def main(): pred = predict.predictor() for name in NAMES: for tp in TYPES: # shutil.copyfile(name + '_' + tp + '.json', name + '_' + tp + '_BACKUP.json') with open(BASE_DIR + name + '_' + tp + '.json', 'r', newline='') as f: f_data = json.load(f) # create a list of the sentiments of each tweet (in order) sentiment_list = pred.infer( tweets=list(map(lambda x: x['text'], f_data['statuses']))) # normalizes the sentiment so that -1 is the most negative and 1 is the most positive sentiment_list = list( map(lambda x: (2.0 * x[1]) - 1, sentiment_list)) # add a sentiment field to each tweet and set it to be the normalized value of the sentiment for (status, sentiment) in zip(f_data['statuses'], sentiment_list): status['sentiment'] = sentiment with open(BASE_DIR + name + '_' + tp + '.json', 'w') as f: json.dump(f_data, f) print('Dumped %d lines of data to file' % len(f_data['statuses']))
def handle_message(event): text = u'นี้คือ {}'.format(event.message.type) output = None if event.message.type == "text": text = u'แมวววว' line_bot_api.reply_message(event.reply_token, TextSendMessage(text=text)) elif event.message.type == "image": message_content = line_bot_api.get_message_content(event.message.id) file_name = event.message.id + "_image.jpg" with open(file_name, 'wb') as fd: for chunk in message_content.iter_content(): fd.write(chunk) fd.close() output = predict.predictor(file_name) #text = u'สไตล์แวนโกะเลย เมี้ยววววว {}'.format(os.path.join(request.url_root, output)) url_img = os.path.join(request.url_root, output) if url_img[:5] != 'https': url_img = 'https' + url_img[4:] line_bot_api.reply_message( event.reply_token, ImageSendMessage(type="image", original_content_url=url_img, preview_image_url=url_img)) else: line_bot_api.reply_message(event.reply_token, TextSendMessage(text=text))
def main(): if not os.path.isdir(flag.output_dir): os.mkdir(flag.output_dir) if flag.mode == 'train': train_op = train.Trainer(flag) train_op.train() elif flag.mode == 'predict': predict_op = predict.predictor(flag) predict_op.inference() elif flag.mode == 'eval': eval_op = predict.predictor(flag) eval_op.evaluate() elif flag.mode == 'cam': cam_op = predict.predictor(flag) cam_op.cam() else: print 'not supported'
def home_repost(): if request.method == 'POST': final_symptoms = request.form.getlist('disease_checkbox') diseases = predictor(final_symptoms) if request.form.get('accept'): db.child('Accepted Diseases').child(input_text).update( {"Symptoms": final_symptoms}) db.child('Accepted Diseases').child(input_text).update( {"Disease": diseases}) return render_template('disease.html', text=input_text, final_symptoms=final_symptoms, diseases=diseases)
def predict(self): self.ignore_warning = False try: self.pred.should_terminate = True print("Successfully terminated predicting thread !") except Exception as err: print(err) # KILL EXISTING PROCESS try: subprocess.Popen.kill(self.graph_process) except Exception as err: print(err) date = (self.calendar.get_date()) date = datetime.strptime(date,"%Y-%m-%d") import threading as th import multiprocessing as mp daydelta = 0 fill_plot=bool(self.options["fill"].get()) # fill plot scatter_plot = bool(self.options["scatter"].get()) # use scatter plot print("plot options",fill_plot,scatter_plot) use_gpu = bool(self.options["use_gpu"].get()) # use gpu for tensorflow num_biomass = self.options["num_biomass"].get() num_biogas = self.options["num_biogas"].get() num_solar = self.options["num_solar"].get() biomass_pv = self.options["biomass_pv"].get() biogas_pv = self.options["biogas_pv"].get() period = 15 self.pred = predictor(dt=date,model_path=self.model,use_gpu=use_gpu,message_callback=self.power_error_callback) # creat new instance of predictor self.pred.iteration_delay = self.interval_scaler.get()/1000 # self.predictor loop delay self.pred.BIOMASS_PV = biomass_pv self.pred.BIOGAS_PV = biogas_pv self.pred.num_biomass = num_biomass self.pred.num_biogas = num_biogas self.pred.num_solar = num_solar pred_thread = th.Thread(target=self.pred.run) pred_thread.start() self.graph_process = subprocess.Popen([ "python", "plot_load.py", "--date",str(date.date()), "--scatter-plot", str(scatter_plot), "--fill-plot", str(fill_plot) ])
def home_post(): if request.method == 'POST': text = request.form.get('symptoms_input') words = word_extractor(text) final_symptoms = symptoms(words) diseases = predictor(final_symptoms) global input_text input_text = text if request.form.get('accept'): db.child('Accepted Diseases').child(input_text).update( {"Symptoms": final_symptoms}) db.child('Accepted Diseases').child(input_text).update( {"Disease": diseases}) return render_template('disease.html', text=text, final_symptoms=final_symptoms, diseases=diseases)
def handle_message(event): if event.message.type == "text": line_bot_api.reply_message(event.reply_token, TextSendMessage(text='แมววว')) elif event.message.type == "image": message_content = line_bot_api.get_message_content(event.message.id) filepath = event.message.id + "image.jpg" with open(filepath, 'wb') as fd: for chunk in message_content.iter_content(): fd.write(chunk) fd.close() line_bot_api.reply_message( event.reply_token, TextSendMessage(text=predict.predictor(filepath))) else: line_bot_api.reply_message( event.reply_token, TextSendMessage(text="นี้คือ " + event.message.type))
import predict as pd my_data = [['slashdot', 'USA', 'yes', 18, 'None'], ['google', 'France', 'yes', 23, 'Premium'], ['digg', 'USA', 'yes', 24, 'Basic'], ['kiwitobes', 'France', 'yes', 23, 'Basic'], ['google', 'UK', 'no', 21, 'Premium'], ['(direct)', 'New Zealand', 'no', 12, 'None'], ['(direct)', 'UK', 'no', 21, 'Basic'], ['google', 'USA', 'no', 24, 'Premium'], ['slashdot', 'France', 'yes', 19, 'None'], ['digg', 'USA', 'no', 18, 'None'], ['google', 'UK', 'no', 18, 'None'], ['kiwitobes', 'UK', 'no', 19, 'None'], ['digg', 'New Zealand', 'yes', 12, 'Basic'], ['slashdot', 'UK', 'no', 21, 'None'], ['google', 'UK', 'yes', 18, 'Basic'], ['kiwitobes', 'France', 'yes', 19, 'Basic']] header = ['Company', 'Country', 'FAQ', 'No. of Visits'] name = raw_input("Company Name:") country = raw_input("Country :") faq = raw_input("Visited FAQ (yes/no)") visits = int(raw_input("No. of Visits")) tple = [name, country, faq, visits] pd.predictor(my_data, tple, header, "Subscription Prediction")
import predict as pd data = [['yes', 'single', 125, 'No'], ['no', 'married', 100, 'No'], ['no', 'single', 70, 'No'], ['yes', 'married', 120, 'No'], ['no', 'divorced', 95, 'Yes'], ['no', 'married', 60, 'No'], ['yes', 'divorced', 220, 'No'], ['no', 'single', 85, 'Yes'], ['no', 'married', 75, 'No'], ['no', 'single', 90, 'Yes']] header = ["Home Owner", "Marital Status", "Annual Income"] own = raw_input("Home Owner?(yes/no)") mar = raw_input("Marital status(single/married/divorced)") inc = int(raw_input("Annual Income")) pd.predictor(data, [own, mar, inc], header, "Defaulted Borrower")
import predict import cv2 weights_path = "../../inceptionV3.299x299.h5" p = predict.predictor(weights_path) #print('\n Enter the path to the image\n') #image_path = input().strip() #print(p.predict_from_path(image_path)) img = cv2.imread("../../pythonFL/data/test/drawings/0A2FD005-76FF-4C5A-8C81-8179010ED1BB.jpg") img = cv2.resize(img, (299, 299)) print(p.predict_from_array(img))