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import gradio as gr
import pandas as pd
import numpy as np
from keras.models import model_from_json
from tf.keras.utils import image
#from keras.preprocessing import image
from keras.applications.vgg16 import VGG16, preprocess_input
file = open("womanlife.json", 'r')
model_json2 = file.read()
file.close()
loaded_model = model_from_json(model_json2)
loaded_model.load_weights("womanlife.h5")
objects = ('There is a benign nodule', 'There is a malignant nodule', 'Normal Breast')
y_pos = np.arange(len(objects))
def predict_image(pic):
img = image.load_img(pic, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
custom = loaded_model.predict(x)
m=0.000000000000000000001
a=custom[0]
for i in range(0,len(a)):
if a[i]>m:
m=a[i]
ind=i
return ('Result of the analysis:',objects[ind])
iface = gr.Interface(
predict_image,
[
gr.inputs.Image(source="upload",type="filepath", label="Imagen")
],
"text",
interpretation="default",
title = 'WomanLife: Deep Learning for the detection of breast cancer',
description = 'Breast cancer is the most common type of cancer in women and is also one of the main causes of death according to the WHO (WHO, 2020). Early detection is the single most important factor in lowering cancer treatment costs and mortality. https://saturdays.ai/2021/12/31/womanlife-deep-learning-for-the-detection-and-classification-of-breast-cancer-2/',
examples=[["A.png"], ["B.png"], ["C.png"], ["D.png"], ["E.png"], ["F.png"]],
theme = 'peach'
)
iface.launch() |