app.py

import streamlit as st
import numpy as input
from tensorflow.keras.preprocessing.image import load_img, img_to_array


def main():
    
    st.title('Breast Cancer Detection App')
    st.write("Created by: [Umair Akram](https://www.linkedin.com/in/m-umair01/)")
   
    h1 = "This App uses Deep Learning to predict whether you have Breast Cancer or not!"
    st.subheader(h1)
    st.write("The model is built using Convolutional Neural Network (CNN) in TensorFlow. Its code and other interesting projects are available on my [website](https://mumairab.github.io/)")

    h2 = "Enter the following values to know the status of your health"
    st.write(h2)
    loaded_img = st.file_uploader(label=color["Upload the Histopathological Image patch of breast"],
                     type=None, accept_multiple_files=False,
                     key="Img_upload_key", 
                     label_visibility="visible")

    if uploaded_file is not None:
    # Convert the file to TensorFlow image.
    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
    img = load_img(file_bytes, target_size=(50,50))
    #Rescale the image from [0,255] to [0,1]
    np_image = np.array(image).astype('float32')/255.0
    
    image = tf.keras.utils.load_img(image_path)
    input_arr = tf.keras.utils.img_to_array(image)
    input_arr = np.array([input_arr])  # Convert single image to a batch.
    predictions = model.predict(input_arr)