brain.py

import streamlit as st



from tensorflow.keras.models import load_model



import numpy as np

from tensorflow.keras.preprocessing.image import load_img, img_to_array

from PIL import Image



st.header("Brain Tumor Detection")

st.caption("Upload an image. ")

st.caption("The application will infer the one label out of 4 labels: 'no_tumor', 'pituitary_tumor', 'meningioma_tumor', 'glioma_tumor'.")

st.caption("Warning: Do not click Recognize button before uploading image. It will result in error.")




model = load_model("Brain_Tumor_Image_Classification_Model(2).h5",compile=False)
model.compile(
    loss = 'categorical_crossentropy',
    optimizer = 'Adam',
    metrics = ['accuracy']
)





# Define the class names

class_names = ['no_tumor', 'pituitary_tumor', 'meningioma_tumor', 'glioma_tumor']





# Fxn
@st.cache_data
def load_image(image_file):

        img = Image.open(image_file)

        return img



imgpath = st.file_uploader("Choose a file", type =['png', 'jpeg', 'jpg'])

if imgpath is not None:

    img = load_image(imgpath )

    st.image(img, width=224)



def predict_label(image2):

    imgLoaded = load_img(image2, target_size=(224, 224))

    # Convert the image to an array

    img_array = img_to_array(imgLoaded)    #print(img_array)



    #print(img_array.shape)



    img_array = np.reshape(img_array, (1, 224, 224, 3))



    # Get the model predictions

    predictions = model.predict(img_array)

    #print("predictions:", predictions)



    # Get the class index with the highest predicted probability

    class_index = predictions.argmax()



    # Get the predicted class label

    predicted_label = class_names[class_index]

    return predicted_label

if st.button('Recognise'):

    predicted_label = predict_label(imgpath)

    st.write("The image is predicted to be '{}'.".format(predicted_label))