File size: 7,076 Bytes
a3a57c6 c7d03f1 a3a57c6 8623c4c a3a57c6 a97a6b4 a3a57c6 0854c30 a3a57c6 a97a6b4 a3a57c6 a97a6b4 a3a57c6 0854c30 a3a57c6 a97a6b4 a3a57c6 0854c30 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 |
import streamlit as st
import nibabel as nib
import os.path
import os
from nilearn import plotting
import torch
from monai.transforms import (
EnsureChannelFirst,
Compose,
Resize,
ScaleIntensity,
LoadImage,
)
import torch.nn.functional as F
import numpy as np
from statistics import mean
from constants import CLASSES
from model.download_model import load_model
from huggingface_hub import hf_hub_download
#SET PAGE TITLE
st.set_page_config(page_title = "Alzheimer Classifier", page_icon = ":brain:", layout = "wide")
#LOAD MODEL
model = load_model()
#SET NIFTI FILE LOADING AND PROCESSING CONFIGURATIONS
transforms = Compose([
ScaleIntensity(),
EnsureChannelFirst(),
Resize((96, 96, 96)),
])
load_img = LoadImage(image_only=True)
#SET CLASSES
class_names = CLASSES
#SET STREAMLIT SESSION STATES
if 'clicked_pp' not in st.session_state:
st.session_state.clicked_pp = False
if 'clicked_pred' not in st.session_state:
st.session_state.clicked_pred = False
def click_pp_true():
st.session_state.clicked_pp = True
def click_pred_true():
st.session_state.clicked_pred = True
def click_false():
st.session_state.clicked_pp = False
st.session_state.clicked_pred = False
###########################################################
###################### STREAMLIT APP ######################
###########################################################
with st.sidebar:
st.title("Alzheimer Classifier Demo")
img_path = st.selectbox(
"Select Image",
tuple(class_names),
on_change= click_false,
)
col1, col2 = st.columns((1,1))
with col1:
run_preprocess = st.button("Preprocess Image", on_click=click_pp_true)
if st.session_state.clicked_pp:
with col2:
run_pred = st.button("Run Prediction", on_click= click_pred_true)
with st.container():
if img_path != "":
if st.session_state.clicked_pp:
if st.session_state.clicked_pred == False:
with st.container():
pred_image = nib.load(hf_hub_download(repo_id= "rootstrap-org/Alzheimer-Classifier-Demo", repo_type="dataset", subfolder="preprocessed", filename = img_path + ".nii.gz"))
bounds_pred = plotting.find_cuts._get_auto_mask_bounds(pred_image)
st.sidebar.write("#")
y_value_pred = st.sidebar.slider('Move the slider to adjust the coronal cut ', bounds_pred[1][0], bounds_pred[1][1], mean([bounds_pred[1][0], bounds_pred[1][1]]))
x_value_pred = st.sidebar.slider('Move the slider to adjust the sagittal cut ', bounds_pred[0][0], bounds_pred[0][1], mean([bounds_pred[0][0], bounds_pred[0][1]]))
z_value_pred = st.sidebar.slider('Move the slider to adjust the axial cut ', bounds_pred[2][0], bounds_pred[2][1], mean([bounds_pred[2][0], bounds_pred[2][1]]))
plotting.plot_img(pred_image, cmap="grey", cut_coords=(x_value_pred,y_value_pred,z_value_pred), black_bg=True)
st.pyplot()
else:
with st.container():
pred_image = nib.load(hf_hub_download(repo_id= "rootstrap-org/Alzheimer-Classifier-Demo", repo_type="dataset", subfolder="preprocessed", filename = img_path + ".nii.gz"))
bounds_pred = plotting.find_cuts._get_auto_mask_bounds(pred_image)
st.sidebar.write("#")
y_value_pred = st.sidebar.slider('Move the slider to adjust the coronal cut ', bounds_pred[1][0], bounds_pred[1][1], mean([bounds_pred[1][0], bounds_pred[1][1]]))
x_value_pred = st.sidebar.slider('Move the slider to adjust the sagittal cut ', bounds_pred[0][0], bounds_pred[0][1], mean([bounds_pred[0][0], bounds_pred[0][1]]))
z_value_pred = st.sidebar.slider('Move the slider to adjust the axial cut ', bounds_pred[2][0], bounds_pred[2][1], mean([bounds_pred[2][0], bounds_pred[2][1]]))
img_array = load_img(hf_hub_download(repo_id= "rootstrap-org/Alzheimer-Classifier-Demo", repo_type="dataset", subfolder="preprocessed", filename = img_path + ".nii.gz"))
new_data = transforms(img_array)
new_data_tensor = torch.from_numpy(np.array([new_data]))
with torch.no_grad():
output = model(new_data_tensor)
probabilities = F.softmax(output, dim=1)
probabilities_np = probabilities.numpy()
probabilities_item = probabilities_np[0]
probabilities_percentage = probabilities_item * 100
predicted_class_index = np.argmax(probabilities_np[0])
predicted_class_name = class_names[predicted_class_index]
predicted_probability = probabilities_percentage[predicted_class_index]
st.sidebar.write("#")
if predicted_class_index == 0:
color_name = "red"
elif predicted_class_index == 1:
color_name = "blue"
elif predicted_class_index == 2:
color_name = "green"
if predicted_probability > 80:
color_prob = "green"
elif predicted_probability > 60:
color_prob = "yellow"
else:
color_prob = "red"
class_col, pred_col = st.columns((1,1))
with class_col:
st.write(f"### Predicted Class: :{color_name}[{predicted_class_name}]")
with pred_col:
st.write(f"### Probability: :{color_prob}[{predicted_probability:.2f}%]")
plotting.plot_img(pred_image, cmap="grey", cut_coords=(x_value_pred,y_value_pred,z_value_pred), black_bg=True)
st.pyplot()
else:
raw_image = nib.load(hf_hub_download(repo_id= "rootstrap-org/Alzheimer-Classifier-Demo", repo_type="dataset", subfolder="raw", filename = img_path + ".nii"))
bounds_raw = plotting.find_cuts._get_auto_mask_bounds(raw_image)
st.sidebar.write("#")
y_value_raw = st.sidebar.slider('Move the slider to adjust the coronal cut', bounds_raw[1][0], bounds_raw[1][1], mean([bounds_raw[1][0], bounds_raw[1][1]]))
x_value_raw = st.sidebar.slider('Move the slider to adjust the sagittal cut', bounds_raw[0][0], bounds_raw[0][1], mean([bounds_raw[0][0], bounds_raw[0][1]]))
z_value_raw = st.sidebar.slider('Move the slider to adjust the axial cut', bounds_raw[2][0], bounds_raw[2][1], mean([bounds_raw[2][0], bounds_raw[2][1]]))
plotting.plot_img(raw_image, cmap = "grey", cut_coords=(x_value_raw,y_value_raw,z_value_raw), black_bg=True)
st.pyplot()
|