Updates

.github/workflows/pylint.yml

@@ -10,6 +10,10 @@ jobs:
         python-version: ["3.10"]
     steps:
     - uses: actions/checkout@v3
+    - uses: actions/setup-python@v5
+      with:
+        python-version: '3.10'
+        cache: 'pip' # caching pip dependencies
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v3
       with:

main.py

@@ -12,18 +12,22 @@ from cachetools import Cache
 
 app = FastAPI()
 
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
 
 # Initialize Cache with no TTL
-cache = Cache(maxsize=1000) 
+cache = Cache(maxsize=1000)
 
 # Load the model using the transformers pipeline
 model = pipeline("image-classification", model="falconsai/nsfw_image_detection")
 
+
 def hash_data(data):
     """Function for hashing image data."""
     return hashlib.sha256(data).hexdigest()
 
+
 @app.post("/api/v1/detect")
 async def classify_image(file: UploadFile = File(...)):
     """Function analyzing image."""
@@ -44,16 +48,16 @@ async def classify_image(file: UploadFile = File(...)):
         results = model(image)
 
         # Find the prediction with the highest confidence using the max() function
-        best_prediction = max(results, key=lambda x: x['score'])
+        best_prediction = max(results, key=lambda x: x["score"])
 
         # Calculate the confidence score, rounded to the nearest tenth and as a percentage
-        confidence_percentage = round(best_prediction['score'] * 100, 1)
+        confidence_percentage = round(best_prediction["score"] * 100, 1)
 
         # Prepare the custom response data
         response_data = {
             "file_name": file.filename,
-            "is_nsfw": best_prediction['label'] == 'nsfw',
-            "confidence_percentage": confidence_percentage
+            "is_nsfw": best_prediction["label"] == "nsfw",
+            "confidence_percentage": confidence_percentage,
         }
 
         # Populate hash
@@ -64,6 +68,8 @@ async def classify_image(file: UploadFile = File(...)):
     except PipelineException as e:
         return JSONResponse(status_code=500, content={"message": str(e)})
 
+
 if __name__ == "__main__":
     import uvicorn
+
     uvicorn.run(app, host="127.0.0.1", port=8000)

test_main.py

@@ -7,11 +7,20 @@ client = TestClient(app)
 
 FILE_NAME = "sunflower.jpg"
 
+
 def test_read_main():
     """Tests that POST /api/v1/detect returns 200 OK with valid request body"""
-    response = client.post("/api/v1/detect", files={"file": (FILE_NAME, open(FILE_NAME, "rb"), "image/jpeg")})
+    response = client.post(
+        "/api/v1/detect",
+        files={"file": (FILE_NAME, open(FILE_NAME, "rb"), "image/jpeg")},
+    )
     assert response.status_code == 200
-    assert response.json() == {"file_name": FILE_NAME, "is_nsfw": False, "confidence_percentage": 100.0}
+    assert response.json() == {
+        "file_name": FILE_NAME,
+        "is_nsfw": False,
+        "confidence_percentage": 100.0,
+    }
+
 
 def test_invalid_input():
     """Tests that POST /api/v1/detect returns 422 with empty request body"""