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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}")
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