app.py

import streamlit as st
import tensorflow as tf
import numpy as np
import cv2
from skimage import io
from PIL import Image
import matplotlib.pyplot as plt
import io as sysio



# Load models
model = tf.keras.models.load_model("your_cnn_model.keras", compile=False)
model_seg = tf.keras.models.load_model("model_seg30.keras", compile=False)

st.title("🧠 Brain Tumor Detection & Segmentation")
st.write("Upload an MRI scan to detect and segment tumor regions.")

uploaded_file = st.file_uploader("Choose an MRI Image", type=["png", "jpg", "jpeg"])
if uploaded_file is not None:
    # Load image using skimage.io for consistency with your Colab code
    image_bytes = uploaded_file.read()
    image = Image.open(sysio.BytesIO(image_bytes)).convert("RGB")
    img_original = np.array(image)
    
    # Preprocessing
    img = img_original.copy()
    img = cv2.resize(img, (256, 256))
    img = np.array(img, dtype=np.float64)
    img -= img.mean()
    img /= img.std()
    img = np.expand_dims(img, axis=0)

    # Detection
    prediction = model.predict(img)
    predicted_class = np.argmax(prediction)
    
    if predicted_class == 1:
        st.markdown("### ⚠️ Tumor Detected! Proceeding to Segmentation...")

        # Segmentation
        predicted_mask = model_seg.predict(img)[0].squeeze()
        predicted_mask = (predicted_mask > 0.5).astype(int)

        # Resize original for display
        img_display = cv2.resize(img_original, (256, 256))

        # Plot original and mask
        st.pyplot(plt.figure(figsize=(10, 5)))
        plt.subplot(1, 2, 1)
        plt.imshow(img_display)
        plt.title('Uploaded MRI Image')
        plt.axis('off')

        plt.subplot(1, 2, 2)
        plt.imshow(predicted_mask, cmap='gray')
        plt.title('Predicted Tumor Mask')
        plt.axis('off')
        st.pyplot()

        # Overlay
        overlay = img_display.copy()
        overlay[predicted_mask == 1] = [0, 255, 0]  # green
        st.image(overlay, caption="MRI with Predicted Tumor Overlay", use_column_width=True)

    else:
        st.success("✅ No Tumor Detected.")