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import gradio as gr | |
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 numpy as np | |
import cv2 | |
import pickle | |
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() | |
return {k:v for k,v in zip(classes,prediction)} | |
css_code = 'body{background-image:url("https://picsum.photos/seed/picsum/200/300");}' | |
thumbnail = "https://github.com/gradio-app/hub-openpose/blob/master/screenshot.png?raw=true" | |
demo = gr.Interface(Prediction_VGG16, gr.inputs.Image(shape=(224,224)),gr.Label(num_top_classes=2),css= css_code ,theme="dark-peach",thumbnail =thumbnail ) | |
demo.launch() |