test

app.py

@@ -12,69 +12,7 @@ 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()