app.py

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)