title: Velra Intelligent Dating
emoji: 💖
colorFrom: pink
colorTo: purple
sdk: docker
app_port: 7860
💖 Velra – AI Relationship Co-Pilot
Velra is an elite AI-powered psychological profiler for modern dating. It analyzes text conversations and chat screenshots, predicts emotional compatibility, detects contradictions, and provides actionable relationship advice without using generic therapy terminology.
🚀 Features
- 💬 Chat + Screenshot Analysis: Upload raw text or screenshots (powered by OCR)
- 🧠 Elite Emotional Intelligence: Extracts true intent, power dynamics, and emotional asymmetry.
- ⚠️ Risk & Contradiction Detection: Calls out discrepancies between what people say and what they do.
- 🎯 Perspective-Aware Strategy: Adjusts advice based on exactly what you want (casual, commitment, detachment).
- 💘 Actionable Replies: Generates exactly calibrated text messages to send.
- 🤖 Multi-Agent System: Powered by specialized AI agents for Analysis, Psychology, and Strategy.
📂 Project Structure
velra/
│
├── frontend/
│ ├── app.py # Main Streamlit UI entry point
│ ├── shared.py # Shared UI components and logic
│ └── pages/
│ └── results.py # Result visualization page
│
├── backend/
│ ├── app.py # FastAPI server handling agent orchestration
│ ├── core/ # App configuration and logging
│ ├── llm/ # LLM factory and provider setup
│ ├── services/ # External services (OCR engine)
│ ├── utils.py # JSON parsing and utilities
│ └── agents/ # Core AI Multi-Agent Architecture
│ ├── perspective.py # Fast perspective and role identification
│ ├── analyst.py # Behavioral analysis and screenshot grounding
│ ├── psychology.py # Attachment, intent alignment, and dynamics
│ └── strategy.py # High-value, objective-based action plans
│
├── requirements.txt # Python dependencies
├── .env # Environment variables (Keys & Providers)
└── README.md # Project documentation
🧠 Architecture Flow
Frontend (Streamlit) → Backend (FastAPI) → OCR Engine (Tesseract) → Agents (Perspective → Analyst + Psychology → Strategy) → LLM Inference (AMD/OpenAI)
⚙️ Setup & Local Development
1. Create Environment
python -m venv velra_env
# Windows:
velra_env\Scripts\activate
# Mac/Linux:
source velra_env/bin/activate
2. Install Dependencies
pip install -r requirements.txt
3. Setup Environment Variables
Create a .env file in the root directory:
LLM_PROVIDER=openai
MODEL_NAME=gpt-4o-mini
OPENAI_API_KEY=your_api_key_here
4. Run the Backend (FastAPI)
uvicorn backend.app:app --reload
5. Run the Frontend (Streamlit)
Open a new terminal window, activate the environment, and run:
streamlit run frontend/app.py
Note: If deploying to Hugging Face Spaces, the platform will automatically read the app_file parameter in the header and launch the Streamlit frontend. The backend FastAPI logic is currently separated; for a pure single-container HF Space, ensure both services run or migrate the backend logic into the Streamlit lifecycle if needed.