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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
smooth = 1e-15
def dice_loss(y_true, y_pred):
y_true = tf.keras.layers.Flatten()(y_true)
y_pred = tf.keras.layers.Flatten()(y_pred)
intersection = tf.reduce_sum(y_true * y_pred)
return 1.0 - (2. * intersection + smooth) / (tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) + smooth)
def dice_coef(y_true, y_pred):
y_true = tf.keras.layers.Flatten()(y_true)
y_pred = tf.keras.layers.Flatten()(y_pred)
intersection = tf.reduce_sum(y_true * y_pred)
return (2. * intersection + smooth) / (tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) + smooth)
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 App")
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()
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