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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('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
# Handle binary decision
def binary_decision(confidence):
return 1 if confidence >= 0.5 else 0
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]
return binary_decision(confidence)
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:")
if result == 1:
st.success("Brain Tumor Found!")
else:
st.success("Brain Tumor Not Found!")
if __name__ == "__main__":
# Streamlit app
main()
# Gradio interface
iface = gr.Interface(
fn=predict_braintumor,
inputs="image",
outputs="text",
examples=[["examples/1_no.jpg"], ["examples/2_no.jpg"], ["examples/3_yes.jpg"], ["examples/4_yes.jpg"]],
enable_queue=True
)
iface.launch()
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