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import streamlit as st
import tensorflow as tf
import cv2
import numpy as np
from patchify import patchify
from huggingface_hub import from_pretrained_keras
model = from_pretrained_keras('ErnestBeckham/MulticancerViT')
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['class_names'] = ['cervix_koc',
'cervix_dyk',
'cervix_pab',
'cervix_sfi',
'cervix_mep',
'colon_bnt',
'colon_aca',
'lung_aca',
'lung_bnt',
'lung_scc',
'oral_scc',
'oral_normal',
'kidney_tumor',
'kidney_normal',
'breast_benign',
'breast_malignant',
'lymph_fl',
'lymph_cll',
'lymph_mcl',
'brain_tumor',
'brain_glioma',
'brain_menin']
def main():
st.title("Multi-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 detect_image_shape(image):
# Get the image shape
return image.shape
def preprocess_image(image, hp):
# Resize the image to the expected input size
image = cv2.resize(image, (hp['image_size'], hp['image_size']))
# Normalize pixel values to be in the range [0, 1]
image = image / 255.0
# Extract patches using the same patching mechanism as during training
patch_shape = (hp['patch_size'], hp['patch_size'], hp['num_channels'])
patches = patchify(image, patch_shape, hp['patch_size'])
# Flatten the patches
patches = np.reshape(patches, hp['flat_patches_shape'])
# Convert the flattened patches into a format suitable for prediction
patches = patches.astype(np.float32)
return patches
def predict_single_image(image, model, hp):
# Preprocess the image
preprocessed_image = preprocess_image(image, hp)
# 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() |