app.py

import streamlit as st
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np

model = load_model('skin_cancer_cnn_model.h5')


def process_image(img):
    img= img.resize((170,170))
    img = np.array(img)
    img = img/255.0 # normalize
    img = np.expand_dims(img,axis=0)
    return img


st.title('Skin Cancer Classification :cancer:')
st.write('Upload Test Image')


file = st.file_uploader('Enter Image', type=['jpg','png','jpeg'])

if file is not None:
    img=Image.open(file)
    st.image(img,caption='Uploaded Image')
    image = process_image(img)
    prediction = model.predict(image)
    predicted_class = np.argmax(prediction)
    class_names = ['Cancer !','Not Cancer !']
    st.write(class_names[predicted_class])