Update api.py

api.py

@@ -17,7 +17,8 @@ app.add_middleware(
     allow_methods=["*"],
     allow_headers=["*"],
 )
-#filtre kısmı
+
+# Filtre kısmı
 def crop_image_from_gray(img, tol=7):
     if img.ndim == 2:
         mask = img > tol
@@ -35,7 +36,6 @@ def crop_image_from_gray(img, tol=7):
             img = np.stack([img1, img2, img3], axis=-1)
         return img
 
-
 def load_ben_color(image, sigmaX=10):
     image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     image = crop_image_from_gray(image)
@@ -43,7 +43,6 @@ def load_ben_color(image, sigmaX=10):
     image = cv2.addWeighted(image, 4, cv2.GaussianBlur(image, (0, 0), sigmaX), -4, 128)
     return image
 
-
 def clahe(image):
     clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
     r, g, b = cv2.split(image)
@@ -53,18 +52,15 @@ def clahe(image):
     result = cv2.merge((r, g, b))
     return result
 
-
 def filter1(image):
     image = load_ben_color(image)
     return image
 
-
 def filter2(image):
     image = clahe(image)
     image = cv2.resize(image, (224, 224))
     return image
 
-
 def predict(image, model, filter_func):
     model_image = filter_func(image)
     model_image = np.array([model_image], dtype=np.float32) / 255.0
@@ -82,7 +78,7 @@ def result(predictions):
 models_names = ["ODIR-B-2K-5Class-LastTrain-Xception", "ODIR-B-2K-6Class-LastTrain-Xception"]
 model_paths = ["Bitirme/odirmodel/ODIR-B-2K-5Class-LastTrain-Xception", "Bitirme/odirmodel/ODIR-B-2K-6Class-LastTrain-Xception"]
 
-filters = [filter1, filter2]  tanımlandı
+filters = [filter1, filter2]  # tanımlandı
 
 models = []
 for model_path in model_paths:
@@ -104,4 +100,4 @@ async def predict_endpoint(file: UploadFile = File(...)):
         prediction = predict(image, model, filters[i])
         result_json[models_names[i]] = result(prediction)
 
-    return {"predictions": result_json}
+    return {"predictions": result_json}