app.py

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

model = load_model("my_cnn_model.h5")


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


st.title("Image Classification - Cancer Classification :cancer: ")
st.write("Upload an image and the models predicts if it is cancer")

file = st.file_uploader("Select an image",type=["jpg","jpeg","png"])

if file is not None:
    img = Image.open(file)
    st.image(img,caption="Uploaded picture")
    img = process_image(img)
    prediction = model.predict(img)
    predicted_class = np.argmax(prediction)

    class_names = ["Non Cancer","Cancer"]
    st.write(class_names[predicted_class])