util.py

import base64


import streamlit as st
import numpy as np
from PIL import ImageOps, Image


def set_background(image_file):
    """
    This function set the background of a streamlit app to  an image specified by the given image file

    Parameters:
    image_file(str): The path to the image file to be used as teh background

    returns:
    None
    """
    with open(image_file,"rb") as f:
        image_data = f.read()
    b64_encoded = base64.b64encode(image_data).decode()
    style = f"""
        <style>
        .stApp{{
            background-image: url(data:image/png;base64,{b64_encoded});
            background-size: cover;
        }}
        </style>
    """
    st.markdown(style, unsafe_allow_html=True)


def classify(image, model, class_names):
    """
    This function takes an image, a model, and a list of class names and returns the predicted class and confidence
    score of the image.

    Parameters:
        image (PIL.Image.Image): An image to be classified.
        model (tensorflow.keras.Model): A trained machine learning model for image classification.
        class_names (list): A list of class names corresponding to the classes that the model can predict.

    Returns:
        A tuple of the predicted class name and the confidence score for that prediction.
    """
    # convert image to (224, 224)
    image = ImageOps.fit(image, (224, 224), Image.Resampling.LANCZOS)

    # convert image to numpy array
    image_array = np.asarray(image)

    # normalize image
    normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1

    # set model input
    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
    data[0] = normalized_image_array

    # make prediction
    prediction = model.predict(data)
    # index = np.argmax(prediction)
    index = 0 if prediction[0][0] > 0.95 else 1
    class_name = class_names[index]
    confidence_score = prediction[0][index]

    return class_name, confidence_score