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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 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}")
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