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app.py
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app.py
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# Importing required libraries, obviously
import streamlit as st
import cv2
from PIL import Image
import numpy as np
import os
from tensorflow.keras.models import model_from_json
model=model_from_json(open('C:/Users/91934/OneDrive/Desktop/btd3/tumor.json','r').read())
model.load_weights('C:/Users/91934/OneDrive/Desktop/btd3/Tumor.h5')
def detect(image):
opencvImage = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
grey=cv2.cvtColor(opencvImage,cv2.COLOR_BGR2GRAY)
grey=cv2.resize(grey,(100,100))
return grey,opencvImage
def about():
st.write(
'''
**Tensorflow and Opencv** are libraries
used for image processing
Read more :point_right: https://docs.opencv.org/2.4/modules/objdetect/doc/cascade_classification.html
https://sites.google.com/site/5kk73gpu2012/assignment/viola-jones-face-detection#TOC-Image-Pyramid
''')
m={0:'Non-Tumored',1:'Tumored'}
def main(model):
st.title("Brain Tumor Detector ")
activities = ["Home", "About"]
choice = st.sidebar.selectbox("Pick something fun", activities)
if choice == "Home":
# You can specify more file types below if you want
image_file = st.file_uploader("Upload image", type=['jpeg', 'png', 'jpg', 'webp'])
if image_file is not None:
image = Image.open(image_file)
if st.button("Process"):
result,opencvImage = detect(image=image)
predictions=model.predict(result.reshape(1,100,100,1))
st.image(opencvImage,caption=m[np.argmax(predictions[0])]+' Image')
st.success("{}\n".format(m[np.argmax(predictions[0])]))
elif choice == "About":
about()
if __name__ == "__main__":
main(model)