app updated

app.py

@@ -1,4 +1,41 @@
 import streamlit as st
+from PIL import Image
+import numpy as np
+import joblib
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# # 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.")