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import streamlit as st | |
from keras.models import load_model | |
from PIL import Image, ImageOps | |
import numpy as np | |
# Disable scientific notation for clarity | |
np.set_printoptions(suppress=True) | |
# Load the model | |
model = load_model("braintumour_model.h5", compile=False) | |
# Load the labels | |
class_names = open("labels.txt", "r").readlines() | |
# Function to preprocess the image | |
def preprocess_image(image): | |
# resizing the image to be at least 224x224 and then cropping from the center | |
size = (224, 224) | |
image = ImageOps.fit(image, size, Image.Resampling.LANCZOS) | |
# turn the image into a numpy array | |
image_array = np.asarray(image) | |
# Normalize the image | |
normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1 | |
# Return the preprocessed image | |
return normalized_image_array | |
# Function to predict the image class | |
def predict_image_class(image): | |
# Create the array of the right shape to feed into the keras model | |
data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32) | |
# Load the image into the array | |
data[0] = image | |
# Predict the model | |
prediction = model.predict(data) | |
index = np.argmax(prediction) | |
class_name = class_names[index] | |
confidence_score = prediction[0][index] | |
return class_name[2:], round(confidence_score, 2)*100 | |
# Streamlit app | |
def main(): | |
# Set page title and icon | |
st.set_page_config(page_title="Brain Tumour Classifier", page_icon="🧠") | |
st.title("Brain Tumour Classifier") | |
# Upload image file | |
uploaded_file = st.file_uploader("Upload the MRI", type=["jpg", "jpeg", "png"]) | |
if uploaded_file is not None: | |
image = Image.open(uploaded_file).convert("RGB") | |
st.image(image, caption="Uploaded Image", use_column_width=True) | |
preprocessed_image = preprocess_image(image) | |
# Predict the image class | |
class_name, confidence_score = predict_image_class(preprocessed_image) | |
# Display the prediction | |
st.subheader("RESULT") | |
st.write("Class:", "**" + class_name + "**") | |
st.write("Prediction Probability:", "**" + str(confidence_score) + "%**") | |
# Run the app | |
if __name__ == "__main__": | |
main() | |