Improvements

.github/workflows/python-lint.yml

@@ -0,0 +1,39 @@
+# 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: flake8
+
+on:
+  push:
+    branches: [ "master", "improvements" ]
+  pull_request:
+    branches: [ "master"]
+
+permissions:
+  contents: read
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - 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
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    - 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

README.md

@@ -6,3 +6,18 @@ An example of English text tone detection with [Hugging Face](https://huggingfac
 
 
 Tests GitHub Actions
+
+# Описание
+
+Данный репозиторий представляет пример создания приложения по определению тональности текста
+
+# Работа с приложением
+
+1. Склонируйте данный репозиторий
+2. Установите необходимые зависимости ```pip install -r requirements.txt```
+3. Запустите веб-приложение ```uvicorn main:app```
+4. Веб приложение будет развернуто по адресу ```http://localhost:8000```
+5. Проект имеет две точки доступа:
+    - при отправке GET-запроса по адресу ```/``` будет возвращен json с текстом «Hello World»
+    - при отправке POST-запроса по адресу ```/predict/``` с передачей параметра «text»
+     в теле запроса, в котором передается текст для определения тональности

main.py

@@ -7,17 +7,28 @@ class Item(BaseModel):
     text: str
 
 
-
 app = FastAPI()
 classifier = pipeline("sentiment-analysis")
 
 
-
 @app.get("/")
 def root():
+    """Handler for a GET request to the root URL
+
+    Returns:
+        dict: Dictionary with "Hello World"
+    """
     return {"message": "Hello World"}
 
 
 @app.post("/predict/")
 def predict(item: Item):
+    """Handler for POST request to /predict/
+
+    Args:
+        item (Item): Item object containing text for sentiment analysis
+
+    Returns:
+        dict: Dictionary with predicted mood of the text
+    """
     return classifier(item.text)[0]

requirements.txt

@@ -4,3 +4,5 @@ uvicorn
 transformers
 torch
 httpx
+pandas
+pytest

test_main.py

@@ -7,20 +7,24 @@
 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():
-    response = client.post("/predict/",
-                           json={"text": "I like machine learning!"})
+    response = client.post("/predict/", json={"text": "I like machine learning!"})
     json_data = response.json()
     assert response.status_code == 200
-    assert json_data['label'] == 'POSITIVE'
+    assert json_data["label"] == "POSITIVE"
 
 
 def test_predict_negative():
-    response = client.post("/predict/",
-                           json={"text": "I hate machine learning!"})
+    response = client.post("/predict/", json={"text": "I hate machine learning!"})
     json_data = response.json()
     assert response.status_code == 200
-    assert json_data['label'] == 'NEGATIVE'
+    assert json_data["label"] == "NEGATIVE"
+
+
+def test_predict_get():
+    response = client.get("/predict/")
+    assert response.status_code == 405
+    assert response.json() == {"detail": "Method Not Allowed"}