application updated

app.py

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+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 = from_pretrained_keras('ErnestBeckham/BreastResViT')
+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)
+        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()