Improvements

.github/workflows/flake8.yml

@@ -0,0 +1,36 @@
+# This workflow will install Python dependencies, run tests and lint with a single version of Python
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: Python application
+
+on: [push, pull_request]
+
+permissions:
+  contents: read
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v3
+    - name: Set up Python 3.10
+      uses: actions/setup-python@v3
+      with:
+        python-version: '3.10'
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install flake8 pytest
+        pip install -r requirements.txt
+    - name: Lint with flake8
+      run: |
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with pytest
+      run: |
+        pytest 

.gitignore

@@ -0,0 +1,8 @@
+# Python files
+*.pyc
+__pycache__/
+.pytest_cache/
+*.pyo
+
+# Virtual environment
+.venv/

README.md

@@ -1,8 +1,30 @@
 [![Tests](https://github.com/tokarevsas31/ml_fastapi_tests/actions/workflows/python-app.yml/badge.svg)](https://github.com/tokarevsas31/ml_fastapi_tests/actions/workflows/python-app.yml)
 
-# An example of ML Application with the pretrained model and test.
+# FastAPI Sentiment Analysis using the pretrained model
 
-An example of English text tone detection with [Hugging Face](https://huggingface.co/) library.
 
+## Description
 
-Tests GitHub Actions
+An example of English text tone detection. The sentiment analysis is performed using the [Hugging Face](https://huggingface.co/) Transformers library, specifically, a pre-trained sentiment analysis model.
+
+### Launch
+
+1. Clone this repository and navigate to the project folder
+2. Install the necessary dependencies
+```
+python -m pip install --upgrade pip
+pip install -r requirements.txt
+```
+3. Launch the server
+```
+uvicorn main:app
+```
+4. The web application will be deployed at ```http://127.0.0.1:8000```
+5. Requests:
+    - `/`: Root GET-request returns a «Hello World» message
+    - `/predict/` A POST-request accepts a text string and returns the sentiment of the text. The sentiment is categorized into "positive" or "negative"
+6. Terminal usage
+    - To use the sentiment analysis API, you need to send a POST-request to the `/predict/` endpoint with the text you want to analyze. The text must be sent in the request body as a JSON object with the `text` key.
+```
+curl -X 'POST' 'http://127.0.0.1:8000/predict/' -H 'Content-Type: application/json' -d '{"text":"YOUR TEXT"}
+```

main.py

@@ -7,11 +7,14 @@ class Item(BaseModel):
     text: str
 
 
-
 app = FastAPI()
 classifier = pipeline("sentiment-analysis")
 
 
+@app.get("/just_another_api/")
+def new_api():
+    return {"wow": "impressive"}
+
 
 @app.get("/")
 def root():

requirements.txt

@@ -1,6 +1,7 @@
 --extra-index-url https://download.pytorch.org/whl/cpu
 fastapi
+httpx
 uvicorn
-transformers
 torch
-httpx
+transformers
+

test_main.py

@@ -4,10 +4,16 @@
 client = TestClient(app)
 
 
+def test_just_another_api():
+    response = client.get("/just_another_api/")
+    assert response.status_code == 200
+    assert response.json() == {"wow": "impressive"}
+
+
 def test_read_main():
     response = client.get("/")
     assert response.status_code == 200
-    assert response.json() == {"message": "World"}
+    assert response.json() == {"message": "Hello World"}
 
 
 def test_predict_positive():
@@ -24,3 +30,9 @@ def test_predict_negative():
     json_data = response.json()
     assert response.status_code == 200
     assert json_data['label'] == 'NEGATIVE'
+
+
+def test_predict_allowed():
+    response = client.get("/predict/")
+    assert response.status_code == 405
+    assert response.json() == {"detail": "Method Not Allowed"}