Create app.py

app.py

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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
+
+# 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()