Upload app.py

app.py

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+
+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.")