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from PIL import Image
import os
import numpy as np
import tensorflow as tf
data = "DATA"
exit()
def predict(values, dic):
# diabetes
if len(values) == 8:
dic2 = {'NewBMI_Obesity 1': 0, 'NewBMI_Obesity 2': 0, 'NewBMI_Obesity 3': 0, 'NewBMI_Overweight': 0,
'NewBMI_Underweight': 0, 'NewInsulinScore_Normal': 0, 'NewGlucose_Low': 0,
'NewGlucose_Normal': 0, 'NewGlucose_Overweight': 0, 'NewGlucose_Secret': 0}
if dic['BMI'] <= 18.5:
dic2['NewBMI_Underweight'] = 1
elif 18.5 < dic['BMI'] <= 24.9:
pass
elif 24.9 < dic['BMI'] <= 29.9:
dic2['NewBMI_Overweight'] = 1
elif 29.9 < dic['BMI'] <= 34.9:
dic2['NewBMI_Obesity 1'] = 1
elif 34.9 < dic['BMI'] <= 39.9:
dic2['NewBMI_Obesity 2'] = 1
elif dic['BMI'] > 39.9:
dic2['NewBMI_Obesity 3'] = 1
if 16 <= dic['Insulin'] <= 166:
dic2['NewInsulinScore_Normal'] = 1
if dic['Glucose'] <= 70:
dic2['NewGlucose_Low'] = 1
elif 70 < dic['Glucose'] <= 99:
dic2['NewGlucose_Normal'] = 1
elif 99 < dic['Glucose'] <= 126:
dic2['NewGlucose_Overweight'] = 1
elif dic['Glucose'] > 126:
dic2['NewGlucose_Secret'] = 1
dic.update(dic2)
values2 = list(map(float, list(dic.values())))
model = pickle.load(open('models/diabetes.pkl','rb'))
values = np.asarray(values2)
return model.predict(values.reshape(1, -1))[0]
# breast_cancer
elif len(values) == 22:
model = pickle.load(open('models/breast_cancer.pkl','rb'))
values = np.asarray(values)
return model.predict(values.reshape(1, -1))[0]
# heart disease
elif len(values) == 13:
model = pickle.load(open('models/heart.pkl','rb'))
values = np.asarray(values)
return model.predict(values.reshape(1, -1))[0]
# kidney disease
elif len(values) == 24:
model = pickle.load(open('models/kidney.pkl','rb'))
values = np.asarray(values)
return model.predict(values.reshape(1, -1))[0]
# liver disease
elif len(values) == 10:
model = pickle.load(open('models/liver.pkl','rb'))
values = np.asarray(values)
return model.predict(values.reshape(1, -1))[0]
def predict_api():
try:
to_predict_dict = request.form.to_dict()
for key, value in to_predict_dict.items():
try:
to_predict_dict[key] = int(value)
except ValueError:
to_predict_dict[key] = float(value)
to_predict_list = list(map(float, list(to_predict_dict.values())))
pred = predict(to_predict_list, to_predict_dict)
return {'prediction': pred}
except Exception as e:
return {'error': str(e)}
def malariapredict_api():
try:
img = Image.open(request.files['image'])
img.save("uploads/image.jpg")
img_path = os.path.join(os.path.dirname(__file__), 'uploads/image.jpg')
os.path.isfile(img_path)
img = tf.keras.utils.load_img(img_path, target_size=(128, 128))
img = tf.keras.utils.img_to_array(img)
img = np.expand_dims(img, axis=0)
model = tf.keras.models.load_model("models/malaria.h5")
pred = np.argmax(model.predict(img))
return {'prediction': int(pred)}
except Exception as e:
return {'error': str(e)}
def pneumoniapredict_api():
try:
img = Image.open(request.files['image']).convert('L')
img.save("uploads/image.jpg")
img_path = os.path.join(os.path.dirname(__file__), 'uploads/image.jpg')
os.path.isfile(img_path)
img = tf.keras.utils.load_img(img_path, target_size=(128, 128))
img = tf.keras.utils.img_to_array(img)
img = np.expand_dims(img, axis=0)
model = tf.keras.models.load_model("models/pneumonia.h5")
pred = np.argmax(model.predict(img))
return {'prediction': int(pred)}
except Exception as e:
return {'error': str(e)}
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