app.py

import streamlit as st
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
from PIL import Image



# ------------------------------
# Main app
# ------------------------------
def main():
    st.title("Brain Tumor Detection (MRI Images)")

    # --------------------------
    # Load the trained CNN model
    # --------------------------
    @st.cache_resource
    def load_cnn_model():
        return load_model("multi_modal_brain_tumor_model.h5")  # Make sure this file is in the same directory

    model = load_cnn_model()

    # --------------------------
    # Upload image
    # --------------------------
    uploaded_file = st.file_uploader("Upload a brain MRI image", type=["jpg", "jpeg", "png"])

    if uploaded_file is not None:
        img = Image.open(uploaded_file).convert("RGB")
    
        st.image(img, caption="Uploaded Image", use_container_width=True)


        # ----------------------
        # Preprocess for CNN
        # ----------------------
        img = img.resize((128, 128))  # Update to your model input size
        x = image.img_to_array(img) / 255.0
        x = np.expand_dims(x, axis=0)

        # ----------------------
        # Predict
        # ----------------------
        pred = model.predict(x)
        class_idx = int(np.round(pred[0][0]))
        result = "Malignant" if class_idx == 1 else "Benign"

        st.success(f"Prediction: {result}")

# ------------------------------
# Run the app
# ------------------------------
if __name__ == "__main__":
    main()