Delete app.py

app.py

@@ -1,42 +0,0 @@
-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(TEMP_DIR, uploaded_file.name)
-    img = load_and_prep_imgg(file_path,scale=True)
-    imgg = Image.open(file_path)
-    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}")
-