def predict(): form = PredictionForm() if form.validate_on_submit(): iam_header = { 'Content-Type': 'application/json', 'Authorization': 'Bearer ' + access_token } objects = [form.month.data, form.dayofweek.data, form.borough.data, form.min_humidity.data, form.max_humidity.data, form.min_temp.data, form.max_temp.data, form.max_wind_speed.data, form.weather_description.data ] userInput = [] userInput.append(objects) payload_scoring = {"input_data": [{"fields": ["month", "dayofweek", "borough", "min_humidity", "max_humidity", "min_temp", "max_temp", "max_wind_speed", "weather_description"], "values": userInput }]} predict_value = requests.post("",json = payload_scoring , headers = iam_header) result = json.loads(predict_value.text) return result return render_template('index.html', form = form)
def predict(): form=PredictionForm() if form.validate_on_submit(): post=Post() if form.picture.data: picture_file = save_picture(form.picture.data) post.image_file = picture_file picture_path = os.path.join(app.root_path, 'static/uploaded_pics', picture_file) breed= predict_breed_transfer(picture_path) if dog_detector(picture_path): #flash('dog detected','success') #flash('Breed: '+breed,'success') post.title="Dog image detected" post.content="The breed is "+breed elif face_detector(picture_path): #flash('face detected','success') #flash('Breed: '+breed,'danger') post.title="Human image detected" post.content="The image resembles to "+breed else: flash('no face or dog detected','danger') post.title="Unknown image detected" post.content="It is neither a dog nor a human" db.session.add(post) db.session.commit() return redirect(url_for('home')) return render_template('prediction.html', title='Prediction', form=form)
def predict(): form = PredictionForm() if form.validate_on_submit(): flash(f'Predicted House Price for given data is {form.crim.data}!', 'success') return redirect(url_for('predict')) return render_template('predict.html', title='Predict', form=form)
def predict(): form = PredictionForm() if form.validate_on_submit(): sqft = request.form['sqft'] sqft_log = np.log(int(sqft)) baths_num = set_baths(request.form['baths_num']) beds_num = set_beds(request.form['beds_num']) story = set_story(request.form['story']) age = set_age(request.form['age']) schools_num = set_schools(request.form['schools_num']) schools_8_up = check_select(request.form['schools_8_up']) zipcode = int(request.form['zipcode']) zipcode_expensive = set_zipcode(zipcode) condo = check_select(request.form['condo']) mobile = check_select(request.form['mobile']) feature_names = [ 'sqft_log', 'zipcode_expensive', 'zipcode', 'age', 'baths_num', 'schools_8_up', 'story', 'Condo', 'beds_num', 'schools_num', 'mobile' ] work_features = [ sqft_log, zipcode_expensive, zipcode, age, baths_num, schools_8_up, story, condo, beds_num, schools_num, mobile ] x_test = pd.DataFrame([work_features], columns=feature_names) x_test = scaler.transform(x_test) price = predict_price(x_test) flash(f'Прогнозируемая цена {price} $', 'success') return render_template('predict.html', title='Predict', form=form)
def index_page(): """ """ global data, columns, dict_val, dataframe form = PredictionForm() if form.validate_on_submit(): # creating a dataframe with the input values for val in form: if val.id in columns: # if the value categorical if val.id in data: # obtaining the labeled id temp_val = data[val.id].index(val.data) idx = columns.index(val.id) dict_val[idx] = temp_val else: idx = columns.index(val.id) dict_val[idx] = val.data print(dict_val) arr = [val for val in dict_val.values()] arr = np.array([arr]) df = pd.DataFrame(arr, columns=columns) dataframe = df print(df) flash(f"prediction completed!", 'success') return redirect(url_for('prediction')) return render_template('index.html', form=form)
def predict(): form = PredictionForm() if form.validate_on_submit(): brand = request.form['mark'] bodyType = request.form['bodyType'] fuelType = request.form['fuelType'] productionDate = request.form['productionDate'] modelDate = request.form['modelDate'] numberOfDoors = request.form['numberOfDoors'] vehicleTransmission = request.form['vehicleTransmission'] engineDisplacement = request.form['engineDisplacement'] enginePower = define_power_category(request.form['enginePower']) mileage = define_mileage_category(request.form['mileage']) drive = request.form['drive'] owners = request.form['owners'] empty_features = [0] * 45 with open('features.list', 'r') as filehandle: features = json.load(filehandle) work_features = [ bodyType, brand, fuelType, modelDate, numberOfDoors, productionDate, vehicleTransmission, engineDisplacement, enginePower, mileage, drive, owners ] df = pd.DataFrame([work_features + empty_features], columns=features) price = predict_price(df) write_logs(work_features, features[:12], price) flash(f'Цена автомобиля {form.mark.data.upper()} {price} руб.', 'success') return render_template('predict.html', title='Predict', form=form)
def data(): form = PredictionForm() if form.validate_on_submit(): flower = Flower(sl=form.sl.data, sw=form.sw.data, pl=form.pl.data, pw=form.pw.data) db.session.add(flower) db.session.commit() return redirect(url_for('predict')) return render_template('data.html', form=form)
def data(): form = PredictionForm() if form.validate_on_submit(): #flower = Flower(sl=form.sl.data, sw=form.sw.data, pl=form.pl.data, pw=form.pw.data) promote = Promotion(dep=form.dep.data, reg=form.reg.data, edu=form.edu.data, gen=form.gen.data, rec=form.rec.data, trn=form.trn.data, age=form.age.data, rat=form.rat.data, srv=form.srv.data, kpi=form.kpi.data, awd=form.awd.data, scr=form.scr.data) db.session.add(promote) db.session.commit() return redirect(url_for('predict')) return render_template('data.html', form=form)