#!pip install gradio


import os
import shutil
import glob
from tqdm.notebook import tqdm
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tensorflow import keras
import cv2
from PIL import Image
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import random
from random import seed
#from livelossplot import PlotLossesKeras
import math
from tensorflow.keras.metrics import Recall,Precision,AUC
from keras.models import load_model

model = load_model('target_xception_model.h5')

class_names={0:'خبيث',1:'حميد'}

def predict_image(img):
    img_4d=img.reshape(-1,299,299,3)
    img_4d=img_4d/255
    prediction=model.predict(img_4d)[0]
    #prediction = [1 if x>0.5 else 0 for x in prediction]
    return {class_names[i]: float(prediction[i]) for i in range(1)}
    import gradio as gr
image = gr.inputs.Image(shape=(299,299))
label = gr.outputs.Label(num_top_classes=1)
gr.Interface(fn=predict_image, inputs=image,
             outputs=label).launch(debug='False',share=True)