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Accompanying code to www.shanelynn.ie tutorial on FastAPI NLP API creation. In this simple API, we're building a web application that can process text in HTTP requests to extract sentiment and entities automatically.

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FastAPI NLP API - Sentiment Analysis and Entity Detection API

Accompanying code to www.shanelynn.ie tutorial on FastAPI NLP API creation.

In this simple API, we're building a web application that can process text in HTTP requests to extract sentiment and entities automatically.

Installation

Start a virtual environment (python3 -m venv venv) and install the requirements in that virtual environment (FastAPI, Spacy, Flair, Uvicorn[standard]). This tutorial was built with Python 3.9 originally.

pip install -r requirements.txt

You'll also need to install language models for Spacy:

python -m spacy download en_core_web_sm

Running the main API

Start the main API with:

uvicorn simple_api:app --reload

The first run may take longer than usual if Flair has to download any models to run.

Examples

There are some basic examples (from the blog) in the examples/ directory showing:

  • Basic GET requests with FastAPI simple_api.py
  • Basic POST API with FastAPI simple_post_route.py
  • Sentiment detection with Flair alone flair_sentiment_sample.py
  • Named entity recognition (NER) with Spacy spacy_ner_example.py

Run these with simple python examples/<example_filename.py>.

Testing with cURL

You can test the application with curl commands. Send a request with the text of your choosing to http://localhost:8000 when the API is running.

curl -X POST -H "Content-Type: application/json" -d '{"query": "I absolutely love building Python applications with FastAPI and Shane's blog."}' http://localhost:8000/analysis/

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Accompanying code to www.shanelynn.ie tutorial on FastAPI NLP API creation. In this simple API, we're building a web application that can process text in HTTP requests to extract sentiment and entities automatically.

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