File size: 3,540 Bytes
720abed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 | import streamlit as st
import json
import requests
from PIL import Image
import os
#option 2
import numpy as np
from tensorflow.keras.models import load_model
import cv2
def load_image(image):
img = Image.open(image)
return img
def save_uploadedfile(uploadedfile):
with open(os.path.join("images/img",uploadedfile.name),"wb") as f:
f.write(uploadedfile.getbuffer())
uploaded_location = os.path.join("images/img",uploadedfile.name)
return uploaded_location#st.success("Saved File:{} to {}".format(uploadedfile.name, uploaded_location))
def image_predict (image_file):
model_path = 'application/models/Tumor_VGG_model.h5'
h5_model = load_model(model_path)
image = cv2.imread(image_file)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (224, 224))
image = np.array(image) / 255
image = np.expand_dims(image, axis=0)
h5_prediction = h5_model.predict(image)
print('Prediction from h5 model: {}'.format(h5_prediction))
print(h5_prediction)
probability = h5_prediction[0]
print("H5 Predictions:")
print (probability)
if probability[0] > 0.8:
brain_tumor_pred = str('%.2f' % (probability[0] * 100) + '% Brain Tumour-Present')
probability = (probability[0] * 100)
else:
brain_tumor_pred = str('%.2f' % ((1 - probability[0]) * 100) + '% No Brain Tumour')
probability = ((1 - probability[0]) * 100)
return brain_tumor_pred
st.title("🦠 Brain Tumor Prediction App from CT Images")
col1, col2 = st.columns([6, 3], gap="medium")
with col1:
image = st.file_uploader("Upload CT Scan", type=["png","jpg","jpeg"])
if image is not None:
# To See details
file_details = {"filename":image.name, "filetype":image.type,
"filesize":image.size}
st.write(file_details)
#View Uploaded Image
st.image(load_image(image),width=250)
#save image to disk
saved = save_uploadedfile(image)
#OPTION 1 - with F-API..
#if st.button ('Analyze'):
#test_file = open(os.path.join("images/img", image.name), "rb")
#response = requests.post('http://127.0.0.1:8000/predict/image', files={'file': test_file })
#prediction = response.json()##json_object["prediction"]
#st.write(prediction)
#st. subheader (f"Response from BrainTumor Analyzer API = {prediction}")
#OPTION 2 - NON API..
if st.button ('Analyze'):
with st.spinner('Analyzing...'):
prediction = image_predict(saved)
#st.write(prediction)
st. subheader (f"Image Prediction = {prediction}")
st.success(f"Image Prediction = {prediction}", icon="✅")
with col2:
#with st.sidebar:
st.write("Developed by AI & IOT Lab https://iot.neu.edu.tr by Olusegun Odewole (oooladeleodewole(at)gmail) ")
#st.header("Sample CT Image")
st.image( "https://res.cloudinary.com/segestic/image/upload/v1670497190/covid/images/Y99_ntvrog.jpg", width=300, caption='Sample CT-Scan Image')#width=400
#streamlit run app.py
#RUN BOTH for F-API...
#uvicorn application.server.main:app
#if __name__ == "__main__":
#import uvicorn
#uvicorn.run("application.server.main:app", host="0.0.0.0", port=8000, reload=False, log_level="debug", workers=1, limit_concurrency=1, limit_max_requests=1)
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