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import streamlit as st | |
import tensorflow as tf | |
from tensorflow.keras.preprocessing import image | |
import numpy as np | |
from PIL import Image | |
import base64 | |
H = 256 | |
W = 256 | |
from metrics import dice_loss, dice_coef | |
model_path = "model.h5" | |
model = tf.keras.models.load_model(model_path,custom_objects={'dice_loss': dice_loss, 'dice_coef': dice_coef}) | |
st.set_page_config( | |
page_title="Brain Tumor Segmentation App", | |
page_icon=":brain:", | |
layout="wide" | |
) | |
custom_style = """ | |
<style> | |
div[data-testid="stToolbar"], | |
div[data-testid="stDecoration"], | |
div[data-testid="stStatusWidget"], | |
#MainMenu, | |
header, | |
footer { | |
visibility: hidden; | |
height: 0%; | |
} | |
</style> | |
""" | |
st.markdown(custom_style, unsafe_allow_html=True) | |
def main(): | |
st.title("Brain Tumor Segmentation") | |
uploaded_file = st.file_uploader("Upload an MRI image for tumor segmentation...", type=["jpg", "png", "jpeg"]) | |
if uploaded_file is not None: | |
original_image = Image.open(uploaded_file) | |
st.image(original_image, caption="Uploaded Image", use_column_width=True) | |
st.markdown("## Tumor Segmentation Result") | |
if __name__ == "__main__": | |
main() | |