🚀 EkoMeet

AI-Powered Meeting Spot Recommendation System

Intelligent meeting point recommendation system based on Eko AI framework, featuring multi-agent collaboration and natural language-driven location suggestions.

🎯 AI-Driven Intelligence | 🤖 Multi-Agent Architecture | 🌐 Natural Language Interface

✨ Core Features

• 🤖 Multi-Agent Intelligence - Distributed AI architecture based on Eko framework
• 🌍 Smart Geographic Analysis - Integrated with Amap API for precise location calculations
• 💬 Natural Language Interaction - Describe needs in simple language, AI understands intelligently
• 🎯 Personalized Recommendations - Smart ranking algorithm based on user preferences
• 📱 Responsive Interface - Modern UI adapted for PC and mobile devices
• ⚡ Real-time Processing - Asynchronous processing for fast responses

🏗️ Technical Architecture

Frontend (HTML/CSS/JS) 
    ↓
FastAPI Backend
    ↓
Eko AI Framework
    ↓
Multi-Agent System
    ↓
- LocationAgent (Geographic Processing)
- POISearchAgent (Venue Search)  
- RecommendationAgent (Smart Recommendations)
- VisualizationAgent (Result Visualization)

🛠️ Tech Stack

• Backend: Python + FastAPI
• AI Framework: Eko AI Framework
• LLM: OpenRouter (Claude-3.5-Sonnet)
• Map Services: Amap (Gaode Map) API
• Frontend: HTML5 + CSS3 + JavaScript
• Type System: TypeScript (Tool Development)

Main Interface

Multi-Venue Selection

Recommendation Results

Detailed Information

🌟 Features

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

✨ Key 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 Enhancements

• ✅ 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/EkoMeet.git
cd EkoMeet

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
}

🔧 Development

Environment Setup

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
venv\Scripts\activate     # Windows

# Install dependencies
pip install -r requirements.txt

Code Standards

• Follow PEP8 Python coding standards
• Use type hints for better code documentation
• Write comprehensive docstrings
• Format code before committing

🚀 Deployment

Local Development

# Start development server
python web_server.py

# Or using npm scripts
npm run dev

Production Deployment

Supported deployment platforms:

• Render: Using render.yaml configuration
• Railway: Auto-detection deployment
• Docker: Containerized deployment
• Traditional Server: Using Nginx + Gunicorn

Environment Variables

Required environment variables:

• AMAP_API_KEY: Amap (Gaode Map) API key
• PORT: Service port (default 8000)

Optional environment variables:

• PYTHON_ENV: Runtime environment (development/production)
• LOG_LEVEL: Logging level (default info)

🤝 Contributing

We welcome contributions of all kinds!

How to Contribute

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

Bug Reports

If you find a bug or have a feature suggestion:

1. Check existing Issues
2. Create a detailed Issue description
3. Provide reproduction steps and environment info

📄 License

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

📞 Contact

• Project Link: https://github.com/JasonRobertDestiny/EkoMeet
• Bug Reports: Issues
• Feature Requests: Discussions

🙏 Acknowledgments

• Eko AI Framework - Powerful AI framework support
• Amap (Gaode Map) API - Precise geographic information services
• FastAPI - Modern web framework
• Boxicons - Beautiful icon library

📈 Star History

---

If this project helps you, please give it a ⭐️!