Spaces:
Sleeping
Sleeping
File size: 1,164 Bytes
baacefd 20f3cc4 387fd92 baacefd |
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 |
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 = tf.keras.models.load_model("model.h5")
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 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()
|