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server.py
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server.py
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import os
from flask import Flask, request, redirect, url_for, render_template
import werkzeug
from werkzeug.utils import secure_filename
import cv2
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
from testing import Testing
test=Testing()
UPLOAD_FOLDER = 'uploads'
ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}
app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
filename_global=""
def allowed_file(filename):
return '.' in filename and \
filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
@app.route('/', methods=['GET', 'POST'])
def main_page():
print(request)
if request.method == 'POST':
if 'file' not in request.files:
return render_template('index.html',error=True)
file = request.files['file']
if (file and file.filename != '' and allowed_file(file.filename)):
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
return redirect(url_for('prediction', filename=filename))
else:
return render_template('index.html',error=True)
return render_template('index.html')
@app.route('/prediction/<filename>')
def prediction(filename):
global filename_global
filename_global=filename
# Step 1
my_image =cv2.imread(os.path.join("uploads",filename))
# Step 2
my_image_re = cv2.resize(my_image, (299,299))
my_image_re=(my_image_re.astype('float16'))/255.0
# Prediction
prediction=test.predict(my_image_re)
print(prediction)
# Order the lists by decreasing probability
zipped_lists = list(zip(prediction[0], prediction[1][0]))
sorted_pairs = sorted(zipped_lists, key = lambda x: x[1])
tuples = zip(*sorted_pairs)
list1, list2 = [ list(tuple) for tuple in tuples]
print(list1)
print(list2)
predictions = {
"class1":list1[2],
"class2":list1[1],
"class3":list1[0],
"prob1":list2[2],
"prob2":list2[1],
"prob3":list2[0],
}
return render_template('prediction.html', predictions=predictions)
@app.route("/partialFit/", methods=['GET','POST'])
def move_forward():
print(filename_global)
my_image =cv2.imread(os.path.join("uploads",filename_global))
#Step 2
my_image_re = cv2.resize(my_image, (299,299))
my_image_re=(my_image_re.astype('float16'))/255.0
y=request.args.get("param")
test.partial_fit(my_image_re,y)
return render_template('ty.html')
app.run(debug=True)