Create app.py

app.py

@@ -0,0 +1,42 @@
+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 Classidfication Predition using Xception ImageNet ')
+
+uploaded_file = st.sidebar.file_uploader('Upload your Image', type=['jpg'])
+
+if uploaded_file:
+    
+    #file_path = os.path.join(uploaded_file.name)
+    img = load_and_prep_imgg(uploaded_file.name,scale=True)
+    imgg = Image.open(uploaded_file.name)
+    st.image(img,caption ="Predicted brain tumor is : {pred_class} with probs : {pred_img:max():.2f}" )
+    
+    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 probs : {pred_img:max():.2f}")
+