app.py

import pickle
import pandas as pd
import sklearn
import numpy as np
import gradio as gr
import imblearn
from ml_demo_gradio_function import *

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

 #LOADING THE DATA
results = loading_data()

Xtrain = results["Xtrain"]
Ytrain = results["Ytrain"]
Xtest_encoded = results["Xtest_encoded"]
Ytest = results["Ytest"]
num_col_trans = results["num_col_trans"]
cat_col_trans = results["cat_col_trans"]


# POUR VGG16




my_input = gr.inputs.Image(shape=(224,224))
my_output = gr.Label(label="Resultat de la prédiction",num_top_classes=2)
output_valeur = gr.outputs.HTML(label="")



description = (
    "Cette page vous montre le résultat de la prédiction effectuée par le modèle VGG16. Le resultat présente si un patient est tumeureux ou pas du tout"
    
)

vgg16_interface = gr.Interface(Prediction_VGG16, my_input,description=description, theme="huggingface",outputs = [output_valeur,my_output])



#---------------------------------------------------------------------------------------
# POUR DIABETES FICHIER PDF



input_file_PDF = gr.File(label='PDF')
text_output2 = gr.Label(label="Resultat de la prédiction",num_top_classes=3)
df_ouput = gr.Dataframe(label="Caractéristique du patient",
            headers=["HighBP","HighChol", "CholCheck", "BMI", "Smoker","Stroke","HeartDiseaseorAttack",
                    "PhysActivity", "Fruits", "Veggies","HvyAlcoholConsump","AnyHealthcare", "NoDocbcCost", "GenHlth",
                    "MentHlth", "PhysHlth", "DiffWalk",
                    "Sex", "Age", "Education","Income"],
            
            row_count=1,
            col_count=(21)
        )
output_valeurs = gr.outputs.HTML(label="")

diabtes_PDF_interface = gr.Interface(get_data_inputPDF, inputs=input_file_PDF, 
                                            outputs=[df_ouput,output_valeurs,text_output2]
                                         )



#---------------------------------------------------------------------------------------

demo = gr.TabbedInterface([vgg16_interface, diabtes_PDF_interface], ["Détéction d'une tumeur", "(PDF) Diabétique, prédiabétique ou sain"])

demo.launch()




if __name__ =="__main__":
    demo.launch(share=True)