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app.py
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app.py
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import os
import numpy as np
import json
# Keras
import keras
from keras.models import load_model
from keras.preprocessing import image
# Flask utils
from flask import Flask, request, render_template
from werkzeug.utils import secure_filename
import tensorflow_hub as hub
# Define a flask app
app = Flask(__name__)
# Model saved with Keras model.save()
model = load_model(('braintumor.h5'), custom_objects={'KerasLayer': hub.KerasLayer})
def model_predict(img_path, model):
test_image = image.load_img(img_path, target_size = (150,150))
test_image = image.img_to_array(test_image)
test_image = np.array(test_image)
test_image = np.expand_dims(test_image, axis=0)
result = model.predict(test_image)
labels = ['Glioma','Meningioma','No Tumor','Pituitary']
return labels[result.argmax()] , result
@app.route('/', methods=['GET'])
def index():
# Main page
return render_template('index.html')
@app.route('/predict', methods=['GET', 'POST'])
def upload():
if request.method == 'POST':
# Get the file from post request
f = request.files['file']
# Save the file to ./uploads
basepath = os.path.dirname(__file__)
file_path = os.path.join(
basepath, 'uploads', secure_filename(f.filename))
f.save(file_path)
# Make prediction
preds , res = model_predict(file_path, model)
data = {
"pred" : preds,
"glioma" : str(round(res[0][0]*100,4)),
"meningioma" :str(round(res[0][1]*100,4)),
"notumor": str(round(res[0][2]*100,4)),
"pituitary":str(round(res[0][3]*100,4)),
"glink": "https://www.lybrate.com/topic/glioma",
"mlink" : "https://www.lybrate.com/topic/meningioma",
"nlink" : "https://www.aans.org/en/Patients/Neurosurgical-Conditions-and-Treatments/Brain-Tumors",
"plink" : "https://medsurgeindia.com/cost/pituitary-tumor-treatment-cost-in-india/#:~:text=A%20pituitary%20tumor%20is%20a,that%20regulate%20vital%20bodily%20processes."
}
y = json.dumps(data)
return y
return None
if __name__ == '__main__':
app.run(debug=False)