title	RLM Interactive Console
emoji	🚀
colorFrom	indigo
colorTo	purple
sdk	docker
pinned	false

RLM Interactive Console

The RLM Interactive Console is a full-stack application designed to demonstrate and interact with Reinforcement Learning Models (or similar agentic systems). It features a generic FastAPI backend for handling model inference and dataset management, coupled with a modern Next.js frontend for an interactive user experience.

✨ Features

• Interactive Chat Interface: user-friendly chat UI to interact with models.
• Dataset Integration: Fetches and caches datasets from Hugging Face (e.g., oolongbench/oolong-real).
• Response Caching: Caches model responses to local JSON files to improve performance and avoid redundant computation.
• Agentic Workflow: Integrates with smolagents for agent-based reasoning.
• Real-time Feedback: Displays thinking process and final answers.

🛠 Tech Stack

• Frontend: Next.js 15, React 19, TailwindCSS, TypeScript.
• Backend: FastAPI, Python 3.12+, Uvicorn.
• AI/ML: smolagents, openenv, datasets, Hugging Face Hub.
• Package Management: npm (frontend), uv or pip (backend).

🚀 Getting Started

Prerequisites

• Node.js (v18+ recommended)
• Python (v3.12+)
• Git

1. Clone the Repository

git clone <your-repo-url>
cd RLM-Demo

2. Backend Setup

The backend is located in the backend/ directory.

Create a Virtual Environment

It is recommended to use uv for fast package management, but standard pip works as well.

Using uv (Recommended):

# Install uv if you haven't already
pip install uv

# Create virtual environment and sync dependencies
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install -r backend/requirements.txt

Using standard pip:

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

Environment Variables

Create a .env file in the root (or backend/ depending on where you run it) with the following variables:

HF_TOKEN=your_hugging_face_token
SPACE_URL=optional_space_url
MODEL_NAME=meta-llama/Llama-3.1-70B-Instruct
DATASET_SUBSET=default
DATASET_SPLIT=test
EXAMPLE_INDEX=0
MAX_ITERATIONS=10
CUTOFF_INDEX=15

3. Frontend Setup

The frontend is located in the frontend/ directory.

cd frontend
npm install

🏃‍♂️ Running the Application

Development Mode

Run the backend and frontend in separate terminals.

Terminal 1: Backend

# From the root directory
source .venv/bin/activate
uvicorn backend.main:app --reload --port 8000

Terminal 2: Frontend

cd frontend
npm run dev

Open http://localhost:3000 to view the application.

Note: The Next.js app is configured to proxy API requests to http://localhost:8000 or expects the backend to server the frontend in production.

Production (Docker)

The project includes a Dockerfile for easy deployment, compatible with Hugging Face Spaces.

docker build -t rlm-demo .
docker run -p 7860:7860 rlm-demo

📂 Project Structure

RLM-Demo/
├── backend/                # FastAPI backend
│   ├── main.py             # App entry point
│   ├── repl_process.py     # Agent logic
│   ├── data/               # Cached datasets
│   ├── answer/             # Cached answers
│   └── requirements.txt    # Python dependencies
├── frontend/               # Next.js frontend
│   ├── app/                # App router (pages & layouts)
│   ├── components/         # React components
│   └── package.json        # Frontend dependencies
├── Dockerfile              # Deployment configuration
└── README.md               # Project documentation