def test_post_delete_batch_images(channel):
stub = service_pb2_grpc.V2Stub(channel)
post_response = stub.PostInputs(
service_pb2.PostInputsRequest(
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(
image=resources_pb2.Image(url=TRUCK_IMAGE_URL, allow_duplicate_url=True)
)
),
resources_pb2.Input(
data=resources_pb2.Data(
image=resources_pb2.Image(url=TRUCK_IMAGE_URL, allow_duplicate_url=True)
)
),
]
),
metadata=metadata(),
)
raise_on_failure(post_response)
input_id1 = post_response.inputs[0].id
input_id2 = post_response.inputs[1].id
wait_for_inputs_upload(stub, metadata(), [input_id1, input_id2])
delete_response = stub.DeleteInputs(
service_pb2.DeleteInputsRequest(ids=[input_id1, input_id2]), metadata=metadata()
)
raise_on_failure(delete_response)
def process_image(self, image_path):
is_valid_url = validators.url(image_path)
if is_valid_url:
request = service_pb2.PostModelOutputsRequest(
# This is the model ID of a publicly available General model. You may use any other public or custom model ID.
model_id=self.model_id,
inputs=[
resources_pb2.Input(data=resources_pb2.Data(image=resources_pb2.Image(url=image_path)))
])
elif os.path.isfile(image_path):
with open(image_path, "rb") as f:
file_bytes = f.read()
request = service_pb2.PostModelOutputsRequest(
# This is the model ID of a publicly available General model. You may use any other public or custom model ID.
model_id=self.model_id,
inputs=[
resources_pb2.Input(data=resources_pb2.Data(image=resources_pb2.Image(base64=file_bytes)))
])
else:
raise ValueError('image_path: {} does not exist'.format(image_path))
response = stub.PostModelOutputs(request, metadata=self.metadata)
if response.status.code != status_code_pb2.SUCCESS:
raise Exception("Request failed, status code: " + str(response.status.code))
else:
return response
def test_search_by_image_url_and_geo_box(channel):
stub = service_pb2_grpc.V2Stub(channel)
with SetupImage(stub) as input_:
response = stub.PostSearches(
service_pb2.PostSearchesRequest(
query=resources_pb2.Query(ands=[
resources_pb2.And(output=resources_pb2.Output(
input=resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=DOG_IMAGE_URL))))),
resources_pb2.And(input=resources_pb2.Input(
data=resources_pb2.Data(geo=resources_pb2.Geo(geo_box=[
resources_pb2.GeoBoxedPoint(
geo_point=resources_pb2.GeoPoint(longitude=43,
latitude=54)),
resources_pb2.GeoBoxedPoint(
geo_point=resources_pb2.GeoPoint(longitude=45,
latitude=56)),
])))),
]),
pagination=service_pb2.Pagination(page=1, per_page=1000),
),
metadata=metadata(),
)
raise_on_failure(response)
assert len(response.hits) > 0
assert input_.id in [hit.input.id for hit in response.hits]
def test_search_by_geo_box_and_annotated_name_and_predicted_name(channel):
stub = service_pb2_grpc.V2Stub(channel)
with SetupImage(stub) as input_:
my_concept_name = input_.data.concepts[0].name
response = stub.PostSearches(
service_pb2.PostSearchesRequest(
query=resources_pb2.Query(ands=[
resources_pb2.And(input=resources_pb2.Input(
data=resources_pb2.Data(geo=resources_pb2.Geo(geo_box=[
resources_pb2.GeoBoxedPoint(
geo_point=resources_pb2.GeoPoint(longitude=43,
latitude=54)),
resources_pb2.GeoBoxedPoint(
geo_point=resources_pb2.GeoPoint(longitude=45,
latitude=56)),
])))),
resources_pb2.And(input=resources_pb2.Input(
data=resources_pb2.Data(concepts=[
resources_pb2.Concept(name=my_concept_name,
value=1)
]))),
resources_pb2.And(output=resources_pb2.Output(
data=resources_pb2.Data(concepts=[
resources_pb2.Concept(name="dog", value=1)
]))),
]),
pagination=service_pb2.Pagination(page=1, per_page=1000),
),
metadata=metadata(),
)
raise_on_failure(response)
assert len(response.hits) > 0
assert input_.id in [hit.input.id for hit in response.hits]
def test_post_list_patch_get_delete_image(channel):
stub = service_pb2_grpc.V2Stub(channel)
post_response = stub.PostInputs(
service_pb2.PostInputsRequest(
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(
image=resources_pb2.Image(url=TRUCK_IMAGE_URL, allow_duplicate_url=True),
concepts=[resources_pb2.Concept(id="some-concept")],
)
)
]
),
metadata=metadata(),
)
raise_on_failure(post_response)
input_id = post_response.inputs[0].id
try:
wait_for_inputs_upload(stub, metadata(), [input_id])
list_response = stub.ListInputs(
service_pb2.ListInputsRequest(per_page=1), metadata=metadata()
)
raise_on_failure(list_response)
assert len(list_response.inputs) == 1
# Most likely we don"t have that many inputs, so this should return 0.
list_response2 = stub.ListInputs(
service_pb2.ListInputsRequest(per_page=500, page=1000), metadata=metadata()
)
raise_on_failure(list_response2)
assert len(list_response2.inputs) == 0
patch_response = stub.PatchInputs(
service_pb2.PatchInputsRequest(
action="overwrite",
inputs=[
resources_pb2.Input(
id=input_id,
data=resources_pb2.Data(
concepts=[resources_pb2.Concept(id="some-new-concept")]
),
)
],
),
metadata=metadata(),
)
raise_on_failure(patch_response)
get_response = stub.GetInput(
service_pb2.GetInputRequest(input_id=input_id), metadata=metadata()
)
raise_on_failure(get_response)
assert get_response.input.data.concepts[0].name == "some-new-concept"
finally:
delete_request = service_pb2.DeleteInputRequest(input_id=input_id)
delete_response = stub.DeleteInput(delete_request, metadata=metadata())
raise_on_failure(delete_response)
def test_predict_video_url_with_custom_sample_ms(channel):
stub = service_pb2_grpc.V2Stub(channel)
request = service_pb2.PostModelOutputsRequest(
model_id=GENERAL_MODEL_ID,
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
video=resources_pb2.Video(url=BEER_VIDEO_URL)))
],
model=resources_pb2.Model(output_info=resources_pb2.OutputInfo(
output_config=resources_pb2.OutputConfig(sample_ms=2000))),
)
response = post_model_outputs_and_maybe_allow_retries(stub,
request,
metadata=metadata())
raise_on_failure(response)
# The expected time per frame is the middle between the start and the end of the frame
# (in milliseconds).
expected_time = 1000
assert len(response.outputs[0].data.frames) > 0
for frame in response.outputs[0].data.frames:
assert frame.frame_info.time == expected_time
expected_time += 2000
def get_tags(self, image_url):
stub = service_pb2_grpc.V2Stub(self.channel)
request = service_pb2.PostModelOutputsRequest(
model_id='aaa03c23b3724a16a56b629203edc62c',
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=image_url)))
])
metadata = (('authorization', 'Key {0}'.format(self.key)), )
response = stub.PostModelOutputs(request, metadata=metadata)
if response.status.code != status_code_pb2.SUCCESS:
raise Exception("Request failed, status code: " +
str(response.status.code))
tags = []
for concept in response.outputs[0].data.concepts:
tags.append(str(concept.name))
print(concept.name)
str1 = ','.join(str(e) for e in tags)
return str1
def request_call_integration(user_url, user_lan):
request = service_pb2.PostModelOutputsRequest(
model_id='aaa03c23b3724a16a56b629203edc62c',
inputs=[
resources_pb2.Input(data=resources_pb2.Data(image=resources_pb2.Image(url=user_url)))
],
model=resources_pb2.Model(
output_info=resources_pb2.OutputInfo(
output_config=resources_pb2.OutputConfig(
language=user_lan
)
)
))
response = stub.PostModelOutputs(request, metadata=metadata)
if response.status.code != status_code_pb2.SUCCESS:
raise Exception("Request failed, status code: " + str(response.status.code))
request_data=[]
for concept in response.outputs[0].data.concepts:
request_data.append(concept.name)
return request_data
def test_post_model_with_hyper_params(channel):
stub = service_pb2_grpc.V2Stub(channel)
model_id = uuid.uuid4().hex[:30]
hyper_params = struct_pb2.Struct()
hyper_params.update({
"MAX_NITEMS": 1000000,
"MIN_NITEMS": 1000,
"N_EPOCHS": 5,
"custom_training_cfg": "custom_training_1layer",
"custom_training_cfg_args": {},
})
post_response = stub.PostModels(
service_pb2.PostModelsRequest(models=[
resources_pb2.Model(
id=model_id,
output_info=resources_pb2.OutputInfo(
data=resources_pb2.Data(concepts=[
resources_pb2.Concept(id="some-initial-concept")
], ),
output_config=resources_pb2.OutputConfig(
hyper_params=hyper_params),
),
)
]),
metadata=metadata(),
)
raise_on_failure(post_response)
assert (post_response.model.output_info.output_config.
hyper_params["custom_training_cfg"] == "custom_training_1layer")
delete_response = stub.DeleteModel(
service_pb2.DeleteModelRequest(model_id=model_id), metadata=metadata())
raise_on_failure(delete_response)
def test_predict_image_url_with_selected_concepts(channel):
stub = service_pb2_grpc.V2Stub(channel)
request = service_pb2.PostModelOutputsRequest(
model_id=GENERAL_MODEL_ID,
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=DOG_IMAGE_URL, ), ), )
],
model=resources_pb2.Model(output_info=resources_pb2.OutputInfo(
output_config=resources_pb2.OutputConfig(select_concepts=[
resources_pb2.Concept(name="dog"),
resources_pb2.Concept(name="cat"),
]))),
)
response = post_model_outputs_and_maybe_allow_retries(stub,
request,
metadata=metadata())
raise_on_failure(response)
concepts = response.outputs[0].data.concepts
assert len(concepts) == 2
dog_concept = [c for c in concepts if c.name == "dog"][0]
cat_concept = [c for c in concepts if c.name == "cat"][0]
assert dog_concept.value > cat_concept.value
def getRecipe():
ingredients = []
# Handle image file
if 'image' not in request.files:
print("did not recive a file")
pass # TODO: handle error
file = request.files['image']
if file.filename == '':
pass # TODO: handle 'No selected file'
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
# A call to clarifai
imageURL = UPLOAD_FOLDER + '/' + filename
with open(imageURL, "rb") as f:
file_bytes = f.read()
print(imageURL)
APIrequest = service_pb2.PostModelOutputsRequest(
model_id='bd367be194cf45149e75f01d59f77ba7',
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(base64=file_bytes)))
])
response = stub.PostModelOutputs(APIrequest, metadata=metadata)
if response.status.code != status_code_pb2.SUCCESS:
raise Exception("Request failed, status code: " +
str(response.status.code))
for ingredient in response.outputs[0].data.concepts:
print('%12s: %.2f' % (ingredient.name, ingredient.value))
ingredients.append(ingredient.name)
# TODO: add delete file after use
else:
pass # TODO: handle error
ingredients.append(request.form['ingredients'])
# Get recipe from spoonacular
if ingredients:
payload = {
'fillIngredients': False,
'ingredients': ingredients,
'limitLicense': False,
'number': 5,
'ranking': 1
}
endpoint = 'https://api.spoonacular.com/recipes/findByIngredients?apiKey=' + SPOONACULAR_KEY
r = requests.get(endpoint, params=payload)
results = r.json()
title = results[0]['title']
print(title)
return jsonify(recipes=results, ingredients=ingredients)
print('error lol')
return jsonify(error="No ingredients were supplied!")
def test_save_and_execute_search_by_id(channel):
stub = service_pb2_grpc.V2Stub(channel)
search_id = "my-search-id-" + uuid.uuid4().hex[:15]
with SetupImage(stub) as input_:
my_concept_id = input_.data.concepts[0].id
# This saves the search under an ID, but does not execute it / return any results.
save_search_response = stub.PostSearches(
service_pb2.PostSearchesRequest(searches=[
resources_pb2.Search(
id=search_id,
save=True,
query=resources_pb2.Query(ands=[
resources_pb2.And(input=resources_pb2.Input(
data=resources_pb2.Data(concepts=[
resources_pb2.Concept(id=my_concept_id,
value=1)
])))
]),
)
]),
metadata=metadata(),
)
raise_on_failure(save_search_response)
# Executing the search returns results.
post_search_by_id_response = stub.PostSearchesByID(
service_pb2.PostSearchesByIDRequest(id=search_id),
metadata=metadata(),
)
raise_on_failure(post_search_by_id_response)
assert len(post_search_by_id_response.hits) == 1
assert post_search_by_id_response.hits[0].input.id == input_.id
def __enter__(self) -> resources_pb2.Input:
my_concept_id = "my-concept-id-" + uuid.uuid4().hex
my_concept_name = "my concept name " + uuid.uuid4().hex
image_metadata = struct_pb2.Struct()
image_metadata.update({
"some-key": "some-value",
"another-key": {
"inner-key": "inner-value"
}
})
post_response = self._stub.PostInputs(
service_pb2.PostInputsRequest(inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=DOG_IMAGE_URL,
allow_duplicate_url=True),
concepts=[
resources_pb2.Concept(
id=my_concept_id, name=my_concept_name, value=1)
],
metadata=image_metadata,
geo=resources_pb2.Geo(geo_point=resources_pb2.GeoPoint(
longitude=44, latitude=55)),
), )
]),
metadata=metadata(),
)
raise_on_failure(post_response)
self._input = post_response.inputs[0]
wait_for_inputs_upload(self._stub, metadata(), [self._input.id])
return self._input
def classification(img):
#with open(img, "rb") as f:
file_bytes = img.read()
post_model_outputs_response = stub.PostModelOutputs(
service_pb2.PostModelOutputsRequest(
model_id="bd367be194cf45149e75f01d59f77ba7",
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(base64=file_bytes)))
]),
metadata=metadata)
if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
raise Exception("Post model outputs failed, status: " +
post_model_outputs_response.status.description)
output = post_model_outputs_response.outputs[0]
consolidate = []
print("Predicted concepts:")
for concept in output.data.concepts:
#print("%s %.2f" % (concept.name, concept.value))
consolidate.append((concept.name, concept.value))
return consolidate
#solucion = classification()
#print(solucion)
def test_image_with_bytes(channel):
stub = service_pb2_grpc.V2Stub(channel)
with open(RED_TRUCK_IMAGE_FILE_PATH, "rb") as f:
file_bytes = f.read()
post_response = stub.PostInputs(
service_pb2.PostInputsRequest(
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(image=resources_pb2.Image(base64=file_bytes))
)
]
),
metadata=metadata(),
)
raise_on_failure(post_response)
input_id = post_response.inputs[0].id
wait_for_inputs_upload(stub, metadata(), [input_id])
delete_response = stub.DeleteInputs(
service_pb2.DeleteInputsRequest(ids=[input_id]), metadata=metadata()
)
raise_on_failure(delete_response)
def test_mixed_success_predict(channel):
stub = service_pb2_grpc.V2Stub(channel)
request = service_pb2.PostModelOutputsRequest(
model_id=GENERAL_MODEL_ID,
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=DOG_IMAGE_URL))),
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=NON_EXISTING_IMAGE_URL))),
],
)
response = stub.PostModelOutputs(request, metadata=metadata())
assert response.status.code == status_code_pb2.MIXED_STATUS
assert response.outputs[0].status.code == status_code_pb2.SUCCESS
assert response.outputs[
1].status.code == status_code_pb2.INPUT_DOWNLOAD_FAILED
def get_concepts(self, url):
request = service_pb2.PostModelOutputsRequest(
model_id='aaa03c23b3724a16a56b629203edc62c',
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=url)))
])
response = self.stub.PostModelOutputs(request, metadata=self.metadata)
if response.status.code != status_code_pb2.SUCCESS:
raise Exception("Request failed, status code: " + str(response.status.code))
return [concept.name for concept in response.outputs[0].data.concepts]
def img_mod_embed(self, url):
request = service_pb2.PostModelOutputsRequest(
model_id='d16f390eb32cad478c7ae150069bd2c6',
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=url)))
])
metadata = (('authorization', self.app_id), )
response = self.stub.PostModelOutputs(request, metadata=metadata)
if response.status.code != status_code_pb2.SUCCESS:
raise Exception("Request failed, status code: " +
str(response.status.code))
return [x.value for x in response.outputs[0].data.concepts]
def txt_embed(self, caption):
response = self.stub.PostModelOutputs(
service_pb2.PostModelOutputsRequest(
model_id="568d48e82924a00d0f98a6d34fa426cf",
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
text=resources_pb2.Text(raw=caption)))
]),
metadata=(('authorization', self.app_id), ))
if response.status.code != status_code_pb2.SUCCESS:
raise Exception("Request failed, status code: " +
str(response.status.code))
return response.outputs[0].data.embeddings[0].vector
def txt_mod_embed(self, caption):
response = self.stub.PostModelOutputs(
service_pb2.PostModelOutputsRequest(
model_id="39f2950a32173f61b3eb40ede0d254e1",
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
text=resources_pb2.Text(raw=caption)))
]),
metadata=(('authorization', self.app_id), ))
if response.status.code != status_code_pb2.SUCCESS:
raise Exception("Request failed, status code: " +
str(response.status.code))
return response.outputs[0].data.embeddings[0].vector
def test_predict_image_url(channel):
stub = service_pb2_grpc.V2Stub(channel)
request = service_pb2.PostModelOutputsRequest(
model_id=GENERAL_MODEL_ID,
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=DOG_IMAGE_URL)))
],
)
response = stub.PostModelOutputs(request, metadata=metadata())
raise_on_failure(response)
assert len(response.outputs[0].data.concepts) > 0
def img_embed(self, url):
request = service_pb2.PostModelOutputsRequest(
model_id='bbb5f41425b8468d9b7a554ff10f8581',
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=url)))
])
metadata = (('authorization', self.app_id), )
response = self.stub.PostModelOutputs(request, metadata=metadata)
if response.status.code != status_code_pb2.SUCCESS:
raise Exception("Request failed, status code: " +
str(response.status.code))
return response.outputs[0].data.embeddings[0].vector
def __enter__(self) -> resources_pb2.Input:
post_response = self._stub.PostInputs(
service_pb2.PostInputsRequest(inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=TRAVEL_IMAGE_URL,
allow_duplicate_url=True), ), )
]),
metadata=metadata(),
)
raise_on_failure(post_response)
self._input = post_response.inputs[0]
wait_for_inputs_upload(self._stub, metadata(), [self._input.id])
return self._input
def get_tags_from_url(image_url):
tags = []
request = service_pb2.PostModelOutputsRequest(
model_id='aaa03c23b3724a16a56b629203edc62c',
inputs=[
resources_pb2.Input(data=resources_pb2.Data(image=resources_pb2.Image(url=image_url)))
])
response = stub.PostModelOutputs(request, metadata=metadata)
if response.status.code != status_code_pb2.SUCCESS:
raise Exception("Request failed, status code: " + str(response.status.code))
for concept in response.outputs[0].data.concepts:
tags.append(concept.name)
return tags
def test_failed_predict(channel):
stub = service_pb2_grpc.V2Stub(channel)
request = service_pb2.PostModelOutputsRequest(
model_id=GENERAL_MODEL_ID,
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=NON_EXISTING_IMAGE_URL)))
],
)
response = stub.PostModelOutputs(request, metadata=metadata())
assert response.status.code == status_code_pb2.FAILURE
assert response.status.description == "Failure"
assert response.outputs[
0].status.code == status_code_pb2.INPUT_DOWNLOAD_FAILED
def img_txt_embed(self, url, caption):
request = service_pb2.PostModelOutputsRequest(
model_id='aaa03c23b3724a16a56b629203edc62c',
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=url)))
])
metadata = (('authorization', self.app_id), )
response = self.stub.PostModelOutputs(request, metadata=metadata)
if response.status.code != status_code_pb2.SUCCESS:
raise Exception("Request failed, status code: " +
str(response.status.code))
img_cons = ' '.join(
[x.name for x in response.outputs[0].data.concepts])
return self.txt_embed(caption + '. ' +
' '.join(img_cons.split(' ')[:10]))
def test_predict_image_url_with_max_concepts(channel):
stub = service_pb2_grpc.V2Stub(channel)
request = service_pb2.PostModelOutputsRequest(
model_id=GENERAL_MODEL_ID,
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=DOG_IMAGE_URL, ), ), )
],
model=resources_pb2.Model(output_info=resources_pb2.OutputInfo(
output_config=resources_pb2.OutputConfig(max_concepts=3))),
)
response = stub.PostModelOutputs(request, metadata=metadata())
raise_on_failure(response)
assert len(response.outputs[0].data.concepts) == 3
def test_predict_video_url(channel):
stub = service_pb2_grpc.V2Stub(channel)
request = service_pb2.PostModelOutputsRequest(
model_id=GENERAL_MODEL_ID,
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
video=resources_pb2.Video(url=CONAN_GIF_VIDEO_URL)))
],
)
response = stub.PostModelOutputs(request, metadata=metadata())
raise_on_failure(response)
assert len(response.outputs[0].data.frames) > 0
for frame in response.outputs[0].data.frames:
assert len(frame.data.concepts) > 0
def test_image_predict_on_public_models(channel):
stub = service_pb2_grpc.V2Stub(channel)
for title, model_id in MODEL_TITLE_AND_ID_PAIRS:
request = service_pb2.PostModelOutputsRequest(
model_id=model_id,
inputs=[
resources_pb2.Input(data=resources_pb2.Data(
image=resources_pb2.Image(url=DOG_IMAGE_URL)))
],
)
response = stub.PostModelOutputs(request, metadata=metadata())
raise_on_failure(
response,
custom_message=
f"Image predict failed for the {title} model (ID: {model_id}).",
)
def test_search_by_annotated_concept_name(channel):
stub = service_pb2_grpc.V2Stub(channel)
with SetupImage(stub) as input_:
my_concept_name = input_.data.concepts[0].name
response = stub.PostSearches(
service_pb2.PostSearchesRequest(query=resources_pb2.Query(ands=[
resources_pb2.And(input=resources_pb2.Input(
data=resources_pb2.Data(concepts=[
resources_pb2.Concept(name=my_concept_name, value=1)
])))
])),
metadata=metadata(),
)
raise_on_failure(response)
assert len(response.hits) == 1
assert response.hits[0].input.id == input_.id
