Create app.py

app.py

@@ -0,0 +1,41 @@
+import streamlit as st
+import tensorflow as tf
+from PIL import Image
+import os
+
+model = tf.keras.models.load_model('Brain_tumor/')
+st.write('Model is loaded successfully')
+
+TEMP_DIR = 'temp'
+if not os.path.exists(TEMP_DIR):
+    os.makedirs(TEMP_DIR)
+
+class_names = ['glioma', 'meningioma', 'notumor', 'pituitary']
+
+def load_and_prep_imgg(filename, img_shape=229, scale=True):
+    img = tf.io.read_file(filename)
+    img = tf.io.decode_image(img)
+    img = tf.image.resize(img, size=[img_shape, img_shape])
+    if scale:
+        return img / 255
+    else:
+        return img
+
+st.title('Brain Tumor Classification Prediction using Xception ImageNet')
+
+uploaded_file = st.sidebar.file_uploader('Upload your Image', type=['jpg'])
+
+if uploaded_file:
+    file_path = os.path.join(TEMP_DIR, uploaded_file.name)
+    
+    # Save the uploaded file to the temporary directory
+    with open(file_path, "wb") as f:
+        f.write(uploaded_file.getbuffer())
+    
+    img = load_and_prep_imgg(file_path, scale=True)
+    imgg = Image.open(file_path)
+    st.image(imgg, caption="Uploaded Image")
+
+    pred_img = model.predict(tf.expand_dims(img, axis=0))
+    pred_class = class_names[pred_img.argmax()]
+    st.write(f"Predicted brain tumor is: {pred_class} with probability: {pred_img.max():.2f}")