app.py

import streamlit as st
from PIL import Image
import numpy as np
import joblib

# # Load your trained model (replace 'model.pkl' with your model filename)
# model = joblib.load('model.pkl')

# Function to preprocess the image for prediction
def preprocess_image(image):
    # Convert the image to the format your model expects
    # This is an example, modify as necessary
    image = image.resize((224, 224))  # Resize the image
    image_array = np.array(image) / 255.0  # Normalize the image
    return image_array.reshape(1, 224, 224, 3)  # Adjust shape for model

# Streamlit UI
st.title("Seizure Prediction App")
st.write("Upload an image to predict if it indicates a seizure or not.")

# Image upload
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    # Display the uploaded image
    image = Image.open(uploaded_file)
    st.image(image, caption='Uploaded Image', use_column_width=True)
    
    # Preprocess the image
    processed_image = preprocess_image(image)
    
    # Button to predict
    if st.button("Predict"):
        # Make prediction
        prediction = model.predict(processed_image)
        
        # Display result
        if prediction[0] == 1:
            st.success("The model predicts: Seizure detected!")
        else:
            st.success("The model predicts: No seizure detected.")