MeetSpot 🗺️

Intelligent Meeting Point Recommendation System - Find the Perfect Place for Every Gathering

🎯 Live Demo | 📺 Demo Video | English | 简体中文

📱 Screenshots

Main Interface

Multi-Venue Selection

Recommendation Results

Detailed Information

🌟 Features

MeetSpot is an intelligent meeting point recommendation system that calculates optimal meeting locations based on multiple participants' geographical positions and recommends nearby venues.

✨ Core Features

• 🎯 Smart Center Point Calculation: Calculate geometric center based on multiple locations for fairness
• 🏢 Multi-Scenario Recommendations: Search multiple venue types simultaneously (cafes + restaurants + libraries)
• 📍 Multi-Location Support: Support 2-10 participant locations
• 🎨 Intuitive User Interface: Modern responsive design
• 🚀 Real-time Recommendations: Generate personalized recommendations quickly
• 📊 Intelligent Ranking: Comprehensive sorting based on ratings, distance, and user requirements

🔥 Latest Optimizations

• ✅ Multi-Scenario Recommendations: Support simultaneous selection of multiple venue types
• ✅ Frontend Multi-Selection: Intuitive venue type selection interface
• ✅ Smart Ranking Algorithm: Scenario matching reward mechanism
• ✅ Performance Monitoring: Complete performance statistics and health checks
• ✅ Error Handling: Robust exception handling mechanism

🚀 Quick Start

Requirements

• Python 3.11+
• Amap (Gaode Map) API Key
• Modern browsers (Chrome, Firefox, Safari, Edge)

Installation

1. Clone Repository

git clone https://github.com/JasonRobertDestiny/MeetSpot.git
cd MeetSpot

2. Install Dependencies

pip install -r requirements.txt

3. Configure API Key

cp config/config.toml.example config/config.toml

Edit config/config.toml and add your Amap API key:

[amap]
api_key = "your_amap_api_key_here"

4. Start Service

python web_server.py

5. Access Application Open browser and visit: http://127.0.0.1:8000

📱 Usage

Basic Usage

1. Input Locations: Add 2-10 participant locations
2. Select Scenarios: Choose 1-3 venue types (cafes, restaurants, libraries, etc.)
3. Set Requirements: Add special requirements (convenient parking, quiet environment, etc.)
4. Get Recommendations: Click search to get intelligent recommendations

Advanced Features

• Multi-Scenario Combination: Search "cafe restaurant" simultaneously for more options
• Custom Keywords: Enter special venue types like "escape room"
• Filter Conditions: Filter by rating, distance, price
• Special Requirements: Support parking, Wi-Fi, private rooms, etc.

🏗️ Technology Architecture

Backend Stack

• FastAPI: High-performance web framework
• Pydantic: Data validation and settings management
• aiohttp: Async HTTP client
• Amap API: Geocoding and POI search

Frontend Stack

• HTML5 + CSS3: Responsive design
• Vanilla JavaScript: Lightweight interaction
• Boxicons: Icon library
• Modern UI Design: Gradients and glass effects

Core Algorithms

• Geometric Center Calculation: Multi-point centroid algorithm
• Intelligent Ranking: Multi-factor scoring system
• Scenario Matching: Keyword matching reward
• Deduplication Algorithm: Smart deduplication based on name and address

📊 API Documentation

Main Endpoints

• GET / - Homepage redirect
• POST /api/find_meetspot - Meeting point recommendation
• GET /health - Health check
• GET /workspace/js_src/{filename} - Generated recommendation pages

Request Example

curl -X POST "http://127.0.0.1:8000/api/find_meetspot" \
  -H "Content-Type: application/json" \
  -d '{
    "locations": ["Beijing University", "Tsinghua University"],
    "keywords": "cafe restaurant",
    "user_requirements": "convenient parking"
  }'

Response Example

{
  "success": true,
  "html_url": "/workspace/js_src/place_recommendation_20250624_12345678.html",
  "locations_count": 2,
  "keywords": "cafe restaurant",
  "processing_time": 0.52
}

🧪 Testing

Complete test suite included:

# Run all tests
python test_optimizations.py      # System optimization tests
python test_multi_scenario.py     # Multi-scenario feature tests
python comprehensive_test.py      # Comprehensive feature tests

# Health check
curl http://127.0.0.1:8000/health

📈 Performance Metrics

• Response Time:

  • Single scenario: 0.3-0.4 seconds
  • Dual scenario: 0.5-0.6 seconds
  • Triple scenario: 0.7-0.8 seconds

• Support Scale:

  • Locations: 2-10
  • Scenarios: 1-3
  • Concurrent users: 100+

🛣️ Roadmap

v1.1.0 (Planned)

• User account system
• History saving
• Favorites feature
• Share recommendations

v1.2.0 (Planned)

• Machine learning recommendations
• Real-time traffic information
• Weather data integration
• Mobile app

v2.0.0 (Long-term)

• AR navigation
• Voice interaction
• Internationalization
• Enterprise features

🤝 Contributing

We welcome all forms of contributions! Please see CONTRIBUTING.md for details.

📄 License

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

📞 Contact

• 📧 Email: [email protected]
• 🐛 Bug Reports: GitHub Issues
• 💬 Discussions: GitHub Discussions

🙏 Acknowledgments

• Amap - Providing geocoding and POI search services
• FastAPI - Excellent web framework
• Boxicons - Beautiful icon library

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Made with ❤️ by JasonRobertDestiny