def upload_pic(request):
if request.method == 'POST':
form = ImageUploadForm(request.POST, request.FILES)
imageName = str(request.FILES['image'])
print "Hello this is :"+imageName
# print request.POST
if form.is_valid():
m = ExampleModel()
m.model_pic = form.cleaned_data['image']
m.save()
im = imClassify.imClassify()
imagePath = 'utilities/images/'+imageName
resultList = im.main(imagePath)
print "THi is view ;;;;;; ------ ;;;;"
print resultList
# Considering only the first string
# [['giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca', 0.89233041], ['indri, indris, Indri indri, Indri brevicaudatus', 0.0085870409], ['lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens', 0.0026423461], ['custard apple', 0.0014067066], ['earthstar', 0.0010706335]]
nameOfTopic = resultList[0][0].split(",")[0]
print nameOfTopic
du = duckduckGo.duckduckGo()
description = du.getInfo(nameOfTopic)
print description
# Now deleting the file
if os.path.isfile(imagePath):
os.remove(imagePath)
return render(request,'result.html',{'imageName':nameOfTopic, 'description':description})
return HttpResponseForbidden('allowed only via POST')
def new_example():
"""Add a new example"""
form = ExampleForm()
if form.validate_on_submit():
example = ExampleModel(
example_id = form.example_id.data,
example_title = form.example_title.data,
added_by = users.get_current_user()
)
example.put()
flash(u'Example successfully saved.', 'success')
return redirect(url_for('list_examples'))
return render_template('new_example.html', form=form)
def upload_pic(request):
c = {}
c.update(csrf(request))
script_args['c'] = c
if request.method == 'POST':
form = ImageUploadForm(request.POST, request.FILES)
if form.is_valid():
m = ExampleModel()
m.model_pic = form.cleaned_data['image']
m.save()
return HttpResponse('image upload success')
return render_to_response("emc/upload_pic.html", c)
def dispatch_request(self):
if request.method == "POST":
form = request.get_json()
example_id = form['example_id']
if example_id:
example = ExampleModel.get_by_id( int(example_id) )
if example:
try:
example.example_name = form['example_name']
example.example_description = form['example_description']
example.put()
return jsonify(result = {'status':'ok'})
except CapabilityDisabledError:
return jsonify(result = {'status':'Error to update'})
else:
return jsonify(result = {'status':'NOT item found'})
else:
return jsonify(result = {'status':'no example_example send'})
else:
return jsonify(result = {'status':'NOT is correct method'})
def on_post(self, req, resp):
email = req.get_json('email',
dtype=str,
min=6,
max=32,
default="*****@*****.**")
name = req.get_json('name',
dtype=str,
min=1,
max=16,
default="Andrei Regiani")
row = ExampleModel(email=email, name=name)
row.save()
resp.json = {'id': str(row.id)}
resp.status = falcon.HTTP_201
def new_example():
"""Add a new example, detecting whether or not App Engine is in read-only mode."""
form = ExampleForm()
if form.validate_on_submit():
example = ExampleModel(
example_id = form.example_id.data,
example_title = form.example_title.data,
added_by = users.get_current_user()
)
try:
example.put()
flash(u'Example successfully saved.', 'success')
return redirect(url_for('list_examples'))
except CapabilityDisabledError:
flash(u'App Engine Datastore is currently in read-only mode.', 'failure')
return redirect(url_for('list_examples'))
return render_template('new_example.html', form=form)
def delete_example(example_id):
example = ExampleModel.get_by_id(example_id)
try:
example.key.delete()
flash(u'Example %s successfully deleted.' % example_id, 'success')
except CapabilityDisabledError:
flash(u'App Engine Datastore is currently in read-only mode.', 'info')
return redirect(url_for('list_examples'))
def dispatch_request(self):
examples = ExampleModel.query()
form = ExampleForm()
if form.validate_on_submit():
example = ExampleModel(
example_name=form.example_name.data,
example_description=form.example_description.data,
added_by=users.get_current_user()
)
try:
example.put()
example_id = example.key.id()
flash(u'Example %s successfully saved.' % example_id, 'success')
return redirect(url_for('list_examples'))
except CapabilityDisabledError:
flash(u'App Engine Datastore is currently in read-only mode.', 'info')
return redirect(url_for('list_examples'))
return render_template('list_examples.html', examples=examples, form=form)
def quick_list():
"""Test function for my simple form"""
form = QuickListForm()
examples = ExampleModel.query()
form.cmb_map.choices = []
for example in examples:
form.cmb_map.choices.append((example.example_description, example.example_name))
return render_template('quick_list.html', form=form)
def dispatch_request(self, example_id):
example = ExampleModel.get_by_id(example_id)
if request.method == "POST":
try:
example.key.delete()
flash(u"Example %s successfully deleted." % example_id, "success")
return redirect(url_for("list_examples"))
except CapabilityDisabledError:
flash(u"App Engine Datastore is currently in read-only mode.", "info")
return redirect(url_for("list_examples"))
def delete_example(example_id):
"""Delete an example object"""
example = ExampleModel.get_by_id(example_id)
try:
example.key.delete()
flash(u'Example %s successfully deleted.' % example_id, 'success')
return redirect(url_for('list_examples'))
except CapabilityDisabledError:
flash(u'App Engine Datastore is currently in read-only mode.', 'info')
return redirect(url_for('list_examples'))
def delete_dimension(dimension_id):
"""Delete an dimension object"""
dimension = ExampleModel.get_by_id(dimension_id)
try:
dimension.key.delete()
flash(u'Example %s successfully deleted.' % dimension_id, 'success')
return redirect(url_for('list_dimensions'))
except CapabilityDisabledError:
flash(u'App Engine Datastore is currently in read-only mode.', 'info')
return redirect(url_for('list_dimensions'))
def list_examples():
"""List all examples"""
examples = ExampleModel.all()
form = ExampleForm()
if form.validate_on_submit():
example = ExampleModel(
example_name=form.example_name.data,
example_description=form.example_description.data,
added_by=users.get_current_user(),
)
try:
example.put()
example_id = example.key().id()
flash(u"Example %s successfully saved." % example_id, "success")
return redirect(url_for("list_examples"))
except CapabilityDisabledError:
flash(u"App Engine Datastore is currently in read-only mode.", "info")
return redirect(url_for("list_examples"))
return render_template("list_examples.html", examples=examples, form=form)
def delete_example(example_id):
"""Delete an example object"""
example = ExampleModel.get_by_id(example_id)
try:
example.delete()
flash(u"Example %s successfully deleted." % example_id, "success")
return redirect(url_for("list_examples"))
except CapabilityDisabledError:
flash(u"App Engine Datastore is currently in read-only mode.", "info")
return redirect(url_for("list_examples"))
def dispatch_request(self, example_id):
example = ExampleModel.get_by_id(example_id)
if request.method == "POST":
try:
example.key.delete()
cache.clear()
flash(u'Example %s successfully deleted.' % example_id, 'success')
return redirect(url_for('list_examples'))
except CapabilityDisabledError:
flash(u'App Engine Datastore is currently in read-only mode.', 'info')
return redirect(url_for('list_examples'))
def edit_example(example_id):
example = ExampleModel.get_by_id(example_id)
form = ExampleForm(obj=example)
if request.method == "POST":
if form.validate_on_submit():
example.example_name = form.data.get('example_name')
example.example_description = form.data.get('example_description')
example.put()
flash(u'Example %s successfully saved.' % example_id, 'success')
return redirect(url_for('list_examples'))
return render_template('edit_example.html', example=example, form=form)
def dispatch_request(self):
if request.method == "POST":
form = request.get_json()
example = ExampleModel(
example_name = form['example_name'],
example_description = form['example_description'],
added_by = users.get_current_user()
)
try:
example.put()
example_id = example.key.id()
return jsonify(result = {'status':'ok'})
except CapabilityDisabledError:
return jsonify(result = {'status':'DONT cant save'})
else:
return jsonify(result = {'status':'NOT is correct method'})
def edit_example(example_id):
example = ExampleModel.get_by_id(example_id)
form = ExampleForm(obj=example)
if request.method == "POST":
if form.validate_on_submit():
example.example_name = form.data.get('example_name')
example.example_description = form.data.get('example_description')
example.put()
flash(u'Example %s successfully saved.' % example_id, 'success')
return redirect(url_for('list_examples'))
return render_template('edit_example.html', example=example, form=form)
def edit_example(example_id):
example = ExampleModel.get_by_id(example_id)
form = ExampleForm(obj=example)
if request.method == "POST":
if form.validate_on_submit():
example.example_name = form.data.get("example_name")
example.example_description = form.data.get("example_description")
example.put()
flash(u"Example %s successfully saved." % example_id, "success")
return redirect(url_for("list_examples"))
return render_template("edit_example.html", example=example, form=form)
def dispatch_request(self, example_id):
example = ExampleModel.get_by_id(example_id)
if request.method == "POST":
try:
example.key.delete()
flash(u'Example %s successfully deleted.' % example_id,
'success')
return redirect(url_for('list_examples'))
except CapabilityDisabledError:
flash(u'App Engine Datastore is currently in read-only mode.',
'info')
return redirect(url_for('list_examples'))
def dashboard():
"""List all examples"""
email = request.args.get('email',"")
profile = request.args.get('profile',"")
logging.info("email=%s profile=%s",email,profile)
examples = ExampleModel.query()
form = ExampleForm()
if form.validate_on_submit():
example = ExampleModel(
example_name=form.example_name.data,
example_description=form.example_description.data,
added_by=users.get_current_user()
)
try:
example.put()
example_id = example.key.id()
flash(u'Example %s successfully saved.' % example_id, 'success')
return redirect(url_for('dashboard'))
except CapabilityDisabledError:
flash(u'App Engine Datastore is currently in read-only mode.', 'info')
return redirect(url_for('dashboard'))
return render_template('dashboard.html', examples=examples, form=form)
def cached_examples():
"""This view should be cached for 60 sec"""
examples = ExampleModel.query()
return render_template('list_examples_cached.html', examples=examples)
def upload_pic(request):
if request.method == 'POST':
form = ImageUploadForm(request.POST, request.FILES)
if form.is_valid():
# os.remove(file) for file in os.listdir('path/to/directory') if file.endswith('.png')
for file in os.listdir(os.path.join(BASE_DIR, 'media', 'img')):
if (file.endswith('.jpg')):
os.remove(os.path.join(BASE_DIR, 'media', 'img', file))
for file in os.listdir(os.path.join(BASE_DIR, 'static', 'img')):
if (file.endswith('.jpg')):
os.remove(os.path.join(BASE_DIR, 'static', 'img', file))
m = ExampleModel()
m.model_pic = form.cleaned_data['image']
m.save()
for file in os.listdir(os.path.join(BASE_DIR, 'media', 'img')):
if (file.endswith('.jpg')):
shutil.move(
os.path.join(BASE_DIR, 'media', 'img', file),
os.path.join(BASE_DIR, 'media', 'img', 'main.jpg'))
shutil.copy(
os.path.join(BASE_DIR, 'media', 'img', 'main.jpg'),
os.path.join(BASE_DIR, 'static', 'img', 'main.jpg'))
pathForImage = os.path.join(BASE_DIR, 'static', 'img', 'main.jpg')
mainImage = cv2.imread(pathForImage)
p = subprocess.Popen(
'/usr/local/src/openalpr/src/build/alpr -c kz -p kz -j ' +
pathForImage,
stdout=subprocess.PIPE,
shell=True)
(output, err) = p.communicate()
p_status = p.wait()
main_out = output.split('\n')[0]
data = (json.loads(main_out))['results']
# print(data)
if (len(data) == 0):
data = {'plateNumber': 'НОМЕР БЫЛ НЕ НАЙДЕН'}
return render(request, 'second.html', data)
else:
coordinates = data[0]['coordinates']
top_left = coordinates[1]
bottom_right = coordinates[3]
y1 = top_left['y']
x1 = top_left['x']
y2 = bottom_right['y']
x2 = bottom_right['x']
cv2.rectangle(mainImage, (x1, int(y1)), (x2, int(y2)),
(0, 255, 0), 1)
cv2.imwrite(pathForImage, mainImage)
plates = data[0]['candidates']
plate_number = (data[0]['candidates'][0]['plate'])
best_predicted = data[0]['candidates'][0]['plate']
best_predicted_by_pattern = []
for plate in plates:
if (plate['matches_template'] == 1):
best_predicted_by_pattern.append(plate['plate'])
if (len(best_predicted_by_pattern) != 0):
best_predicted = best_predicted_by_pattern[0]
data = {'plateNumber': best_predicted}
return render(request, 'second.html', data)
return HttpResponseForbidden('allowed only via POST')
def cached_dimensions():
"""This view should be cached for 60 sec"""
dimensions = ExampleModel.query()
return render_template('list_dimensions_cached.html', dimensions=dimensions)
def cached_examples():
"""This view should be cached for 60 sec"""
examples = ExampleModel.query()
return render_template('list_examples_cached.html', examples=examples)
def dispatch_request(self):
examples = ExampleModel.query()
collective = {'name': 'Viva Dance', 'bucket': 'viva'}
return render_template('collectives/collective.html', examples=examples, collective=collective)
def train_model(data_set_identifier, train_file, val_file, learning_rate,
minibatch_size):
set_protien_experiments_id(data_set_identifier, learning_rate,
minibatch_size)
train_loader = contruct_data_loader_from_disk(train_file, minibatch_size)
validation_loader = contruct_data_loader_from_disk(val_file,
minibatch_size)
validation_dataset_size = validation_loader.dataset.__len__()
model = ExampleModel(9, "ONEHOT", minibatch_size, use_gpu=use_gpu)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# plot settings
if live_plot:
plt.ion()
fig = plt.figure()
sample_num = list()
train_loss_values = list()
validation_loss_values = list()
best_model_loss = 1.1
best_model_minibatch_time = None
best_model_path = None
stopping_condition_met = False
minibatches_proccesed = 0
while not stopping_condition_met:
optimizer.zero_grad()
model.zero_grad()
loss_tracker = np.zeros(0)
for minibatch_id, training_minibatch in enumerate(train_loader, 0):
minibatches_proccesed += 1
primary_sequence, tertiary_positions, mask = training_minibatch
start_compute_loss = time.time()
loss = model.neg_log_likelihood(primary_sequence,
tertiary_positions)
write_out("Train loss:", float(loss))
start_compute_grad = time.time()
loss.backward()
loss_tracker = np.append(loss_tracker, float(loss))
end = time.time()
write_out("Loss time:", start_compute_grad - start_compute_loss,
"Grad time:", end - start_compute_grad)
optimizer.step()
optimizer.zero_grad()
model.zero_grad()
# for every eval_interval samples, plot performance on the validation set
if minibatches_proccesed % eval_interval == 0:
train_loss = loss_tracker.mean()
loss_tracker = np.zeros(0)
validation_loss, data_total = test_eval_model(
validation_loader, model)
prim = data_total[0][0]
pos = data_total[0][1].transpose(0, 1).contiguous().view(-1, 3)
pos_predicted = data_total[0][2].transpose(
0, 1).contiguous().view(-1, 3)
write_to_pdb_strcture(pos, prim, "test")
print(pos)
# cmd.load("output/protein_test.pdb")
write_to_pdb_strcture(pos_predicted.detach(), prim,
"test_pred")
if validation_loss < best_model_loss:
best_model_loss = validation_loss
best_model_minibatch_time = minibatches_proccesed
best_model_path = save_model_on_disk_torch_version(model)
write_out("Validation loss:", validation_loss, "Train loss:",
train_loss)
write_out("Best model so far (label loss): ", validation_loss,
"at time", best_model_minibatch_time)
write_out("Best model stored at " + best_model_path)
write_out("Minibatches processed:", minibatches_proccesed)
sample_num.append(minibatches_proccesed)
train_loss_values.append(train_loss)
validation_loss_values.append(validation_loss)
if live_plot:
drawnow(
draw_plot(fig, plt, validation_dataset_size,
sample_num, train_loss_values,
validation_loss_values))
if minibatches_proccesed > minimum_updates and minibatches_proccesed > best_model_minibatch_time * 2:
stopping_condition_met = True
break
logs(best_model_loss)
return best_model_path
def dispatch_request(self):
examples = ExampleModel.query()
return render_template('ini.html', examples=examples )
def dispatch_request(self):
examples = ExampleModel.query()
collective = {'name': 'Степ студия', 'bucket': 'step'}
return render_template('collectives/collective.html', examples=examples, collective=collective)
def dispatch_request(self):
examples = ExampleModel.query()
collective = {'name': 'Арлекин', 'bucket': 'arlekin'}
return render_template('collectives/collective.html', examples=examples, collective=collective)
def dispatch_request(self):
examples = ExampleModel.query()
return render_template('list_examples_cached.html', examples=examples)
def get_random_entry(): entry = ExampleModel() entry.name = get_random_sentence().capitalize() entry.number = randint(1, 10) entry.description = get_random_sentence(10) return entry
def dispatch_request(self):
list = ExampleModel.query().fetch()
list = to_dict(list)
return jsonify(results = list)
def list_examples():
"""List all examples"""
examples = ExampleModel.all()
return render_template('list_examples.html', examples=examples)
def train_model(data_set_identifier, train_file, val_file, learning_rate,
minibatch_size):
set_experiment_id(data_set_identifier, learning_rate, minibatch_size)
train_loader = contruct_dataloader_from_disk(train_file, minibatch_size)
validation_loader = contruct_dataloader_from_disk(val_file, minibatch_size)
validation_dataset_size = validation_loader.dataset.__len__()
model = ExampleModel(21, minibatch_size,
use_gpu=use_gpu) # embed size = 21
# TODO: is soft_to_angle.parameters() included here?
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
sample_num = list()
train_loss_values = list()
validation_loss_values = list()
rmsd_avg_values = list()
drmsd_avg_values = list()
best_model_loss = 1.1
best_model_minibatch_time = None
best_model_path = None
stopping_condition_met = False
minibatches_proccesed = 0
while not stopping_condition_met:
optimizer.zero_grad()
model.zero_grad()
loss_tracker = np.zeros(0)
for minibatch_id, training_minibatch in enumerate(train_loader, 0):
minibatches_proccesed += 1
primary_sequence, tertiary_positions, mask = training_minibatch
start_compute_loss = time.time()
loss = model.compute_loss(primary_sequence, tertiary_positions)
write_out("Train loss:", float(loss))
start_compute_grad = time.time()
loss.backward()
loss_tracker = np.append(loss_tracker, float(loss))
end = time.time()
write_out("Loss time:", start_compute_grad - start_compute_loss,
"Grad time:", end - start_compute_grad)
optimizer.step()
optimizer.zero_grad()
model.zero_grad()
# for every eval_interval samples, plot performance on the validation set
if minibatches_proccesed % args.eval_interval == 0:
train_loss = loss_tracker.mean()
loss_tracker = np.zeros(0)
validation_loss, data_total, rmsd_avg, drmsd_avg = evaluate_model(
validation_loader, model)
prim = data_total[0][0]
pos = data_total[0][1]
(aa_list, phi_list, psi_list,
omega_list) = calculate_dihedral_angels(prim, pos)
write_to_pdb(
get_structure_from_angles(aa_list, phi_list[1:],
psi_list[:-1], omega_list[:-1]),
"test")
cmd.load("output/protein_test.pdb")
write_to_pdb(data_total[0][3], "test_pred")
cmd.load("output/protein_test_pred.pdb")
cmd.forward()
cmd.orient()
if validation_loss < best_model_loss:
best_model_loss = validation_loss
best_model_minibatch_time = minibatches_proccesed
best_model_path = write_model_to_disk(model)
write_out("Validation loss:", validation_loss, "Train loss:",
train_loss)
write_out("Best model so far (label loss): ", validation_loss,
"at time", best_model_minibatch_time)
write_out("Best model stored at " + best_model_path)
write_out("Minibatches processed:", minibatches_proccesed)
sample_num.append(minibatches_proccesed)
train_loss_values.append(train_loss)
validation_loss_values.append(validation_loss)
rmsd_avg_values.append(rmsd_avg)
drmsd_avg_values.append(drmsd_avg)
if args.live_plot:
data = {}
data["validation_dataset_size"] = validation_dataset_size
data["sample_num"] = sample_num
data["train_loss_values"] = train_loss_values
data["validation_loss_values"] = validation_loss_values
data["phi_actual"] = list(
[math.degrees(float(v)) for v in phi_list[1:]])
data["psi_actual"] = list(
[math.degrees(float(v)) for v in psi_list[:-1]])
data["phi_predicted"] = list([
math.degrees(float(v)) for v in data_total[0]
[2].detach().transpose(0, 1)[0][1:]
])
data["psi_predicted"] = list([
math.degrees(float(v)) for v in data_total[0]
[2].detach().transpose(0, 1)[1][:-1]
])
data["drmsd_avg"] = drmsd_avg_values
data["rmsd_avg"] = rmsd_avg_values
res = requests.post('http://localhost:5000/graph',
json=data)
if res.ok:
print(res.json())
if minibatches_proccesed > args.minimum_updates and minibatches_proccesed > best_model_minibatch_time * 2:
stopping_condition_met = True
break
write_result_summary(best_model_loss)
return best_model_path
def dispatch_request(self):
examples = ExampleModel.query()
collective = {'name': 'Павел Смирнов', 'bucket': 'smirnov'}
return render_template('collectives/collective.html', examples=examples, collective=collective)
