Spaces:

cnasa
/

ML_DEMO

Runtime error

App Files Files Community

ML_DEMO / app_function.py

cnasa

Update app_function.py

2bb3427 almost 2 years ago

raw history blame contribute delete

No virus

40.4 kB




	import pickle
	import pandas as pd
	import sklearn
	import numpy as np
	import gradio as gr
	import imblearn
	import sklearn
	from sklearn.ensemble import RandomForestClassifier
	from sklearn.model_selection import train_test_split
	from sklearn.metrics import confusion_matrix,plot_confusion_matrix,classification_report
	from imblearn.over_sampling import RandomOverSampler
	from sklearn.preprocessing import StandardScaler,OneHotEncoder
	from sklearn.compose import make_column_transformer
	import tensorflow as tf
	from tensorflow.keras.applications import imagenet_utils
	from tensorflow.keras.utils import img_to_array
	from tensorflow.keras.models import load_model
	import pdfplumber
	import re
	from collections import namedtuple
	from compress_pickle import dump, load
	import warnings
	warnings.filterwarnings("ignore")

	# Load Data

	def loading_data():
	df = pd.read_csv("diabetes_012__health_indicators_BRFSS2015.csv")

	#convert all columns to integer
	for col in df.columns:
	df[col] = df[col].astype("int")

	# Drop duplicated rows
	df_ = df.drop_duplicates()
	df=df_

	X,y = df.drop(['Diabetes_012'],axis=1),df['Diabetes_012'].values
	classes = np.unique(y)

	#Oversampling data
	randomSampler = RandomOverSampler(sampling_strategy='all',random_state=24)
	X_new,y_new = randomSampler.fit_resample(X,y)

	new_df = X_new.copy()
	new_df["Diabetes_012"] = y_new

	Xtrain,Xtest,Ytrain,Ytest = train_test_split(X_new,y_new,test_size=0.2,random_state=24,stratify=y_new)



	# Some feature engineering
	Xtrain_transf = Xtrain.copy()

	Xtrain_transf["Age2"] = Xtrain_transf["Age"]**2

	## Numerical column transformation
	col_num=["BMI","MentHlth","PhysHlth","Age","Age2"]
	num_col_trans = make_column_transformer((StandardScaler(),col_num),remainder="passthrough")
	Xtrain_transf_std = num_col_trans.fit_transform(Xtrain_transf,Ytrain)
	Xtrain_transf_std = pd.DataFrame(Xtrain_transf_std ,columns=list(Xtrain_transf.columns) )
	# Données tests:
	Xtest_transf = Xtest.copy()
	Xtest_transf["Age2"] = Xtest_transf["Age"]**2
	Xtest_transf_std = num_col_trans.transform(Xtest_transf)
	Xtest_transf_std = pd.DataFrame(Xtest_transf_std ,columns=list(Xtest_transf.columns) )


	## Categorical columns transformation
	col_cat = ["GenHlth","Education","Income"]
	cat_col_trans = make_column_transformer((OneHotEncoder(handle_unknown = 'ignore'),col_cat),remainder="passthrough")
	Xtrain_transf_std_encoded = cat_col_trans.fit_transform(Xtrain_transf_std,Ytrain)
	# Sur les données tests :
	Xtest_encoded = cat_col_trans.transform(Xtest_transf_std)




	results = {"num_col_trans": num_col_trans,
	"cat_col_trans": cat_col_trans}


	return results


	def load_models():
	## Load Model from pickle file
	with open("Model_Package", "rb") as f:
	model = load(f,compression='gzip')


	res = {"model": model}

	return res


	# Function use to get the user input data
	def user_input(HighBP,HighChol, CholCheck, BMI, Smoker,Stroke,HeartDiseaseorAttack,
	PhysActivity, Fruits, Veggies,HvyAlcoholConsump,AnyHealthcare, NoDocbcCost, GenHlth,
	MentHlth, PhysHlth, DiffWalk,
	Sex, Age, Education,Income):

	# Preprare data type

	AnyHealthcare = 0 if AnyHealthcare=="Non" else 1

	HvyAlcoholConsump = 0 if HvyAlcoholConsump=="Non" else 1

	HighBP = 0 if HighBP=="Non" else 1

	NoDocbcCost = 0 if NoDocbcCost=="Non" else 1

	HighChol= 0 if HighChol=="Non" else 1

	GenHlth = int(GenHlth)

	CholCheck = 0 if CholCheck=="Non" else 1

	MentHlth = int(MentHlth)

	BMI = int(BMI)

	PhysHlth = int(PhysHlth)

	Smoker = 0 if Smoker=="Non" else 1

	DiffWalk = 0 if DiffWalk=="Non" else 1

	Stroke = 0 if Stroke=="Non" else 1

	Sex = 0 if Sex=="Homme" else 1

	HeartDiseaseorAttack = 0 if HeartDiseaseorAttack=="Non" else 1

	Age = int(Age)

	PhysActivity = 0 if PhysActivity=="Non" else 1

	Education = int(Education)

	Fruits = 0 if Fruits=="Non" else 1

	Income = int(Income)

	Veggies = 0 if Veggies=="Non" else 1

	user_data = {'HighBP':HighBP, 'HighChol':HighChol, 'CholCheck':CholCheck, 'BMI':BMI, 'Smoker':Smoker,
	'Stroke':Stroke, 'HeartDiseaseorAttack':HeartDiseaseorAttack, 'PhysActivity':PhysActivity, 'Fruits':Fruits, 'Veggies':Veggies,
	'HvyAlcoholConsump':HvyAlcoholConsump, 'AnyHealthcare':AnyHealthcare, 'NoDocbcCost':NoDocbcCost, 'GenHlth':GenHlth,
	'MentHlth':MentHlth, 'PhysHlth':PhysHlth, 'DiffWalk':DiffWalk, 'Sex':Sex, 'Age':Age, 'Education':Education,
	'Income':Income}

	user_df = pd.DataFrame(user_data,index=[0])

	# ## Load Model from pickle file
	MODELS = load_models()
	model = MODELS["model"]

	transformers = loading_data()
	num_col_trans = transformers["num_col_trans"]
	cat_col_trans = transformers["cat_col_trans"]




	# Data preparation before fitting to the model
	user_df["Age2"] = user_df["Age"]**2
	user_df_std = num_col_trans.transform(user_df)
	user_df_std = pd.DataFrame(user_df_std ,columns=list(user_df.columns) )
	user_df_encoded = cat_col_trans.transform(user_df_std)
	user_encoded = np.array(user_df_encoded).reshape((1,-1))


	#PREDICTION



	target_names = ['No diabetes', 'Prediabetes', 'Diabetes']

	pred_proba = model.predict_proba(user_encoded)[0]


	results = {classe : pred_proba[i] for i, classe in enumerate(target_names)}

	max_prob = 0
	ma_classe = ""
	for k,v in results.items():
	if v > max_prob:
	max_prob= v
	ma_classe= k

	STYLE = """
	<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.2.0-beta1/dist/css/bootstrap.min.css"
	rel="stylesheet" integrity="sha384-0evHe/X+R7YkIZDRvuzKMRqM+OrBnVFBL6DOitfPri4tjfHxaWutUpFmBp4vmVor"
	crossorigin="anonymous">

	"""
	OUTPUT_OK = (
	STYLE
	+ f"""<html>
	<body>




	<div class="row row-cols-1 row-cols-md-3 g-4">
	<div class="col">
	<div class="card text-bg-info mb-3 text-center btn-success">
	<div class="card-header">
	No diabetes
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["No diabetes"],2)*100}%</p>
	</div>
	</div>
	</div>
	<div class="col">
	<div class="card text-bg-warning mb-3 text-center btn-success">
	<div class="card-header">
	Prediabetes
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["Prediabetes"],2)*100}%</p>
	</div>
	</div>
	</div>
	<div class="col">
	<div class="card text-bg-danger mb-3 text-center btn-success">
	<div class="card-header">
	Diabetes
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["Diabetes"],2)*100}%</p>
	</div>
	</div>
	</div>

	</div>

	<div class="container">
	<div class="row"><h1 style="text-align: center">Le Patient est détecté <b> {ma_classe} </b> avec une probabilité de </h1></div>
	<div class="row"><h1 class="display-1 text-success" style="text-align: center"><button type="button" class="btn btn-success">{round(max_prob,2)*100}%</button></h1></div>
	<div class="row"><h1 style="text-align: center">d'être dans cette classe </h1></div>
	<div class="row"><h1 style="text-align: center">
	<button type="button" class="btn btn-success">Félicitation ! </button>
	<br>
	<button type="button" class="btn btn-info">Vous pouvez ressayer!</button>

	<br>
	<div class="row"><h1 style="text-align: center">
	<h1> Importance des variables: </h1>
	<img src="https://huggingface.co/spaces/cnasa/ML_DEMO/resolve/main/feature_importance.PNG" alt="feature importance"/>

	</div>
	</div>


	<script src="https://cdn.jsdelivr.net/npm/@popperjs/core@2.11.5/dist/umd/popper.min.js" integrity="sha384-Xe+8cL9oJa6tN/veChSP7q+mnSPaj5Bcu9mPX5F5xIGE0DVittaqT5lorf0EI7Vk" crossorigin="anonymous"></script>
	<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.0-beta1/dist/js/bootstrap.min.js" integrity="sha384-kjU+l4N0Yf4ZOJErLsIcvOU2qSb74wXpOhqTvwVx3OElZRweTnQ6d31fXEoRD1Jy" crossorigin="anonymous"></script>
	</body>
	</html>
	"""
	)



	OUTPUT_PREDANGER = (
	STYLE
	+ f"""<html>
	<body>





	<div class="row row-cols-1 row-cols-md-3 g-4">
	<div class="col">
	<div class="card text-bg-info mb-3 text-center btn-success">
	<div class="card-header">
	No diabetes
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["No diabetes"],2)*100}%</p>
	</div>
	</div>
	</div>
	<div class="col">
	<div class="card text-bg-warning mb-3 text-center btn-success">
	<div class="card-header">
	Prediabetes
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["Prediabetes"],2)*100}%</p>
	</div>
	</div>
	</div>
	<div class="col">
	<div class="card text-bg-danger mb-3 text-center btn-success">
	<div class="card-header">
	Diabetes
	</div>

	<div class="card-body">
	<h5 class="card-title"> </h5>
	<p class="card-text fw-bold">{round(results["Diabetes"],2)*100}%</p>
	</div>
	</div>
	</div>

	</div>

	<div class="container">
	<div class="row"><h1 style="text-align: center">Le Patient est détecté <b> {ma_classe} </b> avec une probabilité de </h1></div>
	<div class="row"><h1 class="display-1 text-success" style="text-align: center"><button type="button" class="btn btn-warning">{round(max_prob,2)*100}%</button></h1></div>
	<div class="row"><h1 style="text-align: center">d'être dans cette classe </h1></div>

	<div class="row"><h1 style="text-align: center">
	<button type="button" class="btn btn-warning">Warning ! </button>
	<br>
	<button type="button" class="btn btn-info">Vous pouvez ressayer!</button>
	</div>

	<br>
	<div class="row"><h1 style="text-align: center">
	<h1> Importance des variables: </h1>
	<img src="https://huggingface.co/spaces/cnasa/ML_DEMO/resolve/main/feature_importance.PNG" alt="feature importance"/>

	</div>

	</div>




	<script src="https://cdn.jsdelivr.net/npm/@popperjs/core@2.11.5/dist/umd/popper.min.js" integrity="sha384-Xe+8cL9oJa6tN/veChSP7q+mnSPaj5Bcu9mPX5F5xIGE0DVittaqT5lorf0EI7Vk" crossorigin="anonymous"></script>
	<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.0-beta1/dist/js/bootstrap.min.js" integrity="sha384-kjU+l4N0Yf4ZOJErLsIcvOU2qSb74wXpOhqTvwVx3OElZRweTnQ6d31fXEoRD1Jy" crossorigin="anonymous"></script>
	</body>
	</html>
	"""
	)

	OUTPUT_DANGER = (
	STYLE
	+ f"""<html>
	<body>





	<div class="row row-cols-1 row-cols-md-3 g-4">
	<div class="col">
	<div class="card text-bg-info mb-3 text-center btn-success">
	<div class="card-header">
	No diabetes
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["No diabetes"],2)*100}%</p>
	</div>
	</div>
	</div>
	<div class="col">
	<div class="card text-bg-warning mb-3 text-center btn-success">
	<div class="card-header">
	Prediabetes
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["Prediabetes"],2)*100}%</p>
	</div>
	</div>
	</div>
	<div class="col">
	<div class="card text-bg-danger mb-3 text-center btn-success">
	<div class="card-header">
	Diabetes
	</div>

	<div class="card-body">
	<h5 class="card-title"> </h5>
	<p class="card-text fw-bold">{round(results["Diabetes"],2)*100}%</p>
	</div>
	</div>
	</div>

	</div>


	<div class="container">
	<div class="row"><h1 style="text-align: center">Le Patient est détecté <b> {ma_classe} </b> avec une probabilité de </h1></div>
	<div class="row"><h1 class="display-1 text-success" style="text-align: center"><button type="button" class="btn btn-danger">{round(max_prob,2)*100}%</button></h1></div>
	<div class="row"><h1 style="text-align: center">d'être dans cette classe </h1></div>

	<div class="row"><h1 style="text-align: center">
	<button type="button" class="btn btn-danger">Danger ! </button>
	<br>
	<button type="button" class="btn btn-info">Vous pouvez ressayer!</button>
	</div>

	<br>
	<div class="row"><h1 style="text-align: center">
	<h1> Importance des variables: </h1>
	<img src="https://huggingface.co/spaces/cnasa/ML_DEMO/resolve/main/feature_importance.PNG" alt="feature importance"/>

	</div>

	</div>









	<script src="https://cdn.jsdelivr.net/npm/@popperjs/core@2.11.5/dist/umd/popper.min.js" integrity="sha384-Xe+8cL9oJa6tN/veChSP7q+mnSPaj5Bcu9mPX5F5xIGE0DVittaqT5lorf0EI7Vk" crossorigin="anonymous"></script>
	<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.0-beta1/dist/js/bootstrap.min.js" integrity="sha384-kjU+l4N0Yf4ZOJErLsIcvOU2qSb74wXpOhqTvwVx3OElZRweTnQ6d31fXEoRD1Jy" crossorigin="anonymous"></script>
	</body>
	</html>
	"""
	)






	if ma_classe=="No diabetes" and max_prob > 0.7 :
	max_prob = OUTPUT_OK

	elif (ma_classe=="No diabetes" and max_prob <= 0.7) or ma_classe=="Prediabetes" :
	max_prob = OUTPUT_PREDANGER
	else:
	max_prob = OUTPUT_DANGER



	return max_prob




	# Get PDF File for user
	def get_data_inputPDF(PDF_file):



	path_file = PDF_file.name


	with pdfplumber.open(path_file) as pdf:
	num_page = 0
	page = pdf.pages[num_page]
	text = page.extract_text(x_tolerance=2, y_tolerance=0)


	AnyHealthcare_pattern = re.compile('^(AnyHealthcare)\s+(\w{3})')
	HighBP_pattern = re.compile('^(HighBP)\s+(\w{3})')
	NoDocbcCost_pattern = re.compile('^(NoDocbcCost)\s+(\w{3})')
	HighChol_pattern = re.compile('^(HighChol)\s+(\w{3})')
	GenHlth_pattern = re.compile('^(GenHlth)\s+(\d+)')
	CholCheck_pattern = re.compile('^(CholCheck)\s+(\w{3})')
	MentHlth_pattern = re.compile('^(MentHlth)\s+(\d+)')
	BMI_pattern = re.compile('^(BMI)\s+(\d+)')
	PhysHlth_pattern = re.compile('^(PhysHlth)\s+(\d+)')
	Smoker_pattern = re.compile('^(Smoker)\s+(\w{3})')
	DiffWalk_pattern = re.compile('^(DiffWalk)\s+(\w{3})')
	Stroke_pattern = re.compile('^(Stroke)\s+(\w{3})')
	Sex_pattern = re.compile('^(Sex)\s+(\w+)')
	HeartDiseaseorAttack_pattern = re.compile('^(HeartDiseaseorAttack)\s+(\w{3})')
	Age_pattern = re.compile('^(Age)\s+(\d+)')
	PhysActivity_pattern = re.compile('^(PhysActivity)\s+(\w{3})')
	Education_pattern = re.compile('^(Education)\s+(\d+)')
	Fruits_pattern = re.compile('^(Fruits)\s+(\w{3})')
	Income_pattern = re.compile('^(Income)\s+(\d+)')
	Veggies_pattern = re.compile('^(Veggies)\s+(\w{3})')
	HvyAlcoholConsump_pattern = re.compile('^(HvyAlcoholConsump)\s+(\w{3})')





	for line in text.split("\n"):

	AnyHealthcare_ligne = AnyHealthcare_pattern.search(line)
	HighBP_ligne = HighBP_pattern.search(line)
	NoDocbcCost_ligne = NoDocbcCost_pattern.search(line)
	HighChol_ligne = HighChol_pattern.search(line)
	GenHlth_ligne = GenHlth_pattern.search(line)
	CholCheck_ligne = CholCheck_pattern.search(line)
	MentHlth_ligne = MentHlth_pattern.search(line)
	BMI_ligne = BMI_pattern.search(line)
	PhysHlth_ligne = PhysHlth_pattern.search(line)
	Smoker_ligne = Smoker_pattern.search(line)
	DiffWalk_ligne = DiffWalk_pattern.search(line)
	Stroke_ligne = Stroke_pattern.search(line)
	Sex_ligne = Sex_pattern.search(line)
	HeartDiseaseorAttack_ligne = HeartDiseaseorAttack_pattern.search(line)
	Age_ligne = Age_pattern.search(line)
	PhysActivity_ligne = PhysActivity_pattern.search(line)
	Education_ligne = Education_pattern.search(line)
	Fruits_ligne = Fruits_pattern.search(line)
	Income_ligne = Income_pattern.search(line)
	Veggies_ligne = Veggies_pattern.search(line)
	HvyAlcoholConsump_ligne = HvyAlcoholConsump_pattern.search(line)

	if AnyHealthcare_ligne:
	AnyHealthcare = AnyHealthcare_ligne.group(2)
	if HvyAlcoholConsump_ligne:
	HvyAlcoholConsump = HvyAlcoholConsump_ligne.group(2)
	if HighBP_ligne:
	HighBP = HighBP_ligne.group(2)
	if NoDocbcCost_ligne:
	NoDocbcCost = NoDocbcCost_ligne.group(2)
	if HighChol_ligne:
	HighChol= HighChol_ligne.group(2)
	if GenHlth_ligne:
	GenHlth = GenHlth_ligne.group(2)
	if CholCheck_ligne:
	CholCheck = CholCheck_ligne.group(2)
	if MentHlth_ligne:
	MentHlth = MentHlth_ligne.group(2)
	if BMI_ligne:
	BMI = BMI_ligne.group(2)
	if PhysHlth_ligne:
	PhysHlth = PhysHlth_ligne.group(2)
	if Smoker_ligne:
	Smoker = Smoker_ligne.group(2)
	if DiffWalk_ligne:
	DiffWalk = DiffWalk_ligne.group(2)
	if Stroke_ligne:
	Stroke = Stroke_ligne.group(2)
	if Sex_ligne:
	Sex = Sex_ligne.group(2)
	if HeartDiseaseorAttack_ligne:
	HeartDiseaseorAttack = HeartDiseaseorAttack_ligne.group(2)
	if Age_ligne:
	Age = Age_ligne.group(2)
	if PhysActivity_ligne:
	PhysActivity = PhysActivity_ligne.group(2)
	if Education_ligne:
	Education = Education_ligne.group(2)
	if Fruits_ligne:
	Fruits = Fruits_ligne.group(2)
	if Income_ligne:
	Income = Income_ligne.group(2)
	if Veggies_ligne:
	Veggies = Veggies_ligne.group(2)


	tab = namedtuple('table','HighBP HighChol CholCheck BMI Smoker Stroke HeartDiseaseorAttack \
	PhysActivity Fruits Veggies HvyAlcoholConsump AnyHealthcare NoDocbcCost GenHlth \
	MentHlth PhysHlth DiffWalk \
	Sex Age Education Income')


	data_unpreprared = pd.DataFrame([tab(HighBP,HighChol, CholCheck, BMI, Smoker,Stroke,HeartDiseaseorAttack,
	PhysActivity, Fruits, Veggies,HvyAlcoholConsump,AnyHealthcare, NoDocbcCost, GenHlth,
	MentHlth, PhysHlth, DiffWalk,
	Sex, Age, Education,Income)])


	# Preprare data type

	AnyHealthcare = 0 if AnyHealthcare=="Non" else 1

	HvyAlcoholConsump = 0 if HvyAlcoholConsump=="Non" else 1

	HighBP = 0 if HighBP=="Non" else 1

	NoDocbcCost = 0 if NoDocbcCost=="Non" else 1

	HighChol= 0 if HighChol=="Non" else 1

	GenHlth = int(GenHlth)

	CholCheck = 0 if CholCheck=="Non" else 1

	MentHlth = int(MentHlth)

	BMI = int(BMI)

	PhysHlth = int(PhysHlth)

	Smoker = 0 if Smoker=="Non" else 1

	DiffWalk = 0 if DiffWalk=="Non" else 1

	Stroke = 0 if Stroke=="Non" else 1

	Sex = 0 if Sex=="Homme" else 1

	HeartDiseaseorAttack = 0 if HeartDiseaseorAttack=="Non" else 1

	Age = int(Age)

	PhysActivity = 0 if PhysActivity=="Non" else 1

	Education = int(Education)

	Fruits = 0 if Fruits=="Non" else 1

	Income = int(Income)

	Veggies = 0 if Veggies=="Non" else 1


	user_df = pd.DataFrame([tab(HighBP,HighChol, CholCheck, BMI, Smoker,Stroke,HeartDiseaseorAttack,
	PhysActivity, Fruits, Veggies,HvyAlcoholConsump,AnyHealthcare, NoDocbcCost, GenHlth,
	MentHlth, PhysHlth, DiffWalk,
	Sex, Age, Education,Income)])




	# #LOAD MODEL AND TRANSFORMERS


	MODELS = load_models()
	model = MODELS["model"]

	transformers = loading_data()
	num_col_trans = transformers["num_col_trans"]
	cat_col_trans = transformers["cat_col_trans"]


	# Data preparation before fitting to the model
	user_df["Age2"] = user_df["Age"]**2
	user_df_std = num_col_trans.transform(user_df)
	user_df_std = pd.DataFrame(user_df_std ,columns=list(user_df.columns) )
	user_df_encoded = cat_col_trans.transform(user_df_std)
	user_encoded = np.array(user_df_encoded).reshape((1,-1))


	#PREDICTION



	target_names = ['No diabetes', 'Prediabetes', 'Diabetes']

	pred_proba = model.predict_proba(user_encoded)[0]

	results = {classe : pred_proba[i] for i, classe in enumerate(target_names)}

	max_prob = 0
	ma_classe = ""
	for k,v in results.items():
	if v > max_prob:
	max_prob= v
	ma_classe= k

	STYLE = """
	<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.2.0-beta1/dist/css/bootstrap.min.css"
	rel="stylesheet" integrity="sha384-0evHe/X+R7YkIZDRvuzKMRqM+OrBnVFBL6DOitfPri4tjfHxaWutUpFmBp4vmVor"
	crossorigin="anonymous">

	"""
	OUTPUT_OK = (
	STYLE
	+ f"""<html>
	<body>




	<div class="row row-cols-1 row-cols-md-3 g-4">
	<div class="col">
	<div class="card text-bg-success mb-3 text-center btn-success">
	<div class="card-header">
	No diabetes
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["No diabetes"],2)*100}%</p>
	</div>
	</div>
	</div>
	<div class="col">
	<div class="card text-bg-warning mb-3 text-center btn-success">
	<div class="card-header">
	Prediabetes
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["Prediabetes"],2)*100}%</p>
	</div>
	</div>
	</div>
	<div class="col">
	<div class="card text-bg-danger mb-3 text-center btn-success">
	<div class="card-header">
	Diabetes
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["Diabetes"],2)*100}%</p>
	</div>
	</div>
	</div>

	</div>


	<div class="container">
	<div class="row"><h1 style="text-align: center">Le Patient est détecté <b> {ma_classe} </b> avec une probabilité de </h1></div>
	<div class="row"><h1 class="display-1 text-success" style="text-align: center"><button type="button" class="btn btn-success">{round(max_prob,2)*100}%</button></h1></div>
	<div class="row"><h1 style="text-align: center">d'être dans cette classe </h1></div>
	<div class="row"><h1 style="text-align: center">
	<button type="button" class="btn btn-success">Félicitation ! </button>
	<br>
	<button type="button" class="btn btn-info">Vous pouvez ressayer!</button>

	<br>
	<div class="row"><h1 style="text-align: center">
	<h1> Importance des variables: </h1>
	<img src="https://huggingface.co/spaces/cnasa/ML_DEMO/resolve/main/feature_importance.PNG" alt="feature importance"/>

	</div>
	</div>


	<script src="https://cdn.jsdelivr.net/npm/@popperjs/core@2.11.5/dist/umd/popper.min.js" integrity="sha384-Xe+8cL9oJa6tN/veChSP7q+mnSPaj5Bcu9mPX5F5xIGE0DVittaqT5lorf0EI7Vk" crossorigin="anonymous"></script>
	<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.0-beta1/dist/js/bootstrap.min.js" integrity="sha384-kjU+l4N0Yf4ZOJErLsIcvOU2qSb74wXpOhqTvwVx3OElZRweTnQ6d31fXEoRD1Jy" crossorigin="anonymous"></script>
	</body>
	</html>
	"""
	)



	OUTPUT_PREDANGER = (
	STYLE
	+ f"""<html>
	<body>





	<div class="row row-cols-1 row-cols-md-3 g-4">
	<div class="col">
	<div class="card text-bg-success mb-3 text-center btn-success">
	<div class="card-header">
	No diabetes
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["No diabetes"],2)*100}%</p>
	</div>
	</div>
	</div>
	<div class="col">
	<div class="card text-bg-warning mb-3 text-center btn-success">
	<div class="card-header">
	Prediabetes
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["Prediabetes"],2)*100}%</p>
	</div>
	</div>
	</div>
	<div class="col">
	<div class="card text-bg-danger mb-3 text-center btn-success">
	<div class="card-header">
	Diabetes
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["Diabetes"],2)*100}%</p>
	</div>
	</div>
	</div>

	</div>


	<div class="container">
	<div class="row"><h1 style="text-align: center">Le Patient est détecté <b> {ma_classe} </b> avec une probabilité de </h1></div>
	<div class="row"><h1 class="display-1 text-success" style="text-align: center"><button type="button" class="btn btn-warning">{round(max_prob,2)*100}%</button></h1></div>
	<div class="row"><h1 style="text-align: center">d'être dans cette classe </h1></div>

	<div class="row"><h1 style="text-align: center">
	<button type="button" class="btn btn-warning">Warning ! </button>
	<br>
	<button type="button" class="btn btn-info">Vous pouvez ressayer!</button>
	</div>

	<br>
	<div class="row"><h1 style="text-align: center">
	<h1> Importance des variables: </h1>
	<img src="https://huggingface.co/spaces/cnasa/ML_DEMO/resolve/main/feature_importance.PNG" alt="feature importance"/>

	</div>

	</div>



	<script src="https://cdn.jsdelivr.net/npm/@popperjs/core@2.11.5/dist/umd/popper.min.js" integrity="sha384-Xe+8cL9oJa6tN/veChSP7q+mnSPaj5Bcu9mPX5F5xIGE0DVittaqT5lorf0EI7Vk" crossorigin="anonymous"></script>
	<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.0-beta1/dist/js/bootstrap.min.js" integrity="sha384-kjU+l4N0Yf4ZOJErLsIcvOU2qSb74wXpOhqTvwVx3OElZRweTnQ6d31fXEoRD1Jy" crossorigin="anonymous"></script>
	</body>
	</html>
	"""
	)

	OUTPUT_DANGER = (
	STYLE
	+ f"""<html>
	<body>





	<div class="row row-cols-1 row-cols-md-3 g-4">
	<div class="col">
	<div class="card text-bg-success mb-3 text-center btn-success">
	<div class="card-header">
	No diabetes
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["No diabetes"],2)*100}%</p>
	</div>
	</div>
	</div>
	<div class="col">
	<div class="card text-bg-warning mb-3 text-center btn-success">
	<div class="card-header">
	Prediabetes
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["Prediabetes"],2)*100}%</p>
	</div>
	</div>
	</div>
	<div class="col">
	<div class="card text-bg-danger mb-3 text-center btn-success">
	<div class="card-header">
	Diabetes
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["Diabetes"],2)*100}%</p>
	</div>
	</div>
	</div>

	</div>

	<div class="container">
	<div class="row"><h1 style="text-align: center">Le Patient est détecté <b> {ma_classe} </b> avec une probabilité de </h1></div>
	<div class="row"><h1 class="display-1 text-success" style="text-align: center"><button type="button" class="btn btn-danger">{round(max_prob,2)*100}%</button></h1></div>
	<div class="row"><h1 style="text-align: center">d'être dans cette classe </h1></div>

	<div class="row"><h1 style="text-align: center">
	<button type="button" class="btn btn-danger">Danger ! </button>
	<br>
	<button type="button" class="btn btn-info">Vous pouvez ressayer!</button>
	</div>

	<br>
	<div class="row"><h1 style="text-align: center">
	<h1> Importance des variables: </h1>
	<img src="https://huggingface.co/spaces/cnasa/ML_DEMO/resolve/main/feature_importance.PNG" alt="feature importance"/>

	</div>

	</div>











	<script src="https://cdn.jsdelivr.net/npm/@popperjs/core@2.11.5/dist/umd/popper.min.js" integrity="sha384-Xe+8cL9oJa6tN/veChSP7q+mnSPaj5Bcu9mPX5F5xIGE0DVittaqT5lorf0EI7Vk" crossorigin="anonymous"></script>
	<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.0-beta1/dist/js/bootstrap.min.js" integrity="sha384-kjU+l4N0Yf4ZOJErLsIcvOU2qSb74wXpOhqTvwVx3OElZRweTnQ6d31fXEoRD1Jy" crossorigin="anonymous"></script>
	</body>
	</html>
	"""
	)






	if ma_classe=="No diabetes" and max_prob > 0.7 :
	max_prob = OUTPUT_OK

	elif (ma_classe=="No diabetes" and max_prob <= 0.7) or ma_classe=="Prediabetes" :
	max_prob = OUTPUT_PREDANGER
	else:
	max_prob = OUTPUT_DANGER





	return data_unpreprared,max_prob














	def Prediction_VGG16(image):

	#Prepare image
	IMG_SIZE = 224
	image = img_to_array(image)
	image = image*1.0/255.0
	img_prepared = image.reshape((-1,IMG_SIZE,IMG_SIZE,3))


	#Load model vgg6 package
	path = "model_vgg16.h5"
	my_model =load_model(path)



	#Prediction
	classes = ["Brain Tumor","Healthy"]
	prediction = my_model.predict(img_prepared)[0]
	prediction = prediction.tolist()


	results = {k:v for k,v in zip(classes,prediction)}

	max_prob = 0
	ma_classe = ""
	for k,v in results.items():
	if v > max_prob:
	max_prob= v
	ma_classe= k

	STYLE = """
	<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.2.0-beta1/dist/css/bootstrap.min.css"
	rel="stylesheet" integrity="sha384-0evHe/X+R7YkIZDRvuzKMRqM+OrBnVFBL6DOitfPri4tjfHxaWutUpFmBp4vmVor"
	crossorigin="anonymous">

	"""
	OUTPUT_OK = (
	STYLE
	+ f"""<html>
	<body>



	<div class="row row-cols-1 row-cols-md-2 g-4">
	<div class="col">
	<div class="card text-bg-danger mb-3 text-center btn-success">
	<div class="card-header">
	Brain Tumor
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["Brain Tumor"],2)*100}%</p>
	</div>
	</div>
	</div>

	<div class="col">
	<div class="card text-bg-success mb-3 text-center btn-success">
	<div class="card-header">
	Healthy
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["Healthy"],2)*100}%</p>
	</div>
	</div>
	</div>

	</div>


	<div class="container">
	<div class="row"><h1 style="text-align: center">Le Patient est détecté <b> {ma_classe} </b> avec une probabilité de </h1></div>
	<div class="row"><h1 class="display-1 text-success" style="text-align: center"><button type="button" class="btn btn-success">{round(max_prob,2)*100}%</button></h1></div>
	<div class="row"><h1 style="text-align: center">d'être dans cette classe </h1></div>

	<div class="row"><h1 style="text-align: center">
	<button type="button" class="btn btn-success">Félicitation ! </button>
	<br>
	<button type="button" class="btn btn-info">Vous pouvez ressayer!</button>
	</div>


	</div>



	<script src="https://cdn.jsdelivr.net/npm/@popperjs/core@2.11.5/dist/umd/popper.min.js" integrity="sha384-Xe+8cL9oJa6tN/veChSP7q+mnSPaj5Bcu9mPX5F5xIGE0DVittaqT5lorf0EI7Vk" crossorigin="anonymous"></script>
	<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.0-beta1/dist/js/bootstrap.min.js" integrity="sha384-kjU+l4N0Yf4ZOJErLsIcvOU2qSb74wXpOhqTvwVx3OElZRweTnQ6d31fXEoRD1Jy" crossorigin="anonymous"></script>
	</body>
	<script>



	</script>
	</html>
	"""
	)


	OUTPUT_PREDANGER = (
	STYLE
	+ f"""<html>
	<body>



	</div>


	<div class="row row-cols-1 row-cols-md-2 g-4">
	<div class="col">
	<div class="card text-bg-danger mb-3 text-center btn-success">
	<div class="card-header">
	Brain Tumor
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["Brain Tumor"],2)*100}%</p>
	</div>
	</div>
	</div>

	<div class="col">
	<div class="card text-bg-success mb-3 text-center btn-success">
	<div class="card-header">
	Healthy
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["Healthy"],2)*100}%</p>
	</div>
	</div>
	</div>

	</div>


	<div class="container">
	<div class="row"><h1 style="text-align: center">Le Patient est détecté <b> {ma_classe} </b> avec une probabilité de </h1></div>
	<div class="row"><h1 class="display-1 text-success" style="text-align: center"><button type="button" class="btn btn-warning">{round(max_prob,2)*100}%</button></h1></div>
	<div class="row"><h1 style="text-align: center">d'être dans cette classe </h1></div>
	<div class="row"><h1 style="text-align: center">
	<button type="button" class="btn btn-warning">Warning ! </button>
	<br>
	<button type="button" class="btn btn-info">Vous pouvez ressayer !</button>
	</div>




	<script src="https://cdn.jsdelivr.net/npm/@popperjs/core@2.11.5/dist/umd/popper.min.js" integrity="sha384-Xe+8cL9oJa6tN/veChSP7q+mnSPaj5Bcu9mPX5F5xIGE0DVittaqT5lorf0EI7Vk" crossorigin="anonymous"></script>
	<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.0-beta1/dist/js/bootstrap.min.js" integrity="sha384-kjU+l4N0Yf4ZOJErLsIcvOU2qSb74wXpOhqTvwVx3OElZRweTnQ6d31fXEoRD1Jy" crossorigin="anonymous"></script>
	</body>
	</html>
	"""
	)


	OUTPUT_DANGER = (
	STYLE
	+ f"""<html>
	<body>



	</div>



	<div class="row row-cols-1 row-cols-md-2 g-4">
	<div class="col">
	<div class="card text-bg-danger mb-3 text-center btn-success">
	<div class="card-header">
	Brain Tumor
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["Brain Tumor"],2)*100}%</p>
	</div>
	</div>
	</div>

	<div class="col">
	<div class="card text-bg-success mb-3 text-center btn-success">
	<div class="card-header">
	Healthy
	</div>

	<div class="card-body">

	<p class="card-text fw-bold">{round(results["Healthy"],2)*100}%</p>
	</div>
	</div>
	</div>

	</div>


	<div class="container">
	<div class="row"><h1 style="text-align: center">Le Patient est détecté <b> {ma_classe} </b> avec une probabilité de </h1></div>
	<div class="row"><h1 class="display-1 text-success" style="text-align: center"><button type="button" class="btn btn-danger">{round(max_prob,2)*100}%</button></h1></div>
	<div class="row"><h1 style="text-align: center">d'être dans cette classe </h1></div>
	<div class="row"><h1 style="text-align: center">
	<button type="button" class="btn btn-danger">Danger ! </button>
	<br>
	<button type="button" class="btn btn-info">Vous pouvez ressayer!</button>
	</div>




	<script src="https://cdn.jsdelivr.net/npm/@popperjs/core@2.11.5/dist/umd/popper.min.js" integrity="sha384-Xe+8cL9oJa6tN/veChSP7q+mnSPaj5Bcu9mPX5F5xIGE0DVittaqT5lorf0EI7Vk" crossorigin="anonymous"></script>
	<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.0-beta1/dist/js/bootstrap.min.js" integrity="sha384-kjU+l4N0Yf4ZOJErLsIcvOU2qSb74wXpOhqTvwVx3OElZRweTnQ6d31fXEoRD1Jy" crossorigin="anonymous"></script>
	</body>
	</html>
	"""
	)






	if ma_classe=="Healthy" and max_prob > 0.7 :
	max_prob = OUTPUT_OK

	elif ma_classe=="Healthy" and max_prob <= 0.7:
	max_prob = OUTPUT_PREDANGER

	else:
	max_prob = OUTPUT_DANGER


	return max_prob