🎯 Laptops Recommend - AI-Powered Laptop Recommendation System

A web-based application that uses CrewAI and FastAPI to recommend laptops based on student profile, budget, and technical requirements. The system scrapes data from Amazon and provides intelligent recommendations powered by multiple LLM providers.

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📋 Features

• ✅ AI-Powered Recommendations - Uses CrewAI agents to analyze and recommend laptops
• ✅ Multi-LLM Support - Works with OpenAI, Google Gemini, and Kimi (via LiteLLM)
• ✅ Dataset Management - Data cleaning and statistical analysis tools
• ✅ Interactive Dashboard - Beautiful frontend for browsing and filtering laptops
• ✅ Real-time Processing - Fast API responses with streaming support
• ✅ CORS Enabled - Ready for external integrations

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🛠️ Requirements

System Requirements

• Python 3.8 or higher
• Node.js (optional, for frontend development)

Python Dependencies

All dependencies are listed in backend/requirements.txt:

fastapi>=0.110.0           # Web framework
uvicorn>=0.28.0            # ASGI server
pandas>=2.0.0              # Data processing
crewai>=1.14.1             # AI agents framework
python-dotenv>=1.0.0       # Environment variable management
langchain-google-genai>=1.0.0  # Google Gemini integration
litellm>=1.88.0            # Multi-LLM provider support (Kimi, etc.)

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🚀 Installation & Setup

Step 1: Clone the Repository

git clone https://github.com/sumitsartale4952/Crew_AI_Project-Web_scrap_data_from_amazon_and_laptops_Recommend.git
cd Laptop_Recomandation

Step 2: Create a Virtual Environment (Recommended)

# Using venv
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

Step 3: Install Dependencies

pip install -r backend/requirements.txt

Step 4: Configure Environment Variables

Create a .env file in the root directory with your API keys:

# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key_here

# Google Gemini Configuration
GOOGLE_API_KEY=your_google_api_key_here
GEMINI_API_KEY=your_gemini_api_key_here

# Kimi/NVIDIA Configuration (Optional)
NVIDIA_API_KEY=your_nvidia_api_key_here

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📦 Project Structure

Laptop_Recomandation/
├── backend/
│   ├── main.py              # FastAPI main application
│   ├── crew.py              # CrewAI configuration
│   ├── data_manager.py      # Data processing utilities
│   ├── tools.py             # Custom tools for agents
│   ├── requirements.txt      # Python dependencies
│   └── test_backend.py       # Backend tests
├── frontend/
│   ├── index.html           # Main HTML page
│   ├── app.js               # Frontend JavaScript
│   └── style.css            # Styling
├── Datasets/
│   ├── amazon_laptops.csv   # Raw laptop data
│   └── Clean Dataset.csv    # Cleaned data
├── Scraping_Data_From_Amazon.ipynb  # Data scraping notebook
└── README.md                # This file

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▶️ Running the Application

Start the Backend & Frontend Server

The frontend is served from the backend, so you only need to run one command:

cd backend
python -m uvicorn main:app --reload --host 0.0.0.0 --port 8000

Then open your browser and navigate to:

http://localhost:8000

API Endpoints

| Endpoint | Method | Description | |----------|--------|-------------| | / | GET | Serve frontend dashboard | | /api/stats | GET | Get dataset statistics | | /api/laptops | GET | Get all cleaned laptops | | /api/clean | POST | Trigger data cleaning | | /api/recommend | POST | Get AI recommendations |

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💡 Usage Example

Sending a Recommendation Request

curl -X POST http://localhost:8000/api/recommend \
  -H "Content-Type: application/json" \
  -H "X-API-Key: your_api_key" \
  -d '{
    "provider": "openai",
    "major": "Computer Science",
    "budget": 1500,
    "ram": 16,
    "brand": "Dell",
    "os_name": "Windows",
    "details": "Need a laptop for programming and machine learning"
  }'

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🔑 Supported LLM Providers

| Provider | Configuration | |----------|---| | OpenAI | Set OPENAI_API_KEY environment variable | | Google Gemini | Set GOOGLE_API_KEY or GEMINI_API_KEY | | Kimi (via LiteLLM) | Set NVIDIA_API_KEY or use header |

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📊 Data Cleaning

The application includes data cleaning tools. Run:

curl -X POST http://localhost:8000/api/clean

This will:

• Clean raw Amazon laptop data
• Remove duplicates
• Standardize formats
• Generate statistics

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🔧 Troubleshooting

Issue: "LiteLLM not installed"

Solution: Install LiteLLM:

pip install litellm

Issue: "API Key not found"

Solution: Ensure your .env file is configured with the correct API keys.

Issue: Frontend not loading

Solution: Make sure the frontend folder exists in the project root with index.html, app.js, and style.css.

Issue: Port 8000 already in use

Solution: Use a different port:

python -m uvicorn main:app --reload --host 0.0.0.0 --port 8080

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📝 Development Notes

Adding New LLM Providers

Edit backend/crew.py to add support for additional LLM providers.

Customizing Agents

Modify agent configurations in backend/crew.py to change behavior.

Frontend Customization

Edit HTML/CSS/JS in the frontend/ directory. Changes will hot-reload thanks to uvicorn's --reload flag.

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📄 License

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

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👤 Author

Faisal Sayyad

GitHub: @faisalsayyad03

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🤝 Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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📞 Support

For issues, questions, or suggestions, please open an issue on GitHub or contact the author.

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Last Updated: June 2026