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| import streamlit as st | |
| import gradio as gr | |
| import cv2 | |
| import numpy as np | |
| from tensorflow.keras.models import load_model | |
| from tensorflow.keras.applications.vgg16 import preprocess_input | |
| from tensorflow.keras.preprocessing import image | |
| from werkzeug.utils import secure_filename | |
| import os | |
| # Loading Models | |
| braintumor_model = load_model('models/brain_tumor_binary.h5') | |
| # Configuring Streamlit | |
| UPLOAD_FOLDER = 'static/uploads' | |
| ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'} | |
| st.set_page_config(page_title="Brain Tumor Prediction App", page_icon=":brain:") | |
| def allowed_file(filename): | |
| return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS | |
| def preprocess_imgs(set_name, img_size): | |
| set_new = [] | |
| for img in set_name: | |
| img = cv2.resize(img, dsize=img_size, interpolation=cv2.INTER_CUBIC) | |
| set_new.append(preprocess_input(img)) | |
| return np.array(set_new) | |
| def crop_imgs(set_name, add_pixels_value=0): | |
| set_new = [] | |
| for img in set_name: | |
| gray = cv2.GaussianBlur(img, (5, 5), 0) | |
| thresh = cv2.threshold(gray, 45, 255, cv2.THRESH_BINARY)[1] | |
| thresh = cv2.erode(thresh, None, iterations=2) | |
| thresh = cv2.dilate(thresh, None, iterations=2) | |
| cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) | |
| cnts = cnts[0] if len(cnts) == 2 else cnts[1] | |
| c = max(cnts, key=cv2.contourArea) | |
| extLeft = tuple(c[c[:, :, 0].argmin()][0]) | |
| extRight = tuple(c[c[:, :, 0].argmax()][0]) | |
| extTop = tuple(c[c[:, :, 1].argmin()][0]) | |
| extBot = tuple(c[c[:, :, 1].argmax()][0]) | |
| ADD_PIXELS = add_pixels_value | |
| new_img = img[extTop[1] - ADD_PIXELS:extBot[1] + ADD_PIXELS, | |
| extLeft[0] - ADD_PIXELS:extRight[0] + ADD_PIXELS].copy() | |
| set_new.append(new_img) | |
| return np.array(set_new) | |
| # Function to preprocess the image | |
| def preprocess_image(file_path): | |
| img = image.load_img(file_path, target_size=(200, 200)) | |
| img_array = image.img_to_array(img) | |
| img_array = np.expand_dims(img_array, axis=0) | |
| img_array /= 255.0 # Normalize the image | |
| return img_array | |
| def predict_braintumor(img_path): | |
| img_gray = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE) | |
| # Crop and preprocess the grayscale image | |
| img_processed = crop_imgs([img_gray]) | |
| img_processed = preprocess_imgs(img_processed, (224, 224)) | |
| # Make prediction | |
| pred = braintumor_model.predict(img_processed) | |
| # Handle binary decision | |
| confidence = pred[0][0] | |
| if confidence >= 0.5: | |
| return "Brain Tumor Not Found!" | |
| else: | |
| return "Brain Tumor Found!" | |
| def main(): | |
| st.title("Brain Tumor Prediction App") | |
| uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"]) | |
| if uploaded_file is not None: | |
| st.image(uploaded_file, caption="Uploaded Image.", use_column_width=True) | |
| st.write("") | |
| st.write("Classifying...") | |
| # Read the contents of the uploaded file | |
| file_contents = uploaded_file.read() | |
| # Save the uploaded file | |
| filename = secure_filename(uploaded_file.name) | |
| file_path = os.path.join(UPLOAD_FOLDER, filename) | |
| with open(file_path, "wb") as f: | |
| f.write(file_contents) | |
| # Make prediction | |
| result = predict_braintumor(file_path) | |
| # Display prediction | |
| st.subheader("Prediction:") | |
| st.success(result) | |
| if __name__ == "__main__": | |
| main() | |
| gr.Interface(fn=predict_braintumor, inputs="image", outputs="text").launch() | |