app.py

import streamlit as st
import tensorflow as tf
import cv2
import numpy as np
from huggingface_hub import from_pretrained_keras
from lime import lime_image
from skimage.segmentation import mark_boundaries
import matplotlib.pyplot as plt


#model = tf.keras.models.load_model("model/resnet_for_breast_cancer-v1.h5")
model = from_pretrained_keras("ErnestBeckham/BreastResViT-II")
explainer = lime_image.LimeImageExplainer()

hp = {}
hp['class_names'] = ["breast_benign", "breast_malignant"]

def main():
    st.title("Breast Cancer Classification")

    # Upload image through drag and drop
    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

    if uploaded_file is not None:
        # Convert the uploaded file to OpenCV format
        image = convert_to_opencv(uploaded_file)
        
        # Display the uploaded image
        st.image(image, channels="BGR", caption="Uploaded Image", use_column_width=True)

        # Display the image shape
        image_class = predict_single_image(image, model, hp)
        xai_result("lime_Ex.png")
        st.write(f"Image Class: {image_class}")
        

def convert_to_opencv(uploaded_file):
    # Read the uploaded file using OpenCV
    image_bytes = uploaded_file.read()
    np_arr = np.frombuffer(image_bytes, np.uint8)
    image = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
    return image

def process_image_as_batch(image):
    #resize the image
    image = cv2.resize(image, [512, 512])
    #scale the image
    image = image / 255.0
    #change the data type of image
    image = image.astype(np.float32)
    return image

def predict_single_image(image, model, hp):
    # Preprocess the image
    preprocessed_image = process_image_as_batch(image)
    # Convert the preprocessed image to a TensorFlow tensor if needed
    preprocessed_image = tf.convert_to_tensor(preprocessed_image)
    # Add an extra batch dimension (required for model.predict)
    preprocessed_image = tf.expand_dims(preprocessed_image, axis=0)
    # Make the prediction
    predictions = model.predict(preprocessed_image)

    np.around(predictions)
    y_pred_classes = np.argmax(predictions, axis=1)
    class_name = hp['class_names'][y_pred_classes[0]]
    return class_name


def xai_result(image):
    path = "lime_explanation.png"
    tem = cv2.resize(image, [512,512])
    gray_img = cv2.cvtColor(tem, cv2.COLOR_BGR2GRAY)
    explanation = explainer.explain_instance(gray_img.astype('double'),
    model.predict,
    top_labels=1000, hide_color=0,  num_samples=1000)
    temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=True, num_features=5, hide_rest=True)
    plt.imshow(mark_boundaries(temp / 2 + 0.5, mask), interpolation='nearest')
    plt.savefig(path)
    

if __name__ == "__main__":
    main()