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import streamlit as st
import tensorflow as tf
import cv2
import numpy as np
from lime import lime_image
from skimage.segmentation import mark_boundaries
import matplotlib.pyplot as plt
from tensorflow.keras.models import load_model
from grad_cam import GradCam
hp = {}
hp['image_size'] = 512
hp['num_channels'] = 3
hp['patch_size'] = 64
hp['num_patches'] = (hp['image_size']**2) // (hp["patch_size"]**2)
hp["flat_patches_shape"] = (hp["num_patches"], hp['patch_size']*hp['patch_size']*hp["num_channels"])
hp['batch_size'] = 32
hp['lr'] = 1e-4
hp["num_epochs"] = 30
hp['num_classes'] = 2
hp["num_layers"] = 12
hp["hidden_dim"] = 512
hp["mlp_dim"] = 3072
hp['num_heads'] = 12
hp['dropout_rate'] = 0.1
hp['class_names'] = ["breast_benign", "breast_malignant"]
model = load_model("model/resnet_for_breast_cancer-v1.h5")
print("Model initiated")
explainer = lime_image.LimeImageExplainer()
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)
gradCam = GradCam(model, image, last_conv_layer_name='conv5_block3_3_conv')
# 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)
gradCam.save_and_display_gradcam()
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
if __name__ == "__main__":
main() |