readme.md

metadata

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