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

# Load the pre-trained brain tumor segmentation model
model_path = 'brain_tumor_segmentation_model.h5'  # Replace with your actual model path
model = tf.keras.models.load_model(model_path)
img_size = (256, 256)  # Adjust based on your model's input size

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 preprocess_image(img):
    img = img.resize(img_size)
    img_array = image.img_to_array(img)
    img_array = img_array / 255.0
    img_array = np.expand_dims(img_array, axis=0)
    return img_array

def predict_tumor_segmentation(img):
    img_array = preprocess_image(img)
    segmentation_mask = model.predict(img_array)
    segmentation_mask = np.squeeze(segmentation_mask, axis=0)
    return segmentation_mask

def display_segmentation_result(original_image, segmentation_mask):
    original_image = np.array(original_image)
    segmentation_mask = (segmentation_mask > 0.5).astype(np.uint8) * 255
    segmentation_mask = Image.fromarray(segmentation_mask, 'L')

    st.image([original_image, segmentation_mask], caption=['Original Image', 'Segmentation Mask'], use_column_width=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")

        # Perform segmentation
        segmentation_mask = predict_tumor_segmentation(original_image)

        # Display the segmentation result
        display_segmentation_result(original_image, segmentation_mask)

if __name__ == "__main__":
    main()