def predict_number(self):
all_pts = []
for w in self.canvas.children:
if isinstance(w, Line):
wpts = np.array(w.points)
xs = wpts[::2]
ys = wpts[1::2]
try:
tck, u = interpolate.splprep([xs, ys], s=0)
except Exception as e:
print('warn', e)
continue
u_new = np.arange(np.min(u), np.max(u), 0.01)
out = interpolate.splev(u_new, tck)
all_pts += out
img = np.zeros((560+10, 560+10)).astype(np.uint8)
polyx = np.array(all_pts[::2]).astype(np.int)
polyy = np.array(all_pts[1::2]).astype(np.int)
R = 10
from itertools import product
for xs, ys in zip(polyx, polyy):
for x, y in zip(xs, ys):
circle = [(x+i, y+j) for (i, j) in product(range(-R,
R+1), repeat=2) if i**2+j**2 < R**2]
for c in circle:
img[c[0], c[1]] = 128
img = imresize(np.rot90(img), (28, 28),
interp='bilinear').astype('f')/np.max(img)
plt.imshow(img, cmap='gray')
plt.show()
predict(img)
def predict_number(self):
all_pts = []
for w in self.canvas.children:
if isinstance(w, Line):
wpts = np.array(w.points)
xs = wpts[::2]
ys = wpts[1::2]
try:
tck, u = interpolate.splprep([xs, ys], s=0)
except Exception as e:
print('warn', e)
continue
u_new = np.arange(np.min(u), np.max(u), 0.01)
out = interpolate.splev(u_new, tck)
all_pts += out
img = np.zeros((560 + 10, 560 + 10)).astype(np.uint8)
polyx = np.array(all_pts[::2]).astype(np.int)
polyy = np.array(all_pts[1::2]).astype(np.int)
R = 10
from itertools import product
for xs, ys in zip(polyx, polyy):
for x, y in zip(xs, ys):
circle = [(x + i, y + j)
for (i, j) in product(range(-R, R + 1), repeat=2)
if i**2 + j**2 < R**2]
for c in circle:
img[c[0], c[1]] = 128
img = imresize(np.rot90(img),
(28, 28), interp='bilinear').astype('f') / np.max(img)
plt.imshow(img, cmap='gray')
plt.show()
predict(img)
def plotting(itemlist):
x = [row[0] for row in itemlist]
y = [row[1] for row in itemlist]
plt.axis((0, 800, 600, 0))
plt.plot(x, y)
plt.savefig('foo1.jpeg')
try:
testcrop.CropImage()
predictor.predict()
except IndexError:
print("Draw Again!!!")
pass
plt.close()
def plotting(itemlist, model, session, saver, save_path, hyperparams):
x = [row[0] for row in itemlist]
y = [row[1] for row in itemlist]
plt.axis((0, 800, 600, 0))
plt.plot(x, y)
plt.savefig('foo1.jpeg')
try:
testcrop.CropImage()
predictor.predict(model, session, saver, save_path, hyperparams)
except IndexError:
print("Draw Again!!!")
pass
plt.close()
def home():
if request.method == 'POST':
req = request.form['input']
res = predictor.predict(req)
return render_template('submit.html', res=res)
else:
return render_template('submit.html')
async def handle_docs_photo(message):
chat_id = message.chat.id
if message.media_group_id is None:
# Get user's variables
user_name = message.from_user.first_name
user_id = message.from_user.id
message_id = message.message_id
text = WAITING_TEXT % user_name
logging.info(f'{user_name, user_id} is knocking to our bot')
await bot.send_message(chat_id, text)
# Define input photo local path
photo_name = './input/photo_%s_%s.jpg' % (user_id, message_id)
await message.photo[-1].download(
photo_name) # extract photo for further procceses
#Photo processing
photo_output, text = predict(photo_name)
await bot.send_photo(chat_id, photo_output)
output_text = []
for i in text:
output_text.append(CLASSES_DICT[i])
output_text = '\n\n'.join(output_text)
await bot.send_message(chat_id, output_text)
else:
text = NOT_TARGET_TEXT % user_name
await message.reply(text)
def main():
ap = argparse.ArgumentParser(
description='This script allow to predict using a pre-trained model')
ap.add_argument(
'image_path',
default='/home/workspace/paind-project/flowers/test/1/image_06752.jpg')
ap.add_argument('checkpoint',
default='/home/workspace/paind-project/checkpoint.pth')
ap.add_argument('--top_k', dest='top_k', type=int, default=5)
ap.add_argument('--category_names',
dest='category_names',
default='cat_to_name.json')
ap.add_argument('--gpu', dest='gpu', action='store_true')
args = ap.parse_args()
if args.image_path and args.checkpoint:
model = checkpoint.load(checkpoint_path=args.checkpoint, gpu=args.gpu)
probs, classes = predictor.predict(image_path=args.image_path,
model=model,
top_k=args.top_k,
gpu=args.gpu)
with open(args.category_names, 'r') as json_file:
cat_to_name = json.load(json_file)
labels = list(cat_to_name.values())
classes = [labels[x] for x in classes]
for c, p in zip(classes, probs):
print(c, p)
def index():
if "file_urls" not in session:
session['file_urls'] = []
if "result1" not in session:
session['result1'] = []
if "positive" not in session:
session['positive'] = []
full_result = session['result1']
full_file = session['file_urls']
full_positive = session['positive']
if request.method == 'POST':
file_obj = request.files
for f in file_obj:
file = request.files.get(f)
filename = photos.save(file, name=file.filename)
print(filename)
result1, file_urls, positive = predict([filename])
full_result = full_result + result1
full_file = full_file + file_urls
full_positive = full_positive + positive
session['file_urls'] = full_file
session['result1'] = full_result
session['positive'] = full_positive
print('COMPLETE')
return "Uploading..."
return render_template('index.html')
def scorer():
id_number = None
form = NameForm()
my_dict = None
pred, diff, no_assumptions, assumptions, title = (None,
None, None, None, None)
if form.validate_on_submit():
id_number = form.id_number.data
form.id_number.data = ''
pred, diff, no_assumptions, assumptions,title = predict(
id_number, dict90,'../pipeline/data/')
my_dict = assumptions
attrs = ['bed','bath','feet','dog','cat','content',
'getphotos','hasmap','housingtype','lat','long','laundry',
'parking','price','smoking','wheelchair']
return render_template('scorer.html', id_number=id_number, attrs=attrs,
my_dict=my_dict,form=form,pred=pred, diff=diff,
no_assumptions=no_assumptions,assumptions = assumptions,title=title)
def get_images():
unet = skimage.io.imread(
'/Users/arianrahbar/Dropbox/Unet/OutProbs/1_crop.png')
mrcnn = cv2.imread('/Users/arianrahbar/Dropbox/Mrcnn/OutLabels/1_crop.png',
cv2.IMREAD_GRAYSCALE)
unet = pred.predict(unet)
return unet, mrcnn
def send():
if request.method == 'POST':
url = request.form['url']
predict = predictor.predict(url)
return render_template('index.html', predict=predict)
return render_template('form.html')
def main(args):
normal_class = args.normal_class
anomal_classes = args.anomal_classes
train_dataset, test_dataset = get_cifar_datasets(normal_class,
anomal_classes)
print(f"train dataset length: {len(train_dataset)}")
print(f"test dataset length: {len(test_dataset)}")
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print('Using {} device'.format(device))
feature_extractor_version = args.feature_extractor_version # resnet18, 'resnet34', 'resnet50', resnet101, resnet 152
feature_extractor = torch.hub.load('pytorch/vision:v0.9.0',
feature_extractor_version,
pretrained=True)
feature_extractor.to(device)
train_set, test_set = get_feature_space(device, feature_extractor,
train_dataset, test_dataset)
accuracy, tpr, fpr, recall, precision = predict(train_set, test_set)
print(f"feature extractor: {feature_extractor_version}")
print(f"normal class: {normal_class}")
print(f"anomal classes: {anomal_classes}")
print(f"accuracy: {accuracy}")
print(f"tpr: {tpr}, fpr: {fpr}")
print(f"recall: {recall}, precision: {precision}")
def output():
try:
json = request.get_json()
input_text = json["text"]
return predict(input_text)
except Exception as e:
return f"An error Occured: {e}"
def parse_files():
bucket = gcs_client.get_bucket(BUCKET_NAME)
blobs = bucket.list_blobs()
Logger.log_writer("Reading bucket files....")
for blob in blobs:
Logger.log_writer("fileName:" + blob.name)
if blob.name == "LTC.csv":
Logger.log_writer("Downloading...")
blob.download_to_filename(blob.name)
Logger.log_writer("Downloaded! Predicting...")
last_value, prediction, market_cap = predictor.predict(blob.name)
Logger.log_writer(
"Created a prediction for:{0} last_value:{1} prediction:{2} marketcap:{3}"
.format(blob.name, last_value, prediction, market_cap))
save_prediction(blob.name, last_value, prediction, market_cap)
def onClick():
global message, file, image, img, graph
#os.system("script2.py "+ rain.get() +" " + temp.get()+ " " + Population_density.get())
# filo = open("private 2.txt", "r")
value = predictor.predict(city.get(), tempe.get(), rain.get(),
pop_den.get())
# print(value)
########### MAKING GRAPH ###############################
file = "C:\\Users\\acer\\Desktop\\programme_codes\\disease-outbreaks-predictor\\output.png"
image = Image.open(file)
image = image.resize((1450, 450), Image.ANTIALIAS)
img = ImageTk.PhotoImage(image)
graph = Label(lowerFrame, image=img)
graph.grid(row=0, column=0, columnspan=14, pady=2, padx=40)
out1 = str(int(value[0]))
message = "The predicted disease count of next year is " + out1
answer["text"] = message
temp_contri["text"] = "Current Temperature contribution is: " + str(
value[1][0] / (sum(value[1])) * 100)[:5]
rain_contri["text"] = "Current Rainfall contribution is: " + str(
value[1][1] / (sum(value[1])) * 100)[:5]
pop_contri["text"] = "Current Population Density contribution is: " + str(
value[1][2] / (sum(value[1])) * 100)[:5]
def detect():
redditURL = request.form['redditpost']
print(redditURL)
predicted_flair = str(predict(str(redditURL)))
actualflair = str(FlairActual(str(redditURL)))
print(predicted_flair, actualflair)
return render_template('index.html', predicted_flair = predicted_flair, actualflair = actualflair)
def snapshot(self):
ret, frame = self.vid.get_frame()
if ret:
cv2.imwrite("capture.jpg", cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
result = predict()
print(result)
self.selectMusic(result)
def get_tweets_forAgency(agency):
for tweet in tweets:
text = str(tweet[2])
if "b'RT" not in text:
sentiment = TextBlob(text).sentiment.polarity
if sentiment <= 0:
if agency == predict(text):
print(text)
def predict():
sex = request.args["sex"]
age = request.args["age"]
beh_id = int(request.args["beh_id"])
zip_code = int(request.args["zip_code"])
ans = predictor.predict(sex, age, beh_id, zip_code)
return json.dumps(ans)
def main():
graph = Graph()
graph.saveGraph()
root = Tk()
probabilityIncrease, probabilityDecrease = predictor.predict()
gui = MyGUI(probabilityIncrease, probabilityDecrease, root)
gui.appOpen()
root.mainloop()
def save():
filename = "image.png"
# global image1
# image1 = PIL.ImageOps.invert(image1)
image1.save(filename)
predicted = predict()
print(predicted)
root.title(predicted)
def compare_scans(request):
last_two_scans = ImageModel.objects.all().order_by('-id')[:2]
print(last_two_scans)
file1 = last_two_scans[0].get_name()
file2 = last_two_scans[1].get_name()
path1 = "/home/prajwala/Videos/oct_analyzer"+settings.MEDIA_URL+file1
path2 = "/home/prajwala/Videos/oct_analyzer"+settings.MEDIA_URL+file2
results_path = "/home/prajwala/Videos/oct_analyzer"+settings.MEDIA_URL+"results/"
first,name1 = file1.split("/")
first,name2 = file2.split("/")
result1 = predict(path1,results_path,name1)
result2 = predict(path2,results_path,name2)
context = {'file1':file1,'file2':file2}
results = {'result1':result1,'result2':result2}
plot_hist(result1,result2)
return render(request,'uploader/compare.html',{'context':context,'results':results})
def home():
if request.method == 'POST':
req = request.json
print(req)
content = req['text']
res = predictor.predict(content)
return jsonify({'generatedText': res})
else:
return jsonify({'generatedText': 'hi'})
def get_prediction(calendar, schedule): schedule = get_schedule(schedule) try: weekday = [ep for ep in schedule['event_plans'] if ep['event_plan'] == 'Weekday'][0] min_temp = weekday['min_temp_business_hours'] temp = min_temp if min_temp !="" else "20" except: temp = "20" return predictor.predict(temp)
def predict():
review_text = request.form['review']
cleaned = predictor.clean_str(review_text)
rating = predictor.predict(review_text)
return render_template('prediction.html',
review=review_text,
cleaned=cleaned,
rating=rating)
def receiver():
if request.method == 'POST':
if 'imagefile' in request.files:
img = request.files.get('imagefile', '')
response = model.predict(img)
return {'response': response}
elif request.method == 'GET':
return redirect('/')
def handle_text(text):
# predict
prediction = predict(model, text)
# save to firebase
saveToFirebase(text, prediction)
# return
return prediction
def sentiment_analyzer():
'''
Receives company name and send back json response ['Positive', 'Negative']
'''
company = request.form['company']
res = predict(company).resp()
return jsonify(res)
def partition(remaining, swift, container_name='videos', file_list=None):
""" Naive partitioning algorithm for figuring out which workloads can go
on each VM. This is done using the predict machine learning algorithm on
each of the files using the index file written earlier during the ingest
portion of the program.
Note: I (the person writing the docstrings) did not create this
algorithm, so am unsure exactly of what's going on here. The lines and
comments were left mostly as I found them with some minor formatting
changes. Contact Ruben Madera (https://github.com/Roastmaster) for more
info.
"""
if not file_list:
container_data = []
for data in swift.get_container(container_name)[1]:
container_data.append('{0}\t{1}'.format(data['name'], data['bytes']))
container_data = [token.split('\t') for token in container_data]
# Use a list comprehension to create a list of all the file names
file_list = []
try:
file_list = [token[0] for token in container_data]
except IndexError:
print "IndexError: Container empty"
# Where we store the partitioned list of videos.
# Internal lists separate what is possible to transcode in time on one VM
partitioned_video_list = []
# Given a time-until-completion by Joe's look up table, we keep
# decrementing "time_until_deadline" by these times until it reaches
# zero, then, create a new list (representing a new vm), and repeat.
tmp_t_u_d = remaining
print "Time Remaining:", predictor.prettify_time(remaining)
single_vm_capacity = []
for video in file_list:
single_vm_capacity.append(video)
prediction_time = predictor.predict(video)
if prediction_time > remaining:
print "WARNING: File is too big to be transcoded by VM in time."
partitioned_video_list.append(single_vm_capacity)
single_vm_capacity = []
tmp_t_u_d -= prediction_time
continue
if tmp_t_u_d - prediction_time > 0:
tmp_t_u_d -= prediction_time
if video == file_list[-1]:
partitioned_video_list.append(single_vm_capacity)
else:
tmp_t_u_d = remaining
partitioned_video_list.append(single_vm_capacity)
single_vm_capacity = []
return partitioned_video_list
def recognize(img_path, rect_list):
os.chdir(attention_root)
boxes = []
for rect in rect_list:
box = (rect[0], rect[1], rect[0] + rect[2], rect[1] + rect[3])
boxes.append(box)
words = predictor.predict(img_path, boxes)
os.chdir(cwd)
return words
def process_images(input, output):
if os.path.isdir(output):
shutil.rmtree(output)
os.makedirs(output, exist_ok=True)
for segmentation in os.listdir(input):
os.makedirs(os.path.join(output, segmentation), exist_ok=True)
for filename in os.listdir(os.path.join(input, segmentation)):
img = imread(os.path.join(input, segmentation, filename))
prediction = predict(img)
imwrite(os.path.join(output, segmentation, filename), prediction)
def resultCancer():
if request.method == 'POST':
gene = request.form["Gene"]
variation = request.form['Variation']
text = request.form['Text']
wCancer.writerow([gene, variation, text])
re = predictor.predict()
return render_template("resultCancer.html", result=re)
def main():
s = time.time()
train = pd.read_csv("train.csv")
test = pd.read_csv("test.csv")
clean(train)
clean(test)
print("Data processed after " + str(time.time() - s) + " sec")
#-----------Predictor-------------------------------
tfidf_bag = bag(train.tweet)
predictor.predict(tfidf_bag, train.label)
#---------------------------------------------------
#-----------Clustering------------------------------
hateful = train.copy(deep=True)
get_hateful(hateful)
kmeans_model, vectorizer = classifier.train(hateful, False)
print("Clusters Found after " + str(time.time() - s) + " sec")
def predict():
#Symptoms are set as a CSV list:
symptoms_csv = request.form['symptoms']
symptoms = symptoms_csv.split(',')
symptoms = symptoms[:-1] #Remove empty entry at end
#Age:
age = int(request.form['age'])
#Make prediction:
diagnosed_disease = predictor.predict(symptoms, age)
#Connect to database:
dbConn = MySQLdb.connect('localhost', 'project', 'project', 'cse3002')
dbCursor = dbConn.cursor()
#Get recommended drugs for disease:
dbCursor.execute('select drugs_list from drugs where disease_name=%s',
(diagnosed_disease, ))
drugs_string = dbCursor.fetchone()[0]
#Parse list:
drugs_string = drugs_string.split(':')
final_dstring = ""
for i in range(len(drugs_string) - 1):
d = drugs_string[i]
drug = d.split(',')
final_dstring = final_dstring + drug[0]
if drug[1]:
final_dstring = final_dstring + ' (Prescription Required)'
final_dstring = final_dstring + ', '
if final_dstring == '':
final_dstring = 'None'
#Get session ID:
session_id = request.cookies.get('session_id')
#Check if user is anonymous:
if session_id != 'ANONYMOUS':
#Get current date and time:
diag_datetime = str(datetime.datetime.now()).split('.')[0]
#Get username:
dbCursor.execute('select username from login where session_id=%s',
(session_id, ))
username = dbCursor.fetchone()[0]
#Commit record to database:
dbCursor.execute(
'insert into diagnoses values(%s, %s, %s, %s)',
(username, diag_datetime, diagnosed_disease, symptoms_csv[:-1]))
dbConn.commit()
#Close DB connection:
dbConn.close()
#Return results:
return render_template("diagnosis.html",
disease_name=diagnosed_disease,
symptoms=symptoms,
age=age,
drugs=final_dstring)
def processor(r_queue, s_queue):
while True:
data = r_queue.get()
ip,entry = data.split('\t')
dic = transform(entry)
#process data
cls = predictor.predict(dic)
result = map_pt[cls]
data = ip+'\t'+result
s_queue.put(data)
print 'from\t'+ip+'\t\tpredict\t'+result
def predict():
rawImage = request.files['file']
X = predictor.readAndNormalizeImg(rawImage)
preds = predictor.predict(X)
trueLabel = util.LABEL_DICT[preds[0]]
data = {"prediction": trueLabel}
response = jsonify(data)
return response
def classify():
t = timer()
""" choose here your c, gamma range """
for c in frange(0.1, 1.5, 0.1):
#c = 32;
for gamma in frange(0.1, 1.5, 0.1):
print "### combination ###"
print c, gamma
print "### build model ###"
sys.stdout.flush()
t.next()
model_creator.build_model("tmps.arff", c, gamma)
sys.stdout.flush()
print t.next()
print "### predict ###"
t.next()
predictor.predict("tmps_independent.arff")
print t.next()
sys.stdout.flush()
def main(argv):
date = "20180302"
file = open("../../server/data/" + date, "r")
lines = file.readlines()
file.close()
for i in range(len(lines)):
line = lines[i]
tokens = line.split(",")
time = tokens[0]
result = predictor.predict(date, time)
process_result(tokens, time, result)
def estimate():
if request.method == 'POST':
try:
data = ast.literal_eval(request.data.decode("utf-8"));
data = data["input"];
vec,ans = predictor.predict(data);
print(vec);
rv = json.dumps({"vec":vec,"ans":str(ans)});
return(rv);
except Exception as ex:
print(str(ex));
return(str(ex));
except Error as er:
print(str(er));
return(str(er));
def partition_workload(time_until_deadline, swiftclient, container_name, file_list = None):
if not file_list:
container_data = []
for data in swiftclient.get_container(container_name)[1]:
container_data.append('{0}\t{1}'.format(data['name'], data['bytes']))
container_data = [token.split('\t') for token in container_data]
# use a list comprehension to create a list of all the filenames
file_list = []
try:
file_list = [token[0] for token in container_data]
except IndexError:
print "IndexError: Container empty"
# where we store the partitioned list of videos.
# Internal lists seperate what is possible to transcode in time on one VM
partitioned_video_list = []
# given a time-until-completetion by joe's look up table, we keep decrementing "time_until_deadline" by
# these times until it reaches zero, then, create a new list (representing a new vm), and repeat.
tmp_t_u_d = time_until_deadline
print "Time Remaining:", predictor.prettify_time(time_until_deadline)
single_vm_capacity = []
for video in file_list:
single_vm_capacity.append(video)
prediction_time = predictor.predict(video)
if (prediction_time > time_until_deadline):
print "WARNING: File is too big to be transcoded by VM in time."
partitioned_video_list.append(single_vm_capacity)
single_vm_capacity = []
tmp_t_u_d -= prediction_time
continue
if (tmp_t_u_d - prediction_time > 0):
tmp_t_u_d -= prediction_time
if (video == file_list[-1]):
partitioned_video_list.append(single_vm_capacity)
else:
tmp_t_u_d = time_until_deadline
partitioned_video_list.append(single_vm_capacity)
single_vm_capacity = []
return partitioned_video_list
def update(): predictor.predict()
def predict():
source = request.params.source
sentences = predictor.predict(source)
response.set_header('Access-Control-Allow-Origin', '*')
return {"sentences": sentences}
def question_tags():
question = request.json['question']
return predict(question)
def main():
poke_name = flask.request.args.get('name')
res = predictor.predict(poke_name)
return json.dumps(res, ensure_ascii=False, indent=2)