🎉 Gather-Up-AI - Intelligent Event Planning Assistant

Gather-Up-AI is a microservices-based event planning platform that leverages AI to help users find the perfect vendors and venues for their events. Using natural language processing and intelligent recommendation systems, it simplifies the event planning process.

🌟 Features

🤖 AI-Powered Recommendations: Uses cloud-based LLM and RAG (Retrieval-Augmented Generation) for intelligent vendor suggestions
📍 Smart Location Search: Integrates with Google Places API to find ideal event venues
🎨 AI Image Generation: 🆕 ComfyUI + Ollama Integration - Fast, high-quality image generation with Z-Image Turbo (4-step generation, ~10s)
🤖 Intelligent Prompt Enhancement: Uses Ollama LLM to transform simple prompts into detailed, optimized descriptions
☁️ Cloud Storage: Automatic Cloudinary integration for CDN-delivered images
🔗 Microservices Architecture: Scalable, modular design with separate services for different functionalities
🚀 FastAPI Backend: High-performance async API services
🧠 Vector Search: Semantic similarity search using sentence transformers
🎯 Event Context Awareness: Image generation understands event themes, colors, and mood

🆕 NEW: Image Generation Service

Generate stunning event images with AI in seconds!

Quick Start:

Install Ollama: winget install Ollama.Ollama
Get model: ollama pull llama3.2
Configure Cloudinary in .env
Start service: cd services/image-service && .\start-service.ps1

Features:

⚡ Fast: ~10-15 seconds per image
🎨 High Quality: Z-Image Turbo model optimized for speed
🤖 Smart: Ollama LLM enhances prompts automatically
☁️ Cloud-Ready: Cloudinary CDN integration
🎯 Context-Aware: Understands your event details

Documentation:

📁 Project Structure

Gather-Up-AI/
│
├── services/
│   ├── api-gateway/              # Main entry point - Routes requests to services
│   │   ├── main.py
│   │   ├── requirements.txt
│   │   ├── .env.example
│   │   ├── .env
│   │   └── tests/
│   │       └── test_main.py
│   │
│   ├── location-service/         # Venue/location search using Google Places API
│   │   ├── main.py
│   │   ├── requirements.txt
│   │   ├── schemas.py
│   │   ├── .env.example
│   │   ├── routes/
│   │   │   └── location_routes.py
│   │   ├── services/
│   │   │   └── places_service.py
│   │   └── tests/
│   │       └── test_main.py
│   │
│   ├── vendor-service/           # RAG-based vendor recommendations
│   │   ├── main.py
│   │   ├── requirements.txt
│   │   ├── database.py
│   │   ├── schemas.py
│   │   ├── .env.example
│   │   ├── .env
│   │   ├── routes/
│   │   │   └── vendor_routes.py
│   │   ├── services/
│   │   │   ├── llama_service.py
│   │   │   └── vector_service.py
│   │   └── tests/
│   │       └── test_main.py
│   │
│   └── image-service/            # 🆕 AI Image Generation with SDXL
│       ├── main.py
│       ├── requirements.txt
│       ├── schemas.py
│       ├── .env
│       ├── routes/
│       │   └── image_routes.py
│       ├── services/
│       │   ├── comfyui_service.py
│       │   └── llama_service.py
│       ├── test_service.py
│       └── README.md
│
├── .venv/
├── start_all.py                  # 🔥 One-click launcher for all services
├── COMFYUI_SETUP.md             # 🎨 ComfyUI installation guide
├── .gitignore
└── README.md

🏗️ Architecture

API Gateway (Port: 8080)

Central entry point for all client requests
Routes requests to appropriate microservices
Handles CORS and request/response aggregation
Supports image generation endpoints

Vendor Service (Port: 8001)

Manages vendor data in MongoDB
Uses sentence transformers for semantic search
Implements RAG pattern with LLM for intelligent recommendations
Vector similarity search for matching user requirements

Location Service (Port: 8002)

Integrates with Google Places API
Searches for venues based on location and event type
Returns detailed venue information including ratings and contact details

Image Service (Port: 8003) 🆕

AI-powered image generation using ComfyUI
Ollama LLM for intelligent prompt enhancement
Z-Image Turbo model for fast generation
Cloudinary integration for cloud storage
Event context-aware image generation
Your ComfyUI Setup: Already configured at http://127.0.0.1:8000

Image Service (Port: 8000) 🆕

AI-powered image generation using Zephyr Image Turbo (cloud-optimized model)
Prompt enhancement with cloud-based LLM for better results
Real-time progress streaming via Server-Sent Events
Integration with ComfyUI for professional image generation
Supports customizable image generation with modern turbo diffusion
Uses qwen_3_4b text encoder for superior text understanding

🛠️ Technology Stack

Framework: FastAPI
Language: Python 3.10+
Database: MongoDB (Vendor Service)
AI/ML:
- PyTorch
- Sentence Transformers
- Hugging Face Transformers
- Zephyr Image Turbo (via ComfyUI)
- Cloud-based LLM via Ollama (glm-4.6:cloud)
- qwen_3_4b text encoder
External APIs: Google Places API, Ollama Cloud API
Image Generation: ComfyUI with Zephyr Image Turbo
HTTP Client: HTTPX, Requests, aiohttp
Environment Management: python-dotenv
Testing: pytest, pytest-asyncio, pytest-cov
CI/CD: GitHub Actions

🚀 Quick Start

Prerequisites

Python 3.10+ installed
MongoDB instance (for vendor service)
Google Places API key (for location service)
ComfyUI with Zephyr Image Turbo models (for image service)
- qwen_3_4b.safetensors (text encoder)
- z_image_turbo_bf16.safetensors (diffusion model)
- ae.safetensors (VAE)
Cloud-based Ollama API access (or local Ollama with models)

1️⃣ Clone the Repository

git clone https://github.com/Gather-Up/Gather-Up-AI
cd Gather-Up-AI

2️⃣ Create Virtual Environment

python -m venv .venv

3️⃣ Activate Virtual Environment

Windows PowerShell:

.\.venv\Scripts\Activate.ps1

Command Prompt:

.venv\Scripts\activate

macOS/Linux:

source .venv/bin/activate

You should see (.venv) before your terminal prompt.

4️⃣ Install Dependencies

Install all service dependencies into the shared virtual environment:

pip install -r services/api-gateway/requirements.txt
pip install -r services/location-service/requirements.txt
pip install -r services/vendor-service/requirements.txt
pip install -r services/image-service/requirements.txt

Note: For GPU support with PyTorch, use:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu129

5️⃣ Configure Environment Variables

Each service has an .env.example file. Copy and configure them:

API Gateway:

cd services/api-gateway
cp .env.example .env
# Edit .env with your configuration

Location Service:

cd services/location-service
cp .env.example .env
# Add your GOOGLE_PLACES_API_KEY

Vendor Service:

cd services/vendor-service
cp .env.example .env
# Add your MONGODB_URI and other configurations

Image Service: 🆕

cd services/image-service
# .env already created - verify COMFYUI_URL and OLLAMA_API_URL

5.5️⃣ Setup ComfyUI (for Image Service) 🎨

Required models for Zephyr Image Turbo:

Text Encoder: qwen_3_4b.safetensors
Diffusion Model: z_image_turbo_bf16.safetensors
VAE: ae.safetensors

Place models in:

ComfyUI/models/
├── text_encoders/qwen_3_4b.safetensors
├── diffusion_models/z_image_turbo_bf16.safetensors
└── vae/ae.safetensors

Start ComfyUI on port 8000 and verify at http://localhost:8000

6️⃣ Run the Services

Option A: One-Click Launcher 🔥 RECOMMENDED

# From project root
python start_all.py

This will open separate windows for:

API Gateway (8000)
Vendor Service (8001)
Location Service (8002)
Image Service (8000)

Option B: Manual Start

Open 4 separate terminal windows, activate the virtual environment in each, and run:

Terminal 1 - API Gateway:

cd services/api-gateway
python main.py

Runs on: http://localhost:8000

Terminal 2 - Vendor Service:

cd services/vendor-service
python main.py

Runs on: http://localhost:8001

Terminal 3 - Location Service:

cd services/location-service
python main.py

Runs on: http://localhost:8002

📡 API Endpoints

API Gateway (`:8000`)

GET / - Root endpoint
GET /health - Health check for all services
POST /plan-event - Main event planning endpoint (natural language input)

Vendor Service (`:8001`)

GET / - Service info
GET /health - Health check
POST /api/vendors/recommend - Get vendor recommendations
POST /api/vendors - Add new vendor
GET /api/vendors - List all vendors

Location Service (`:8002`)

GET / - Service info
GET /health - Health check
POST /api/locations/search - Search venues by location and type

🔧 Development

Adding New Dependencies

After installing new packages:

pip freeze > services/<service-name>/requirements.txt

Running Individual Tests

To run a specific test file:

cd services/<service-name>
pytest tests/test_main.py -v

To run tests with detailed output:

pytest tests/ -v -s

Code Style

Follow PEP 8 guidelines
Use type hints where possible
Document functions with docstrings

🐛 Troubleshooting

Virtual Environment Issues

If you move or rename the project folder, recreate the virtual environment:

Remove-Item -Recurse -Force .venv
python -m venv .venv
.\.venv\Scripts\Activate.ps1
# Reinstall dependencies

Port Already in Use

Change the port in the respective service's .env file:

API_GATEWAY_PORT=8000
VENDOR_SERVICE_PORT=8001
LOCATION_SERVICE_PORT=8002

MongoDB Connection Issues

Ensure MongoDB is running and the MONGODB_URI in .env is correct.

📚 Additional Resources

👥 Contributing

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

📄 License

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

👤 Author

Sachitha Samadhi

GitHub: @Gather-Up

🙏 Acknowledgments

Google Places API for venue data
Hugging Face for transformer models
FastAPI framework for the excellent async support

Screenshots

📝 Notes

The shared .venv ensures consistency and avoids duplicate installations
Do not move the .venv folder after creation — paths inside it are absolute
All services must be running simultaneously for full functionality

Happy Event Planning! 🎊

Name		Name	Last commit message	Last commit date
Latest commit History 35 Commits
api-gateway		api-gateway
services		services
.gitignore		.gitignore
README.md		README.md
check_services.py		check_services.py
start_all.py		start_all.py
start_services.py		start_services.py
stop_services.py		stop_services.py
test_image_generation.py		test_image_generation.py
test_streaming_endpoint.py		test_streaming_endpoint.py

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