson_data = {
+            "user_id": "test_user",
+            "difficulty": "beginner"
+        }
+        
+        # Act
+        response = requests.post(
+            f"{test_server_url}/mcp/lesson/generate",
+            json=lesson_data,
+            timeout=10
+        )
+        
+        # Assert
+        assert response.status_code == 422  # Validation error
+    
+    def test_get_user_progress_new_user(self, test_server_url):
+        """Test getting progress for new user"""
+        # Act
+        response = requests.get(
+            f"{test_server_url}/mcp/progress/new_test_user",
+            timeout=10
+        )
+        
+        # Assert
+        assert response.status_code == 200
+        data = response.json()
+        assert data["user_id"] == "new_test_user"
+        assert "skills_progress" in data
+        assert "total_skills_learning" in data
+        assert "mcp_server" in data
+        assert data["mcp_server"] == "SkillSprout"
+    
+    def test_get_user_progress_with_skill_filter(self, test_server_url):
+        """Test getting progress filtered by skill"""
+        # Act
+        response = requests.get(
+            f"{test_server_url}/mcp/progress/test_user?skill=Python%20Programming",
+            timeout=10
+        )
+        
+        # Assert
+        assert response.status_code == 200
+        data = response.json()
+        assert data["user_id"] == "test_user"
+    
+    def test_submit_quiz_results(self, test_server_url):
+        """Test submitting quiz results"""
+        # Arrange
+        quiz_data = {
+            "user_id": "test_user",
+            "skill": "Python Programming", 
+            "lesson_title": "Variables and Data Types",
+            "answers": ["A storage container", "True", "int"]
+        }
+        
+        # Act
+        response = requests.post(
+            f"{test_server_url}/mcp/quiz/submit",
+            json=quiz_data,
+            timeout=15
+        )
+        
+        # Assert
+        assert response.status_code == 200
+        data = response.json()
+        assert "score" in data
+        assert "feedback" in data
+        assert data["user_id"] == "test_user"
+        assert "mcp_server" in data
+        assert data["mcp_server"] == "SkillSprout"
+    
+    def test_submit_quiz_empty_answers(self, test_server_url):
+        """Test submitting quiz with empty answers"""
+        # Arrange
+        quiz_data = {
+            "user_id": "test_user",
+            "skill": "Data Science",
+            "lesson_title": "Introduction to Data",
+            "answers": []
+        }
+        
+        # Act
+        response = requests.post(
+            f"{test_server_url}/mcp/quiz/submit",
+            json=quiz_data,
+            timeout=10
+        )
+        
+        # Assert
+        assert response.status_code == 200
+        data = response.json()
+        assert data["score"] == 0
+    
+    def test_submit_quiz_invalid_data(self, test_server_url):
+        """Test submitting quiz with invalid data"""
+        # Arrange
+        quiz_data = {
+            "user_id": "test_user",
+            # Missing required fields
+        }
+        
+        # Act
+        response = requests.post(
+            f"{test_server_url}/mcp/quiz/submit",
+            json=quiz_data,
+            timeout=10
+        )
+        
+        # Assert
+        assert response.status_code == 422  # Validation error
+
+@pytest.mark.integration
+class TestMCPEndpointsSlow:
+    """Slower integration tests that require more setup"""
+    
+    @pytest.mark.slow
+    def test_end_to_end_learning_flow(self, test_server_url):
+        """Test complete learning flow through MCP endpoints"""
+        user_id = "e2e_test_user"
+        skill = "JavaScript"
+        
+        # Step 1: Get available skills
+        response = requests.get(f"{test_server_url}/mcp/skills")
+        assert response.status_code == 200
+        
+        # Step 2: Generate a lesson
+        lesson_data = {
+            "skill": skill,
+            "user_id": user_id,
+            "difficulty": "beginner"
+        }
+        response = requests.post(
+            f"{test_server_url}/mcp/lesson/generate",
+            json=lesson_data,
+            timeout=30
+        )
+        assert response.status_code == 200
+        lesson_response = response.json()
+        lesson_title = lesson_response["lesson"]["title"]
+        
+        # Step 3: Submit quiz results
+        quiz_data = {
+            "user_id": user_id,
+            "skill": skill,
+            "lesson_title": lesson_title,
+            "answers": ["answer1", "answer2"]
+        }
+        response = requests.post(
+            f"{test_server_url}/mcp/quiz/submit",
+            json=quiz_data,
+            timeout=15
+        )
+        assert response.status_code == 200
+        
+        # Step 4: Check progress
+        response = requests.get(f"{test_server_url}/mcp/progress/{user_id}")
+        assert response.status_code == 200
+        progress_data = response.json()
+        assert skill in progress_data["skills_progress"]
+    
+    @pytest.mark.slow
+    def test_multiple_concurrent_requests(self, test_server_url):
+        """Test server handling multiple concurrent requests"""
+        import concurrent.futures
+        
+        def make_request(user_id):
+            """Make a lesson generation request"""
+            lesson_data = {
+                "skill": "Python Programming",
+                "user_id": f"concurrent_user_{user_id}",
+                "difficulty": "beginner"
+            }
+            response = requests.post(
+                f"{test_server_url}/mcp/lesson/generate",
+                json=lesson_data,
+                timeout=30
+            )
+            return response.status_code == 200
+        
+        # Act - Make 5 concurrent requests
+        with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
+            futures = [executor.submit(make_request, i) for i in range(5)]
+            results = [future.result() for future in concurrent.futures.as_completed(futures)]
+        
+        # Assert - All requests should succeed
+        assert all(results)
+
+@pytest.mark.unit
+class TestMCPErrorHandling:
+    """Unit tests for MCP error handling scenarios"""
+    
+    def test_invalid_endpoint(self, test_server_url):
+        """Test accessing invalid endpoint"""
+        try:
+            response = requests.get(f"{test_server_url}/invalid/endpoint", timeout=5)
+            assert response.status_code == 404
+        except requests.exceptions.ConnectionError:
+            pytest.skip("MCP server not running")
+    
+    def test_malformed_json_request(self, test_server_url):
+        """Test sending malformed JSON"""
+        try:
+            response = requests.post(
+                f"{test_server_url}/mcp/lesson/generate",
+                data="invalid json",
+                headers={"Content-Type": "application/json"},
+                timeout=5
+            )
+            assert response.status_code in [400, 422]
+        except requests.exceptions.ConnectionError:
+            pytest.skip("MCP server not running")
+
+class TestMCPHelpers:
+    """Helper functions for MCP testing"""
+    
+    @staticmethod
+    def is_server_running(url: str) -> bool:
+        """Check if MCP server is running"""
+        try:
+            response = requests.get(url, timeout=2)
+            return response.status_code == 200
+        except:
+            return False
+    
+    @staticmethod
+    def wait_for_server(url: str, timeout: int = 30) -> bool:
+        """Wait for server to become available"""
+        for _ in range(timeout):
+            if TestMCPHelpers.is_server_running(url):
+                return True
+            time.sleep(1)
+        return False

tests/test_real_functionality.py

@@ -0,0 +1,89 @@
+"""
+Integration test using real Azure OpenAI API - run this when you want to test the full stack
+"""
+import pytest
+import asyncio
+import os
+from space_app import AgenticSkillBuilder
+
+@pytest.mark.integration
+@pytest.mark.skipif(not os.getenv("AZURE_OPENAI_KEY"), reason="No Azure API key")
+class TestRealIntegration:
+    """Integration tests using real Azure OpenAI API"""
+    
+    def test_full_lesson_flow(self):
+        """Test the complete lesson flow with real AI"""
+        app = AgenticSkillBuilder()
+        
+        # This will make real API calls - that's OK for integration tests!
+        skill = "Basic Addition"
+        
+        try:
+            # Test lesson generation
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            
+            lesson_content, _, _ = loop.run_until_complete(app.start_lesson(skill))
+            
+            # Basic validation
+            assert "addition" in lesson_content.lower() or "add" in lesson_content.lower()
+            assert len(lesson_content) > 100  # Should be substantial content
+            
+            print(f"✅ Real lesson generated for '{skill}'")
+            print(f"Content length: {len(lesson_content)} characters")
+            
+        except Exception as e:
+            pytest.skip(f"Integration test failed (this is OK): {e}")
+
+@pytest.mark.unit
+class TestWithoutMocks:
+    """Unit tests focusing on business logic without complex mocks"""
+    
+    def test_app_initialization(self):
+        """Test that the app initializes correctly"""
+        app = AgenticSkillBuilder()
+        
+        assert app.predefined_skills is not None
+        assert len(app.predefined_skills) > 0
+        assert "Python Programming" in app.predefined_skills
+    
+    def test_progress_tracking(self):
+        """Test progress tracking logic"""
+        from space_app import EnhancedUserProgress
+        
+        progress = EnhancedUserProgress("test_user", "Math")
+        
+        # Test initial state
+        assert progress.lessons_completed == 0
+        assert progress.mastery_level == 0.0
+        assert len(progress.quiz_scores) == 0
+        
+        # Test adding quiz scores
+        progress.quiz_scores = [0.8, 0.9, 0.7]
+        progress.lessons_completed = 3
+        
+        # Test mastery calculation
+        mastery = progress.calculate_mastery()
+        assert mastery > 0
+        assert mastery <= 100
+    
+    def test_gamification_basics(self):
+        """Test gamification without complex mocks"""
+        from space_app import GamificationManager, UserStats
+        
+        gm = GamificationManager()
+        
+        # Test user stats creation
+        stats = gm.get_user_stats("test_user")
+        assert isinstance(stats, UserStats)
+        assert stats.user_id == "test_user"
+        assert stats.total_points == 0
+        
+        # Test point addition
+        stats.add_points(100)
+        assert stats.total_points == 100
+        
+        # Test level calculation
+        initial_level = stats.level
+        stats.add_points(500)  # Should trigger level up
+        assert stats.level >= initial_level

validate_hackathon.py

@@ -1,235 +0,0 @@
-"""
-Hackathon Validation Script
-Tests the MCP server functionality for submission requirements
-"""
-
-import asyncio
-import requests
-import time
-import subprocess
-import threading
-from datetime import datetime
-
-def test_mcp_server_endpoints():
-    """Test MCP server endpoints to ensure hackathon compliance"""
-    print("🧪 HACKATHON VALIDATION - MCP SERVER TESTING")
-    print("=" * 60)
-    
-    base_url = "http://localhost:8001"  # MCP server port
-    
-    tests = [
-        ("Root endpoint", "GET", "/"),
-        ("Skills list", "GET", "/mcp/skills"),
-        ("Progress endpoint", "GET", "/mcp/progress/test_user"),
-    ]
-    
-    print(f"🌐 Testing MCP server at {base_url}")
-    print(f"📋 Running {len(tests)} endpoint tests...\n")
-    
-    results = []
-    
-    for test_name, method, endpoint in tests:
-        try:
-            url = f"{base_url}{endpoint}"
-            print(f"🔄 Testing {test_name}: {method} {endpoint}")
-            
-            if method == "GET":
-                response = requests.get(url, timeout=5)
-            elif method == "POST":
-                response = requests.post(url, json={}, timeout=5)
-            
-            if response.status_code == 200:
-                print(f"  ✅ SUCCESS: {response.status_code}")
-                try:
-                    data = response.json()
-                    if "mcp" in str(data).lower() or "agentic" in str(data).lower():
-                        print(f"  🎯 MCP-compliant response detected")
-                except:
-                    pass
-                results.append((test_name, True, response.status_code))
-            else:
-                print(f"  ❌ FAILED: {response.status_code}")
-                results.append((test_name, False, response.status_code))
-                
-        except requests.exceptions.RequestException as e:
-            print(f"  ❌ CONNECTION ERROR: {e}")
-            results.append((test_name, False, "Connection Error"))
-        
-        print()
-    
-    # Summary
-    print("📊 TEST SUMMARY")
-    print("-" * 40)
-    passed = sum(1 for _, success, _ in results if success)
-    total = len(results)
-    print(f"✅ Passed: {passed}/{total}")
-    
-    if passed == total:
-        print("🎉 ALL MCP ENDPOINT TESTS PASSED!")
-    else:
-        print("⚠️  Some tests failed. Check the MCP server.")
-    
-    return results
-
-def test_post_endpoints():
-    """Test POST endpoints with sample data"""
-    print("\n🧪 TESTING POST ENDPOINTS")
-    print("=" * 40)
-    
-    base_url = "http://localhost:8001"
-    
-    # Test lesson generation
-    try:
-        print("🔄 Testing lesson generation...")
-        lesson_data = {
-            "skill": "Python Programming",
-            "user_id": "test_user",
-            "difficulty": "beginner"
-        }
-        response = requests.post(f"{base_url}/mcp/lesson/generate", json=lesson_data, timeout=10)
-        if response.status_code == 200:
-            print("  ✅ Lesson generation successful")
-            data = response.json()
-            if "lesson" in data:
-                print("  🎯 Lesson data structure valid")
-        else:
-            print(f"  ❌ Lesson generation failed: {response.status_code}")
-    except Exception as e:
-        print(f"  ❌ Lesson generation error: {e}")
-
-    # Test quiz submission
-    try:
-        print("🔄 Testing quiz submission...")
-        quiz_data = {
-            "user_id": "test_user",
-            "skill": "Python Programming",
-            "lesson_title": "Variables and Data Types",
-            "answers": ["correct", "incorrect", "correct"]
-        }
-        response = requests.post(f"{base_url}/mcp/quiz/submit", json=quiz_data, timeout=10)
-        if response.status_code == 200:
-            print("  ✅ Quiz submission successful")
-        else:
-            print(f"  ❌ Quiz submission failed: {response.status_code}")
-    except Exception as e:
-        print(f"  ❌ Quiz submission error: {e}")
-
-def validate_hackathon_requirements():
-    """Validate all hackathon submission requirements"""
-    print("\n🏆 HACKATHON SUBMISSION VALIDATION")
-    print("=" * 50)
-    
-    requirements = []
-    
-    # Check README.md for required tag
-    try:
-        with open("README.md", "r", encoding="utf-8") as f:
-            readme_content = f.read()
-            if "mcp-server-track" in readme_content:
-                print("✅ README.md contains 'mcp-server-track' tag")
-                requirements.append(("README tag", True))
-            else:
-                print("❌ README.md missing 'mcp-server-track' tag")
-                requirements.append(("README tag", False))
-    except FileNotFoundError:
-        print("❌ README.md not found")
-        requirements.append(("README file", False))
-    
-    # Check for demo video link
-    try:
-        with open("README.md", "r", encoding="utf-8") as f:
-            readme_content = f.read()
-            if "demo-video-link.com" in readme_content or "your-demo-video-link.com" in readme_content:
-                print("⚠️  Demo video link is placeholder - needs actual video")
-                requirements.append(("Demo video", False))
-            elif any(video_keyword in readme_content.lower() for video_keyword in ["youtube.com", "vimeo.com", "loom.com", "demo video"]):
-                print("✅ Demo video link appears to be present")
-                requirements.append(("Demo video", True))
-            else:
-                print("❌ Demo video link not found")
-                requirements.append(("Demo video", False))
-    except:
-        requirements.append(("Demo video check", False))
-    
-    # Check space_app.py exists and has MCP endpoints
-    try:
-        with open("space_app.py", "r", encoding="utf-8") as f:
-            app_content = f.read()
-            if "FastAPI" in app_content and "@mcp_app" in app_content:
-                print("✅ space_app.py has MCP server integration")
-                requirements.append(("MCP integration", True))
-            else:
-                print("❌ space_app.py missing MCP server integration")
-                requirements.append(("MCP integration", False))
-    except FileNotFoundError:
-        print("❌ space_app.py not found")
-        requirements.append(("Main app file", False))
-    
-    # Check requirements.txt
-    try:
-        with open("requirements.txt", "r") as f:
-            reqs = f.read()
-            if "gradio" in reqs and "fastapi" in reqs:
-                print("✅ requirements.txt has necessary dependencies")
-                requirements.append(("Dependencies", True))
-            else:
-                print("❌ requirements.txt missing key dependencies")
-                requirements.append(("Dependencies", False))
-    except FileNotFoundError:
-        print("❌ requirements.txt not found")
-        requirements.append(("Requirements file", False))
-    
-    print("\n📋 SUBMISSION CHECKLIST")
-    print("-" * 30)
-    passed = sum(1 for _, success in requirements if success)
-    total = len(requirements)
-    
-    for req_name, success in requirements:
-        status = "✅" if success else "❌"
-        print(f"{status} {req_name}")
-    
-    print(f"\n📊 Overall Score: {passed}/{total}")
-    
-    if passed == total:
-        print("🎉 READY FOR HACKATHON SUBMISSION!")
-    else:
-        print("⚠️  Please address the failed requirements above.")
-    
-    return requirements
-
-def main():
-    """Main validation function"""
-    print("🚀 AGENTIC SKILL BUILDER - HACKATHON VALIDATION")
-    print("=" * 60)
-    print(f"⏰ Validation Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
-    print()
-    
-    # First validate file-based requirements
-    validate_hackathon_requirements()
-    
-    # Ask user if they want to test the running server
-    print("\n" + "="*60)
-    print("🌐 MCP SERVER TESTING")
-    print("To test MCP endpoints, the server should be running on localhost:8001")
-    print("You can start it with: python space_app.py")
-    
-    try:
-        # Try to test if server is running
-        response = requests.get("http://localhost:8001", timeout=2)
-        print("✅ Server detected running!")
-        test_mcp_server_endpoints()
-        test_post_endpoints()
-    except:
-        print("⚠️  Server not detected. Start the server to test MCP endpoints.")
-        print("   Command: python space_app.py")
-    
-    print("\n" + "="*60)
-    print("🎯 NEXT STEPS FOR HACKATHON SUBMISSION:")
-    print("1. 📹 Record demo video showing MCP server in action")
-    print("2. 🔗 Update README.md with actual demo video link")
-    print("3. 🚀 Upload to Hugging Face Spaces under Agents-MCP-Hackathon org")
-    print("4. ✅ Ensure all MCP endpoints work in the deployed Space")
-    print("=" * 60)
-
-if __name__ == "__main__":
-    main()