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from io import BytesIO
import numpy as np
import tensorflow as tf
from PIL import Image
#from tensorflow.keras.applications.imagenet_utils import decode_predictions
from tensorflow.keras.models import load_model
import os
model = None
def load_model2():
model = load_model('application/models/Tumor_VGG_model.h5')
print("Model loaded")
return model
def predict(image: Image.Image):
global model
if model is None:
model = load_model2()
image = np.asarray(image.resize((224, 224)))[..., :3]
image = np.expand_dims(image, 0)
image = image / 127.5 - 1.0
result = model.predict(image)
probability = result[0]
#print("VGG Predictions:")
if probability[0] > 0.8:
brainTum_pred = str('%.2f' % (probability[0]*100) + '% Brain Tumor Present')
else:
brainTum_pred = str('%.2f' % ((1-probability[0])*100) + '% No Brain Tumor')
response = []
for i, res in enumerate(result):
resp = {}
resp["prediction"] = brainTum_pred
#resp["confidence"] = f"{res[2]*100:0.2f} %"
response.append(resp)
return response
def read_imagefile(file) -> Image.Image:
image = Image.open(BytesIO(file))
return image
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