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	import torchvision.transforms as transforms
	import matplotlib.pyplot as plt
	import torch
	import numpy as np

	from PIL import Image
	from collections import OrderedDict
	from Transformer_Explainability.modules.layers_ours import *
	from Transformer_Explainability.baselines.ViT.ViT_LRP import vit_base_patch16_224 as vit_LRP
	from Transformer_Explainability.baselines.ViT.ViT_explanation_generator import LRP
	from Transformer_Explainability.XAI import *

	from io import BytesIO
	from PIL import Image
	import streamlit as st
	import pandas as pd
	from streamlit_option_menu import option_menu
	# import Pages as pg

	# mean = [0.485, 0.456, 0.406] # our images have three channels
	# std = [0.229, 0.224, 0.225]
	# normalize = transforms.Normalize(mean=mean, std=std)
	# transform = transforms.Compose([
	# transforms.Resize((224,224)),
	# transforms.ToTensor(),
	# normalize,
	# ])
	# resizing = transforms.Resize((224,224))

	CLS2IDX= {0: "Benign",
	1: "Ductal carcinoma",
	2: "Lobular carcinoma",
	3: "Mucinous carcinoma",
	4: "Papillary carcinoma"}

	st.set_page_config(
	page_title="Breast Cancer Diagnosis",
	# page_icon="👨‍⚕️",
	layout="wide",
	initial_sidebar_state="expanded", #collapsed
	)

	# "st.session_state :", st.session_state

	if "loop" not in st.session_state:
	st.session_state["loop"] = 1

	def use_model(upl, mag):
	num_classes=5
	model = vit_LRP(pretrained=True)
	num_ftrs = model.head.in_features
	model.head = Linear(num_ftrs, num_classes)
	state_dict = torch.load("./Model/V" + mag + ".pth", map_location=torch.device('cpu'))
	model.load_state_dict(state_dict)
	model.eval()
	ag = LRP(model)

	model_pred=[]

	with st.container():
	st.subheader("Result")

	for file in upl:
	bytes_data = file.read()
	img = Image.open(BytesIO(bytes_data))
	inputs = tf(img)
	outputs = model(inputs.unsqueeze(0))

	prob = torch.softmax(outputs, dim=1)
	class_indices = outputs.data.topk(5, dim=1)[1][0].tolist()

	model_prop = []
	for cls_idx in class_indices:
	model_prop.append([CLS2IDX[cls_idx], "{:.2f}".format(prob[0, cls_idx]*100)])

	img_col1, img_col2, txt_col = st.columns((1, 1, 2))

	with img_col1:
	st.image(
	transforms.Resize((224,224))(img),
	output_format="auto",
	caption="original image"
	)

	with img_col2:
	st.image(
	generate_visualization(inputs, ag),
	caption="explainability"
	)

	with txt_col:
	df = pd.DataFrame(
	model_prop,
	columns=["Class", "Probability"]
	)

	st.dataframe(
	df,
	width=600
	)

	rslt = CLS2IDX[class_indices[0]]
	if rslt == "Benign":
	tp = "-"
	else:
	tp = rslt
	rslt = "Malignant"
	model_pred.append([file.name, rslt, tp])

	with st.container():
	st.subheader("Conclusion")

	df2 = pd.DataFrame(
	model_pred, columns=["Name", "Result", "Type"]
	)

	with st.container():
	st.dataframe(
	df2,
	width=800
	)





	# --Header--
	with st.container():
	st.title("Breast cancer diagnosis by transformer model")
	st.subheader("Hi :wave:")
	st.write("This website will help you to diagnose breast cancer images that collected by surgical open biopsy method(SOB)")
	st.write("The data that you will input must same to BREAKHIS dataset [Learn more >](https://docs.google.com/document/d/12NYTJkh2yKdR75XQhD3OwQoYFFwqIK7mkSVbd0-VWok/edit?usp=drivesdk)")

	# --Sidebar--
	with st.sidebar:
	# --Inputs--
	with st.container():
	st.subheader("Inputs")
	maglist = ["--Select--", "40X", "100X", "200X", "400X"]
	magnification = st.sidebar.selectbox("Select the Magnification", maglist)

	with st.container():
	uploaded = st.file_uploader(
	"Choose images to diagnose", type=["jpg", "jpeg", "png"], accept_multiple_files = True
	)
	# with st.container():
	# diagnose = st.button("Diagnose")

	with st.container():
	if uploaded != [] and magnification != "--Select--":
	use_model(uploaded, magnification)

	st.session_state["loop"]+=1
	# "st.session_state obj :", st.session_state
	# pass
	# selected = option_menu(
	# menu_title=None, # required
	# options=["Home", "How to use", "Contact"]
	# )
	# if selected == "Home":
	# inputs()
	# elif selected == "How to use":
	# pg.howtouse()
	# elif selected == "Contact":
	# pg.contact()



	# st.set_page_config(
	# page_title="Breast Cancer Diagnosis",
	# page_icon="👨‍⚕️",
	# layout="wide",
	# initial_sidebar_state="expanded", #collapsed
	# )

	# st.title("Breast Cancer Diagnosis BY Transformer Model")
	# st.sidebar.subheader("Input")

	# "st.session_state obj :", st.session_state

	# if 'boolean' not in st.session_state:
	# st.session_state['boolean'] = False

	# "st.session_state obj :", st.session_state

	# models_list = ["--Select--", "40X", "100X", "200X", "400X"]
	# magnitude = st.sidebar.selectbox("Select the Magnification", models_list)

	# uploaded_file = st.sidebar.file_uploader(
	# "Choose images to diagnose", type=["jpg", "jpeg", "png"], accept_multiple_files = True
	# )

	# diagnosis = st.sidebar.button("Diagnose")

	# if diagnosis:
	# st.session_state['boolean'] = True

	# num_classes = 5

	# model = vit_LRP(pretrained=True)
	# num_ftrs = model.head.in_features
	# model.head = Linear(num_ftrs, num_classes)

	# if uploaded_file != [] and st.session_state['boolean'] and magnitude != "--Select--":
	# state_dict = torch.load("./Model/V" + magnitude + ".pth", map_location=torch.device('cpu'))
	# model.load_state_dict(state_dict)
	# model.eval()
	# ag = LRP(model)

	# predictions = []
	# xai = []

	# for each_file in uploaded_file:
	# bytes_data = each_file.read()

	# img = Image.open(BytesIO(bytes_data))

	# inputs = transform(img)
	# outputs = model(inputs.unsqueeze(0))

	# model_predicted = CLS2IDX[print_top_classes(outputs)[0]]

	# if model_predicted == "benign":
	# type = '-'
	# else:
	# type = model_predicted
	# model_predicted = "malignant"

	# xai.append(generate_visualization(inputs, ag))
	# predictions.append([img, each_file.name, model_predicted, type])
	# #st.write(each_file)
	# #st.write(each_file.name)
	# # st.image(img)
	# #st.write(model_predicted)

	# def showim():
	# df = pd.DataFrame(
	# predictions, columns=["Image", "Name", "Result", "Type"]
	# )

	# st.dataframe(
	# df.iloc[:,1:], width=1000
	# )

	# idx = st.select_slider("Select the index of picture", df.index, on_change=showim)
	# st.write(idx)
	# # st.image(predictions[index][0])
	# st.image(xai[idx])

	# showim()
	# print(uploaded_file, diagnosis, magnitude)