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import streamlit as st
import tensorflow as tf
from PIL import Image
import numpy as np
from tensorflow.keras.applications.efficientnet import EfficientNetB7 as PretrainedModel, preprocess_input
from tensorflow.keras.layers import GlobalAveragePooling2D, Flatten, Dense
from tensorflow.keras.models import Model
from skimage.filters import threshold_otsu
import cv2
import time
X = Y = 224
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
def preprocess_image(image, predicted_label):
img= cv2.imread(image.name)
img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
thresh = threshold_otsu(img_gray)
img_otsu = img_gray < thresh
total_area = img_otsu.size
black_area = np.count_nonzero(img_otsu == 0)
white_area = np.count_nonzero(img_otsu == 1)
#print(total_area)
#print(white_area)
#print(black_area)
if predicted_label != 'lung_n':
if white_area >= 300000:
level = 3
elif 200000 <= white_area < 3000000:
level = 2
elif 100000 <= white_area < 200000:
level = 1
elif white_area < 100000:
level = 0
if level == 3:
st.error("Cancer type: "+ predicted_label )
st.error("Level of Cancer: 3")
#st.error("Please consult a doctor immediately")
elif level == 2:
st.warning("Cancer type: "+ predicted_label )
st.warning("Level of Cancer: 2")
elif level == 1:
st.info("Cancer type: "+ predicted_label)
st.info("Level of Cancer: 1")
elif level == 0:
st.success("Cancer type: "+ predicted_label)
st.success("Level of Cancer: 0")
else:
st.success("Predicted as Lung with Benign Tumor")
# Create a Streamlit application
def main():
st.title('Lung Cancer Detection & Severity Level using Deep Learning')
input_shape = (X, Y, 3)
K = 3
start_time = time.time()
ptm = PretrainedModel(
input_shape=(X, Y, 3),
weights='imagenet',
include_top=False
)
ptm.trainable = False
x = GlobalAveragePooling2D()(ptm.output)
x = Flatten()(x)
x = Dense(128, activation='relu')(x)
x = Dense(64, activation='relu')(x)
y = Dense(K, activation='softmax')(x)
model = Model(inputs=ptm.input, outputs=y)
model.load_weights('lungs_weights.h5')
# model.save('lungs_model.h5')
label_map = {0: 'lung_Adenocarcinoma', 1: 'lung_benign', 2: 'lung_squamous cell carcinoma'}
uploaded_image = st.file_uploader('Upload an image')
if uploaded_image is not None:
image = Image.open(uploaded_image)
image = image.resize((224, 224))
image_array = np.array(image)
image_array = preprocess_input(image_array)
image_array = np.expand_dims(image_array, axis=0)
predictions = model.predict(image_array)
predicted_class = np.argmax(predictions)
predicted_label = label_map[predicted_class]
preprocess_image(uploaded_image, predicted_label)
end_time = time.time()
elapsed_time = end_time - start_time
st.write('Time taken to predict is approximately:', round(elapsed_time, 2), 'seconds')
st.image(image, caption='Uploaded Image', width=300)
else:
st.write('Please upload an image')
if __name__ == '__main__':
main()
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