app.py

import streamlit as st
import tensorflow as tf
import numpy as np
from PIL import Image

# Load the model
model = tf.keras.models.load_model("brain_stroke_model.keras")

# Set title
st.title("Brain Stroke Prediction")

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

# Prediction function
def predict(image):
    image = image.resize((150, 150))
    img_array = np.array(image) / 255.0
    img_array = np.expand_dims(img_array, axis=0)
    pred = model.predict(img_array)[0][0]
    return pred

# Predict button
if uploaded_file is not None:
    image = Image.open(uploaded_file)
    st.image(image, caption="Uploaded Image", use_column_width=True)
    prediction = predict(image)
    if prediction >= 0.5:
        st.error("❗ Prediction: This person is likely to have a stroke.")
    else:
        st.success("✅ Prediction: This person is not likely to have a stroke.")