feat(api): add sentence processing endpoint

Author: xaviml

.gitignore

@@ -129,4 +129,7 @@ dmypy.json
 .pyre/
 
 #VSCode
-.vscode/
+.vscode/
+
+# Junit
+junit/

Dockerfile

@@ -10,6 +10,7 @@ WORKDIR /usr/src/example-api
 COPY . .
 
 RUN pipenv install
+RUN pipenv run spacy download en_core_web_sm
 
 EXPOSE 5000
 

Pipfile

@@ -9,6 +9,7 @@ python_version = "3.8"
 [packages]
 fastapi = "*"
 uvicorn = "*"
+spacy = "*"
 
 [dev-packages]
 pytest = "*"

Pipfile.lock

@@ -1,14 +1,19 @@
+[![azure-pipelines-build](https://img.shields.io/azure-devops/build/xaviml93/ControllerX/3/master.svg?style=for-the-badge)](https://dev.azure.com/xaviml93/ControllerX/_build/latest?definitionId=3&branchName=master)
+[![azure-pipelines-coverage](https://img.shields.io/azure-devops/coverage/xaviml93/ControllerX/3/master.svg?style=for-the-badge)](https://dev.azure.com/xaviml93/ControllerX/_build/latest?definitionId=3&branchName=master)
+[![last-release](https://img.shields.io/github/v/release/xaviml/example-api.svg?style=for-the-badge)](https://github.com/xaviml/controllerx/releases)
+
 # Example API
 
 This project was created for the purpose of an interview test. Its goal is to build an API endpoint that returns name entities given a sentence as an input.
 
 ## Dependencies
 
 - FastAPI: Web framework to build APIs with Python
+- spaCY: Library for NLP in Python
 - pre-commit: It allows me to run some checks before a git commit is done
 - commitizen: It gives a standard way to commit code and also allows me to version the code
 - pytest: Used to run the tests
-- Azure pipelines: Used to run a pipeline everytime there is a commit o a new tag is added for a new release with automated release notes.
+- Azure pipelines: Used to run a pipeline everytime there is a commit o a new tag is added for a new release with automated release notes
 
 ## Install
 
@@ -17,13 +22,14 @@ Install [pipenv](https://pipenv.kennethreitz.org/en/latest/install/#installing-p
 ```
 pipenv install --dev
 pipenv run pre-commit install
+pipenv run spacy download en_core_web_sm
 ```
 
-### Run
+## Run
 
 There are two ways to run the project, via Docker or through python virtual environment.
 
-## With docker
+### With docker
 
 If we want to run with Docker, we first need to build an image:
 
@@ -33,26 +39,43 @@ And then run an instance:
 
 `docker run -i -d -p 8080:5000 example-api`
 
-## Without docker
+### Without docker
 
 However, if we want to run without Docker, we can run directly with uvicorn:
 
 ```
 pipenv run uvicorn --host 0.0.0.0 --port 5000 app.main:app
 ```
 
-### Tests
+## Tests
 
 Run this command to run tests with coverage:
 
-`python -m pytest --cov=app`
+`pipenv run pytest --cov=app`
+
+## Formatting
+
+Run the following command to format all the project:
 
-### Deploying
+`pipenv run pre-commit run --all-files`
 
-By running `cz bump` it automatically detecs the version to bump - major, minor or patch. It follows [SemVer](https://semver.org/) standard. Then when pushin the code to GitHub, it needs to be done like this:
+## Deploying
+
+By running `cz bump` it automatically detecs the version to bump - major, minor or patch. It follows [SemVer](https://semver.org/) standard. Then when pushing the code to GitHub, it needs to be done like this:
 
 ```
 git push origin master --tags
 ```
 
 It will then push the code and the Azure Pipeline will create the GitHub release if the pipeline passes.
+
+### API and documentation
+
+The API currently has 2 endpoints:
+
+- `/api/v1/sentence/process`: It processess a sentence
+- `/check`: It checks the system is running
+
+Furthermore, FastAPI automatically generates the documentation for you, so when running you can access to `/docs` and you will be able to see in detail all the endpoints with its I/O.
+
+For the sentence processing, I hace used `spacy`, a free open-source library for NLP in Python. [These](https://spacy.io/api/annotation#named-entities) are the named entity types that can be sent by the API.

README.md

@@ -0,0 +1,7 @@
+from fastapi import APIRouter
+
+from app.api.api_v1.endpoints import sentence
+
+api_router = APIRouter()
+
+api_router.include_router(sentence.router, prefix="/sentence", tags=["sentence"])

app/__init__.py

@@ -0,0 +1,21 @@
+import spacy
+from fastapi import APIRouter
+from starlette.responses import JSONResponse
+
+from app.models.sentence import NamedEntity, ProcessedSentence, Sentence
+
+router = APIRouter()
+
+nlp = spacy.load("en_core_web_sm")
+
+
+@router.post("/process", response_model=ProcessedSentence)
+def extract_name(sentence: Sentence):
+    """
+    This endpoint returns a processed sentence
+    """
+    doc = nlp(sentence.sentence)
+    named_entities = [
+        NamedEntity(entity=ent.text, entity_type=ent.label_) for ent in doc.ents
+    ]
+    return ProcessedSentence(named_entities=named_entities)