RNN Question Answering API

A simple RNN-based Question Answering system built using Pytorch + FastAPI, with Docker support

Workflow

• Train model using train.py
• Model + vocab stored in config/
• API loads model and serves prediction

Project Structure

rnn-qa/  
│  
├── config/     # model artifacts    
├── data/       # sample-dataset  
│  
├── .gitignore  
├── .dockerignore  
│  
├── Dockerfile      # continer setup   
│  
├── inference.py    # prediction logic  
├── main.py         # FastAPI app / entrypoint  
├── model.py        # RNN model architecture  
│  
├── requirements.txt   
│  
├── tokenizer.py       # text processing  
├── train.py           # training script  
└── utils.py           # helper functions  

Running Locally

# Start API  

uvicorn main:app --reload

# Base URL  

http://localhost:8080

# Example Request  

curl --location 'localhost:8000/predict' \
--header 'Content-Type: application/json' \
--data '{
    "question": "What is the capital of France"
}'

# Example Response   

{
    "answer": "Paris",
    "confidence": 0.85
}

Docker Image

Prebuilt Docker image available with 600+ pulls on Docker Hub
https://hub.docker.com/r/ssnym/rnn-qa

Acknowledgements

Tutorial Followed : FastAPI course by CampusX