app_function.py

import pickle
import pandas as pd
import sklearn
import numpy as np
import gradio as gr
import imblearn
import pandas as pd
import seaborn as sns
import sklearn
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
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
import numpy as np
from sklearn.preprocessing import StandardScaler,OneHotEncoder
from sklearn.compose import make_column_transformer
import warnings
warnings.filterwarnings("ignore")
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 cv2
import pdfplumber
import re
from collections import namedtuple

# 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 = {"Xtrain": Xtrain,
                "Ytrain": Ytrain,
                "Xtest_encoded": Xtest_encoded,
                "Ytest": Ytest,
                "num_col_trans": num_col_trans,
                "cat_col_trans": cat_col_trans}


    return results


# 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 from pickle file
    with open("Model_package.pkl","rb") as f:
        Model_package = pickle.load(f)
        model = Model_package['my_classif']

    #LOADING THE DATA
    results = loading_data()
    num_col_trans = results["num_col_trans"]
    cat_col_trans = results["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="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 class="text-success" style="text-align: center">Merci pour votre visite !</h1></div>
            <div class="row"><small style="text-align: center">Vous pouvez ressayer!</small><div class="row">
        </div>

        <button type="button" class="btn btn-success">Félicitation !</button>
        


        <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="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 class="text-success" style="text-align: center">Merci pour votre visite !</h1></div>
             <div class="row"><small style="text-align: center">Vous pouvez ressayer!</small><div class="row">
        </div>

        
        
        <button type="button" class="btn btn-warning">Warning</button>
        

        <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="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 class="text-success" style="text-align: center">Merci pour votre visite !</h1></div>
             <div class="row"><small style="text-align: center">Vous pouvez ressayer!</small><div class="row">
        </div>

        
        <button type="button" class="btn btn-danger">Danger ! </button>
        
        

        <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":
        max_prob = OUTPUT_OK

    elif ma_classe=="Prediabetes":
        max_prob = OUTPUT_PREDANGER
    else:
        max_prob = OUTPUT_DANGER
    




    return data_unpreprared,max_prob,results














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="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 class="text-success" style="text-align: center">Merci pour votre visite !</h1></div>
            <div class="row"><small style="text-align: center">Vous pouvez ressayer!</small><div class="row">
        </div>

        <button type="button" class="btn btn-success">Félicitation !</button>
        


        <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="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 class="text-success" style="text-align: center">Merci pour votre visite !</h1></div>
             <div class="row"><small style="text-align: center">Vous pouvez ressayer!</small><div class="row">
        </div>

        
        <button type="button" class="btn btn-danger">Danger ! </button>
        
        

        <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":
    
        max_prob = OUTPUT_OK

    else:
        max_prob = OUTPUT_DANGER


    return max_prob,results