Delete app.py

app.py

@@ -1,32 +0,0 @@
-
-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.")