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michael 6ee80e67aa Create fresh LLM clients per call instead of caching
The previous event loop tracking approach still caused issues - when replacing
a cached client, its garbage collection triggers aclose() which tries to close
the aiohttp session on the wrong event loop.

Simplest fix: create a fresh client for each call. The overhead is minimal
compared to the actual LLM API call, and this completely avoids all event
loop mismatch issues in ASGI environments.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2025-12-19 16:56:36 -06:00
.claude added full persona profile export in bulk actions menu (CSV, JSON and Markdown formats) 2025-09-10 22:38:35 -05:00
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Semblance Synthetic Society

A platform for creating and managing synthetic personas for focus groups and market research.

Project info

URL: https://lovable.dev/projects/ee7a424f-7f6c-4b5d-9645-e66074cea7d3

Features

  • Create and manage synthetic personas with detailed profiles
  • Organize personas into focus groups
  • Run interactive focus group sessions
  • Analyze results and extract insights
  • MongoDB-based backend for data persistence
  • User authentication and access control

Getting Started

Prerequisites

  • Node.js & npm installed - install with nvm
  • Python 3.8+ installed for the backend
  • MongoDB installed and running locally (default configuration: mongodb://localhost:27017)

Installation

# Step 1: Clone the repository
git clone <YOUR_GIT_URL>

# Step 2: Navigate to the project directory
cd <YOUR_PROJECT_NAME>

# Step 3: Install frontend dependencies
npm install

# Step 4: Install backend dependencies
cd backend
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt
cd ..

Running the Application

Use the provided start script to run both frontend and backend:

./start.sh

The start script will:

  1. Check for and start MongoDB if needed
  2. Set up the Python virtual environment
  3. Install dependencies
  4. Populate the database with sample personas and focus groups
  5. Start both the backend and frontend servers

Or run them separately:

# Start the backend
cd backend
source venv/bin/activate
python run.py

# In another terminal, run the frontend
npm run dev

The frontend will be available at http://localhost:5173 The backend API is available at http://localhost:5137/api

Default Login

  • Username: user
  • Password: pass

Technology Stack

Frontend

  • Vite
  • TypeScript
  • React
  • React Router
  • shadcn-ui
  • Tailwind CSS
  • Axios for API requests

Backend

  • Python
  • Flask
  • PyMongo (MongoDB client)
  • JWT for authentication

Project Structure

  • /src: Frontend source code
    • /components: React components
    • /contexts: React contexts for state management
    • /hooks: Custom React hooks
    • /lib: Utility functions and API client
    • /pages: Main application pages
    • /types: TypeScript type definitions
  • /backend: Python backend
    • /app: Flask application
      • /models: Database models
      • /routes: API endpoints
    • run.py: Backend entry point

Deployment

The application is configured to be deployed at the /semblance/ path. For hosting:

  1. Build the frontend:

    npm run build
    
  2. Deploy the backend using a WSGI server like Gunicorn:

    cd backend
    gunicorn -w 4 "app:create_app()"
    

Contributing

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