app.py

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()