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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 | |
# Loading Models | |
braintumor_model = load_model('models/brain_tumor_binary.h5') | |
# Configuring Streamlit | |
st.set_page_config( | |
page_title="Brain Tumor Prediction App", | |
page_icon=":brain:", | |
layout="centered", | |
) | |
# Streamlit app title and description | |
st.title("Brain Tumor Prediction App") | |
st.write( | |
"Upload an image, and the app will predict whether a brain tumor is present or not." | |
) | |
# Add Streamlit sidebar for additional information or options | |
st.sidebar.title("About") | |
st.sidebar.info( | |
"This app uses a trained model to predict brain tumors from images. " | |
"The model is based on VGG16 architecture." | |
) | |
# Function to preprocess the image | |
def preprocess_image(img): | |
# If it's a NumPy array, use it directly | |
if isinstance(img, np.ndarray): | |
img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) | |
else: | |
# Convert Gradio image data to bytes | |
img_bytes = img.read() | |
# Convert to NumPy array | |
nparr = np.frombuffer(img_bytes, np.uint8) | |
# Decode image | |
img_gray = cv2.imdecode(nparr, cv2.IMREAD_GRAYSCALE) | |
# Crop and preprocess the grayscale image | |
img_processed = preprocess_imgs([img_gray], (224, 224)) | |
return img_processed | |
# Handle binary decision | |
def binary_decision(confidence): | |
return 1 if confidence >= 0.5 else 0 | |
def predict_braintumor(img): | |
# Preprocess the image | |
img_processed = preprocess_image(img) | |
# Make prediction | |
pred = braintumor_model.predict(img_processed) | |
# Handle binary decision | |
confidence = pred[0][0] | |
return "Brain Tumor Not Found!" if binary_decision(confidence) == 1 else "Brain Tumor Found!" | |
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) | |
# Gradio interface | |
iface = gr.Interface( | |
fn=predict_braintumor, | |
inputs="image", | |
outputs="text", | |
examples=[["examples/1_no.jpeg"], ["examples/2_no.jpeg"], ["examples/3_no.jpg"], ["examples/Y1.jpg"], ["examples/Y2.jpg"], ["examples/Y3.jpg"]], | |
live=True # Allows real-time updates without restarting the app | |
) | |
# Display Gradio interface | |
iface.launch() | |
# Streamlit components below the Gradio interface | |
uploaded_file = st.file_uploader("Choose an MRI image", type=["jpg", "jpeg"]) | |
if uploaded_file is not None: | |
# Display the uploaded image | |
st.image(uploaded_file, caption="Uploaded MRI Image.", use_column_width=True) | |
# Perform prediction when the "Predict" button is clicked | |
if st.button("Predict"): | |
# Preprocess the image | |
img_array = preprocess_image(uploaded_file) | |
# Make prediction | |
pred = braintumor_model.predict(img_array) | |
# Handle binary decision | |
confidence = pred[0][0] | |
result = "Brain Tumor Not Found!" if binary_decision(confidence) == 1 else "Brain Tumor Found!" | |
# Display the prediction result | |
st.write(result) | |