Create util.py

util.py

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+import base64
+
+import streamlit as st
+from PIL import ImageOps, Image
+import numpy as np
+
+
+def set_background(image_file):
+    """
+    This function sets 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 the background.
+
+    Returns:
+        None
+    """
+    with open(image_file, "rb") as f:
+        img_data = f.read()
+    b64_encoded = base64.b64encode(img_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