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strexp
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import streamlit as st
from PIL import Image
import settings
import captum
import numpy as np
import torch
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
from utils import get_args
from utils import CTCLabelConverter, AttnLabelConverter, Averager, TokenLabelConverter
import string
import time
import sys
from dataset import hierarchical_dataset, AlignCollate
import validators
from model import Model, STRScore
from PIL import Image
from lime.wrappers.scikit_image import SegmentationAlgorithm
from captum._utils.models.linear_model import SkLearnLinearModel, SkLearnRidge
import random
import os
from skimage.color import gray2rgb
import pickle
from train_shap_corr import getPredAndConf
import re
from captum_test import acquire_average_auc, saveAttrData
import copy
from skimage.color import gray2rgb
from matplotlib import pyplot as plt
from torchvision import transforms

device = torch.device('cpu')
opt = get_args(is_train=False)

""" vocab / character number configuration """
if opt.sensitive:
    opt.character = string.printable[:-6]  # same with ASTER setting (use 94 char).

cudnn.benchmark = True
cudnn.deterministic = True
# opt.num_gpu = torch.cuda.device_count()

# combineBestDataXAI(opt)
# acquire_average_auc(opt)
# acquireSingleCharAttrAve(opt)
modelName = "parseq"
opt.modelName = modelName
# opt.eval_data = "datasets/data_lmdb_release/evaluation"

if modelName=="vitstr":
    opt.benchmark_all_eval = True
    opt.Transformation = "None"
    opt.FeatureExtraction = "None"
    opt.SequenceModeling = "None"
    opt.Prediction = "None"
    opt.Transformer = True
    opt.sensitive = True
    opt.imgH = 224
    opt.imgW = 224
    opt.data_filtering_off = True
    opt.TransformerModel= "vitstr_base_patch16_224"
    opt.saved_model = "pretrained/vitstr_base_patch16_224_aug.pth"
    opt.batch_size = 1
    opt.workers = 0
    opt.scorer = "mean"
    opt.blackbg = True
elif modelName=="parseq":
    opt.benchmark_all_eval = True
    opt.Transformation = "None"
    opt.FeatureExtraction = "None"
    opt.SequenceModeling = "None"
    opt.Prediction = "None"
    opt.Transformer = True
    opt.sensitive = True
    opt.imgH = 32
    opt.imgW = 128
    opt.data_filtering_off = True
    opt.batch_size = 1
    opt.workers = 0
    opt.scorer = "mean"
    opt.blackbg = True

# x = st.slider('Select a value')
# st.write(x, 'squared is', x * x)

image = Image.open('demo_image/demo_ballys.jpg') #Brand logo image (optional)
#Create two columns with different width
col1, col2 = st.columns( [0.8, 0.2])
with col1:               # To display the header text using css style
    st.markdown(""" <style> .font {
    font-size:35px ; font-family: 'Cooper Black'; color: #FF9633;}
    </style> """, unsafe_allow_html=True)
    st.markdown('<p class="font">Upload your photo here...</p>', unsafe_allow_html=True)
with col2:               # To display brand logo
    st.image(image,  width=150)

uploaded_file = st.file_uploader("Choose a file", type=["png", "jpg"])
if uploaded_file is not None:
    # To read file as bytes:
    bytes_data = uploaded_file.getvalue()
    pilImg = Image.open(uploaded_file)

    orig_img_tensors = transforms.ToTensor()(pilImg).unsqueeze(0)
    img1 = orig_img_tensors.to(device)
    # image_tensors = ((torch.clone(orig_img_tensors) + 1.0) / 2.0) * 255.0
    image_tensors = torch.mean(orig_img_tensors, dim=1).unsqueeze(0).unsqueeze(0)
    imgDataDict = {}
    img_numpy = image_tensors.cpu().detach().numpy()[0] ### Need to set batch size to 1 only
    if img_numpy.shape[0] == 1:
        img_numpy = gray2rgb(img_numpy[0])
    # print("img_numpy shape: ", img_numpy.shape) # (1, 32, 128, 3)
    segmOutput = segmentation_fn(img_numpy[0])

    results_dict = {}
    aveAttr = []
    aveAttr_charContrib = []
    target = converter.encode([labels])

    # labels: RONALDO
    segmDataNP = segmOutput
    img1.requires_grad = True
    bgImg = torch.zeros(img1.shape).to(device)

    # preds = model(img1, seqlen=converter.batch_max_length)
    input = img1
    origImgNP = torch.clone(orig_img_tensors).detach().cpu().numpy()[0][0] # (1, 1, 224, 224)
    origImgNP = gray2rgb(origImgNP)
    charOffset = 0
    img1 = transforms.Normalize(0.5, 0.5)(img1) # Between -1 to 1
    target = converter.encode([labels])

    ### Local explanations only
    collectedAttributions = []
    for charIdx in range(0, len(labels)):
        scoring_singlechar.setSingleCharOutput(charIdx + charOffset)
        gtClassNum = target[0][charIdx + charOffset]

        gs = GradientShap(super_pixel_model_singlechar)
        baseline_dist = torch.zeros((1, 3, opt.imgH, opt.imgW))
        baseline_dist = baseline_dist.to(device)
        attributions = gs.attribute(input, baselines=baseline_dist, target=0)
        collectedAttributions.append(attributions)
    aveAttributions = torch.mean(torch.cat(collectedAttributions,dim=0), dim=0).unsqueeze(0)
    # if not torch.isnan(aveAttributions).any():
    #     rankedAttr = rankedAttributionsBySegm(aveAttributions, segmDataNP)
    #     rankedAttr = rankedAttr.detach().cpu().numpy()[0][0]
    #     rankedAttr = gray2rgb(rankedAttr)
    #     mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map', cmap='RdYlGn')
    #     mplotfig.savefig(outputDir + '{}_shapley_l.png'.format(nameNoExt))
    #     mplotfig.clear()
    #     plt.close(mplotfig)

    ### Local Sampling
    gs = GradientShap(super_pixel_model)
    baseline_dist = torch.zeros((1, 3, opt.imgH, opt.imgW))
    baseline_dist = baseline_dist.to(device)
    attributions = gs.attribute(input, baselines=baseline_dist, target=0)
    # if not torch.isnan(attributions).any():
    #     collectedAttributions.append(attributions)
    #     rankedAttr = rankedAttributionsBySegm(attributions, segmDataNP)
    #     rankedAttr = rankedAttr.detach().cpu().numpy()[0][0]
    #     rankedAttr = gray2rgb(rankedAttr)
    #     mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map', cmap='RdYlGn')
    #     mplotfig.savefig(outputDir + '{}_shapley.png'.format(nameNoExt))
    #     mplotfig.clear()
    #     plt.close(mplotfig)

    ### Global + Local context
    aveAttributions = torch.mean(torch.cat(collectedAttributions,dim=0), dim=0).unsqueeze(0)
    if not torch.isnan(aveAttributions).any():
        rankedAttr = rankedAttributionsBySegm(aveAttributions, segmDataNP)
        rankedAttr = rankedAttr.detach().cpu().numpy()[0][0]
        rankedAttr = gray2rgb(rankedAttr)
        mplotfig, _ = visualize_image_attr(rankedAttr, origImgNP, method='blended_heat_map', cmap='RdYlGn')
        fig = mplotfig.figure(figsize=(8,8))
        st.pyplot(fig)
        # mplotfig.savefig(outputDir + '{}_shapley_gl.png'.format(nameNoExt))
        # mplotfig.clear()
        # plt.close(mplotfig)