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---
title: GDG Aranjuez - Backend inference endpoints docker
emoji: 📊
colorFrom: yellow
colorTo: red
sdk: docker
pinned: false
license: apache-2.0
short_description: Creamos un backend con docker, langchain, fastapi e inferende endpoints
app_port: 7860
---
# SmolLM2 Backend
This project implements a FastAPI API that uses LangChain and LangGraph to generate text with the Qwen2.5-72B-Instruct model from HuggingFace.
## Configuration
### In HuggingFace Spaces
This project is designed to run in HuggingFace Spaces. To configure it:
1. Create a new Space in HuggingFace with SDK Docker
2. Configure the `HUGGINGFACE_TOKEN` or `HF_TOKEN` environment variable in the Space configuration:
- Go to the "Settings" tab of your Space
- Scroll down to the "Repository secrets" section
- Add a new variable with the name `HUGGINGFACE_TOKEN` and your token as the value
- Save the changes
### Local development
For local development:
1. Clone this repository
2. Create a `.env` file in the project root with your HuggingFace token:
``
HUGGINGFACE_TOKEN=your_token_here
``
3. Install the dependencies:
``
pip install -r requirements.txt
``
## Local execution
``bash
uvicorn app:app --reload
``
The API will be available at `http://localhost:8000`.
## Endpoints
### GET `/`
Welcome endpoint that returns a greeting message.
### POST `/generate`
Endpoint to generate text using the language model.
**Request parameters:**
``json
{
"query": "Your question here",
"thread_id": "optional_thread_identifier"
}
``
**Response:**
``json
{
"generated_text": "Generated text by the model",
"thread_id": "thread identifier"
}
``
## Docker
To run the application in a Docker container:
``bash
# Build the image
docker build -t smollm2-backend .
# Run the container
docker run -p 8000:8000 --env-file .env smollm2-backend
``
## API documentation
The interactive API documentation is available at:
- Swagger UI: `http://localhost:8000/docs`
- ReDoc: `http://localhost:8000/redoc` |