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2023-01-09 12:02:24 2023-01-09 12:02:24.465445: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled
2023-01-09 12:12:15 Mon Jan  9 12:12:14 2023 - *** HARAKIRI ON WORKER 2 (pid: 24, try: 1) ***
2023-01-09 12:12:15 Mon Jan  9 12:12:14 2023 - HARAKIRI !!! worker 2 status !!!
2023-01-09 12:12:15 Mon Jan  9 12:12:14 2023 - HARAKIRI [core 0] 10.0.0.75 - POST /neyedinanaliz since 1673265733
2023-01-09 12:12:15 Mon Jan  9 12:12:14 2023 - HARAKIRI !!! end of worker 2 status !!!
2023-01-09 12:12:15 DAMN ! worker 2 (pid: 24) died, killed by signal 9 :( trying respawn ...
2023-01-09 12:12:15 Respawned uWSGI worker 2 (new pid: 34)
2023-01-09 12:12:15 spawned 2 offload threads for uWSGI worker 2

import requests

#open('model_weights.h5', 'wb').write(r.content)

app = Flask(__name__)
api = Api(app)

global image

def preprocess_image(image, target_size):
    if image.mode != "RGB":
        image = image.convert("RGB")

    image = image.resize(target_size)
    image = img_to_array(image)
    image = np.expand_dims(image, axis=0)
    print(np.shape(image))

    return image


def get_model():
    global model
    global graph
    vgg16_model = applications.vgg16.VGG16()
    model = tf.keras.Sequential()


    for i in vgg16_model.layers:
        model.add(i)


    for layer in model.layers:
        layer.trainable = False


    model.add(Dense(4, activation='softmax'))
    model.make_predict_function('model_weights.h5')
    graph = tf.compat.v1.get_default_graph()
    print("Model loaded!")


print(" Loading Keras model...")
get_model()

import csv
csvFile = 0;

with open('/home/iamonuryilmaz/mysite/csvornek.csv', mode ='r')as file:
  csvFile = csv.reader(file)
  for lines in csvFile:
          fileList = lines


class NeYedim(Resource):
      def get(self):

        data = {
            'prediction': {
            'get' : 'get işlemi',
            }
        }


        print(data)
        return {'data' : data}, 200

      #post işlemi
      def post(self):
        name = request.form['name']
        encoded = request.form['image']
        decoded = base64.b64decode(encoded)
        decode = io.BytesIO(decoded)
        image = Image.open(decode)
        image.save("/home/iamonuryilmaz/mysite/picture.jpg")
        image = Image.open("/home/iamonuryilmaz/mysite/picture.jpg")

        processed_image = preprocess_image(image, target_size=(224, 224))

        with graph.as_default():
            vgg16_model = applications.vgg16.VGG16()
            model = tf.keras.Sequential()

            for i in vgg16_model.layers:
                model.add(i)

            for layer in model.layers:
                layer.trainable = False

            model.add(Dense(4, activation='softmax'))
            model.load_weights('/home/iamonuryilmaz/mysite/model_weights.h5')
            prediction = model.predict(processed_image).tolist()

        prediction_max = max(prediction[0])
        print(prediction_max)

        id = -1
        sizeOfDemoList = len(fileList)
        for i in range(sizeOfDemoList):
            if(prediction[0][i]==prediction_max):
                id=i
        print(id)


        data = {
            'prediction': {
            'name' :  name,
            'post' : 'post işlemi',
            'sonuc': fileList[0]
            }
        }

        print(data)
        return {'data' : data}, 200

# Add URL endpoints
api.add_resource(NeYedim, '/neyedinanaliz')

if __name__ == '__main__':
#     app.run(host="0.0.0.0", port=5000)
    app.run()