AlgoGuide Backend - Gemini Web Integration

This backend service integrates Google's Gemini AI to analyze user onboarding data and generate personalized learning resources by scraping information across the internet.

Features

• 🤖 Gemini AI Integration: Uses Google's Gemini API to analyze user profiles and generate search queries
• 🌐 Web Scraping: Intelligently searches for learning resources across the web
• 📚 Resource Categorization: Automatically categorizes resources based on user needs
• 🔥 Firebase Integration: Stores generated resources in Firestore
• 🚀 FastAPI: RESTful API endpoints for easy integration
• 📊 Personalized Learning: Tailored resources based on user's weak areas, target companies, and learning preferences

Setup Instructions

1. Install Dependencies

pip install -r requirements.txt

2. Set up Gemini API Key

1. Visit Google AI Studio
2. Create a new API key
3. Set the environment variable:

# On Windows
set GEMINI_API_KEY=your_api_key_here

# On Linux/Mac
export GEMINI_API_KEY=your_api_key_here

Or create a .env file (copy from .env.example):

GEMINI_API_KEY=your_actual_api_key_here

3. Set up Firebase

Make sure your firebase-credentials.json file is properly configured with your Firebase service account credentials.

4. Run the Server

python main.py

The server will start on http://localhost:8000

API Endpoints

Generate Resources

Generate personalized learning resources for a user:

By User ID:

POST /generate-resources/{user_id}

By Email:

POST /generate-resources-by-email/{email}

Get Home Resources

Retrieve generated resources for a user:

By User ID:

GET /home/{user_id}

By Email:

GET /home-by-email/{email}

How It Works

1. User Analysis: The Gemini agent analyzes the user's onboarding answers to create a learning profile
2. Query Generation: Gemini generates targeted search queries based on:
   • Weak areas to improve
   • Target companies for interview prep
   • Preferred technologies and roles
   • Learning timeline and preferences
3. Resource Discovery: The system searches for high-quality resources from:
   • YouTube tutorials
   • Technical blogs (Medium, Dev.to)
   • GitHub repositories
   • Coding practice platforms (LeetCode, HackerRank)
   • Course platforms (Coursera, Udemy)
4. Categorization: Resources are automatically categorized:
   • 📈 Weak Areas Improvement
   • 💼 Interview Preparation
   • 🛠️ Skill Development
   • 💻 Practice Problems
   • 📖 Technology Tutorials
   • 🎓 General Learning
5. Storage: All resources are saved to Firebase under the home collection

Data Structure

User Profile (from onboarding)

{
  "name": "User Name",
  "status": "College Student",
  "education": "B.Tech",
  "graduation_year": "2027",
  "primary_language": "Python",
  "tech_stack": ["React", "Node.js", "FastAPI"],
  "familiar_topics": ["Web Development"],
  "weak_areas": ["DSA", "System Design"],
  "target_companies": ["Google", "Microsoft"],
  "preferred_role": "Software Developer",
  "target_timeline": "6 months",
  "preferred_resources": ["Video", "Interactive Coding"]
}

Generated Resources

{
  "user_id": "user123",
  "user_profile": { /* user profile data */ },
  "search_queries": [
    "DSA tutorial Python beginner",
    "Google software engineer interview preparation",
    "System design basics course"
  ],
  "total_resources": 25,
  "resources": {
    "weak_areas_improvement": [
      {
        "title": "Data Structures and Algorithms in Python",
        "url": "https://example.com/dsa-python",
        "description": "Comprehensive DSA course for beginners",
        "resource_type": "course",
        "difficulty": "beginner",
        "estimated_time": 120,
        "tags": ["DSA", "Python", "Algorithms"]
      }
    ],
    "interview_preparation": [ /* ... */ ],
    /* ... other categories ... */
  },
  "generated_at": "2025-10-31T00:04:08Z"
}

Testing

Run the test script to verify the integration:

python test_gemini_integration.py

This will:

1. Store sample user answers
2. Generate personalized resources
3. Retrieve and display the results

Environment Variables

• GEMINI_API_KEY: Required for Gemini AI integration
• HOST: Server host (default: 0.0.0.0)
• PORT: Server port (default: 8000)

Firebase Collections

• users: User profiles and basic information
• users/{uid}/question_answers: User onboarding responses
• home: Generated personalized resources for each user

Error Handling

The system includes comprehensive error handling:

• Fallback queries if Gemini API fails
• Graceful degradation for resource categorization
• Detailed error messages in API responses

Rate Limiting

The system includes built-in delays to respect API rate limits:

• 0.5 second delay between search queries
• Configurable batch sizes for resource generation

Security Notes

• Never commit API keys to version control
• Use environment variables or secure key management
• Restrict CORS origins in production
• Validate all user inputs

Production Deployment

1. Set up proper environment variables
2. Configure CORS origins for your frontend
3. Use a production WSGI server (gunicorn, uvicorn)
4. Set up proper Firebase security rules
5. Monitor API usage and costs