Update api.py

api.py

@@ -1,24 +1,13 @@
-from fastapi import FastAPI, File, UploadFile
-import numpy as np
-import tensorflow as tf
-from fastapi.middleware.cors import CORSMiddleware
 import cv2
+import tensorflow as tf
+import numpy as np
+from fastapi import FastAPI, UploadFile, File
 from pydantic import BaseModel
-from huggingface_hub import from_pretrained_keras
+import uvicorn
 
 app = FastAPI()
 
-origins = ["*"]
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=origins,
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
 
-# Filtre kısmı
 def crop_image_from_gray(img, tol=7):
     if img.ndim == 2:
         mask = img > tol
@@ -36,6 +25,7 @@ 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)
@@ -43,6 +33,7 @@ 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)
@@ -52,52 +43,72 @@ 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)
+
+def predict(image, model, filter):
+    model_image = filter(image)
     model_image = np.array([model_image], dtype=np.float32) / 255.0
-    predictions = model(tf.constant(model_image))
-    return predictions.numpy()
+    infer = model.signatures["serving_default"]
+    predictions = infer(tf.constant(model_image))[next(iter(infer.structured_outputs.keys()))].numpy()
+    return predictions
+
 
 def result(predictions):
     class_labels = ["Age related Macular Degeneration", "Cataract", "Diabetes", "Glaucoma", "Hypertension", "Normal", "Others", "Pathological Myopia"]
     predictions = np.array(predictions)
     predictions = predictions.tolist()[0]
     predictions_index = np.argmax(predictions)
-    return class_labels[predictions_index]
+
+    result_json = {
+        "class": class_labels[predictions_index],
+        "probability": predictions[predictions_index]
+    }
+
+    return result_json
 
 # Model tanımlamaları
-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"]
+models_names = ["ODIR-B-2K-5Class-LastTrain-Xception"]
+
+models_paths = [
+    "ODIR-B-2K-5Class-LastTrain-Xception"
+]
 
-filters = [filter1, filter2]  # tanımlandı
+filters = [filter1, filter1, filter1, filter1]
 
 models = []
-for model_path in model_paths:
-    model = from_pretrained_keras(model_path)
+
+for model_path in models_paths:
+    model = tf.saved_model.load(model_path)
     models.append(model)
 
 class PredictionResponse(BaseModel):
-    predictions: dict
+    ODIR-B-2K-5Class-LastTrain-Xception: dict
 
 @app.post("/predict", response_model=PredictionResponse)
 async def predict_endpoint(file: UploadFile = File(...)):
+
     contents = await file.read()
-    nparr = np.frombuffer(contents, np.uint8)
+    nparr = np.fromstring(contents, np.uint8)
     image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
 
     result_json = {}
+
     for i in range(len(models)):
         model = models[i]
         prediction = predict(image, model, filters[i])
         result_json[models_names[i]] = result(prediction)
 
-    return {"predictions": result_json}
+    return result_json
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="localhost", port=8000)