def predict():
'''
For rendering results on HTML GUI
'''
int_features = [x for x in request.form.values()]
result_knn,result_tfidf,result,result_collaborative = model_final.get_input(int_features)
result_word=[]
for i in result:
result_word.append(i[0])
print(result_collaborative)
return render_template ( 'index.html',result_knn=result_knn,result_tfidf=result_tfidf,result_word=result_word,result_collaborative=result_collaborative)
def postentry():
year = request.form["year"]
month = request.form["month"]
date = request.form["date"]
type = request.form["type"]
order = request.form["order"]
return render_template("result.html",
entries=model.get_input(year, month, date, type,
order),
year=year,
month=month,
date=date,
type=type,
order=order)
def test_input(self) -> None:
global my_dic, output
my_dic = {"age":int(self.l1.text()), "Medu":int(self.l2.text()),"Fedu":int(self.l3.text()), "traveltime":int(self.l4.text()), "failures": int(self.l5.text()),
"schoolsup": int(self.l6.text()),"famsup": int(self.l7.text()),"paid": int(self.l8.text()),"activities": int(self.l9.text()),"internet": int(self.l10.text()),
"freetime": int(self.l11.text()),"goout": int(self.l12.text()),"Dalc": int(self.l13.text()),"Walc": int(self.l14.text()),"health": int(self.l15.text()),"absences": int(self.l5.text()),
"G1": int(self.l17.text()),"G2": int(self.l18.text())}
output = model.get_input(my_dic)
self.report_subhead.setText("About")
self.model_details.setText("This model uses Naive Bayes Algorithm. We have used student dataset from UCI archive.")
if output == 0:
self.results.setText("The model predicts that the academic performance based on the given data attributes will be POOR")
elif output == 1:
self.results.setText("The model predicts that the academic performance based on the given data attributes will be FAIR")
else:
self.results.setText("The model predicts that the academic performance based on the given data attributes will be GOOD")
self.results.setFont(QFont("Arial",14, weight=QFont.Bold))
def predict():
"""Classifies JPEG image passed in as POST data
Assuming a JPEG file is passed in (as raw bytes), this function saves the
image to a the local temp directory, passes in the image to the TensorFlow
model, and returns the top-5 guesses and path to the saved image to be
rendered to the client.
NOTE: This function is NOT SAFE. Strictly for demonstration purposes. Does
not do any safe-checking of the data being saved locally. Only use locally.
"""
results = []
filename = None
if 'file' not in request.files:
flash('No file part')
return redirect(request.url)
file = request.files['file']
if file.filename == '':
flash('No selected file')
return redirect(request.url)
if file:
for f in os.listdir(app.config['UPLOAD_FOLDER']):
os.remove(os.path.join(app.config['UPLOAD_FOLDER'], f))
filename = os.path.join(app.config['UPLOAD_FOLDER'],
'{}.jpg'.format(random.randint(0, 999999999)))
file.save(filename)
file.seek(0)
data = file.read()
feed_dict = {model.get_input(sess): data}
prediction = sess.run(model.get_predictions(sess), feed_dict)
top_k = prediction.argsort()[0][-5:][::-1]
descriptions = get_descriptions()
for idx in top_k:
description = descriptions[idx]
score = prediction[0][idx]
print('{} (score = {})'.format(description, score))
results.append((description, score))
return render_template('predict.html', results=results, filename=filename)
def POST(self):
searchinput = model.get_input()
return render.search(searchinput)