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)