def isWearingMask(file_path):
prediction_client = automl.PredictionServiceClient()
# Get the full path of the model.
model_full_id = automl.AutoMlClient.model_path(project_id, "us-central1",
model_id)
# Read the file.
with open(file_path, "rb") as content_file:
content = content_file.read()
image = automl.Image(image_bytes=content)
payload = automl.ExamplePayload(image=image)
# params is additional domain-specific parameters.
# score_threshold is used to filter the result
# https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#predictrequest
params = {"score_threshold": "0.1"}
request = automl.PredictRequest(name=model_full_id,
payload=payload,
params=params)
response = prediction_client.predict(request=request)
#print("Prediction results:")
print(len(response.payload))
for result in response.payload:
print("Predicted class name: {}".format(result.display_name))
print("Predicted class score: {}".format(result.classification.score))
return True if (result.display_name == "with_mask"
and result.classification.score > 0.9999) else False
return False
def get_results_from_automl(filepath):
project_id = "numeric-polygon-283403"
model_id = "ICN2007288218877165568"
prediction_client = automl.PredictionServiceClient()
# Get the full path of the model.
model_full_id = automl.AutoMlClient.model_path(project_id, "us-central1",
model_id)
# Read the file.
with open(filepath, "rb") as content_file:
content = content_file.read()
image = automl.Image(image_bytes=content)
payload = automl.ExamplePayload(image=image)
# params is additional domain-specific parameters, score_threshold is used to filter the result
# https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#predictrequest
params = {"score_threshold": "0.5"}
request = automl.PredictRequest(name=model_full_id,
payload=payload,
params=params)
response = prediction_client.predict(request=request)
results = []
for result in response.payload:
results.append([result.display_name, result.classification.score])
return results
def predict(project_id, model_id, content):
"""Predict."""
# [START automl_language_entity_extraction_predict]
from google.cloud import automl
# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# model_id = "YOUR_MODEL_ID"
# content = "text to predict"
prediction_client = automl.PredictionServiceClient()
# Get the full path of the model.
model_full_id = automl.AutoMlClient.model_path(project_id, "us-central1",
model_id)
# Supported mime_types: 'text/plain', 'text/html'
# https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#textsnippet
text_snippet = automl.TextSnippet(content=content, mime_type="text/plain")
payload = automl.ExamplePayload(text_snippet=text_snippet)
response = prediction_client.predict(name=model_full_id, payload=payload)
for annotation_payload in response.payload:
print("Text Extract Entity Types: {}".format(
annotation_payload.display_name))
print("Text Score: {}".format(
annotation_payload.text_extraction.score))
text_segment = annotation_payload.text_extraction.text_segment
print("Text Extract Entity Content: {}".format(text_segment.content))
print("Text Start Offset: {}".format(text_segment.start_offset))
print("Text End Offset: {}".format(text_segment.end_offset))
def predict(content):
"""
Predict labels
"""
project_id = c.PROJECT_ID
model_id = c.MODEL_ID
location = c.LOCATION
predictor = automl.PredictionServiceClient.from_service_account_json(
"service_account.json")
model_full_id = automl.AutoMlClient.model_path(project_id, location,
model_id)
image = automl.Image(image_bytes=content)
payload = automl.ExamplePayload(image=image)
request = automl.PredictRequest(name=model_full_id, payload=payload)
response = predictor.predict(request=request)
pred_sum = 0
prediction = {}
for result in response.payload:
prediction[result.display_name] = result.classification.score
pred_sum += result.classification.score
if pred_sum != 0:
for pred in prediction:
prediction[pred] /= pred_sum
return prediction
def predict(filePath):
project_id = "762814515048"
model_id = "ICN244084984296505344"
file_path = filePath
prediction_client = automl.PredictionServiceClient()
# Get the full path of the model.
model_full_id = automl.AutoMlClient.model_path(project_id, "us-central1",
model_id)
# Read the file.
with open(file_path, "rb") as content_file:
content = content_file.read()
image = automl.Image(image_bytes=content)
payload = automl.ExamplePayload(image=image)
# params is additional domain-specific parameters.
# score_threshold is used to filter the result
# https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#predictrequest
params = {"score_threshold": "0.7"}
request = automl.PredictRequest(name=model_full_id,
payload=payload,
params=params)
response = prediction_client.predict(request=request)
print("Prediction results:")
results = []
for result in response.payload:
results.append(result.display_name)
results.append(result.classification.score)
print("Predicted class name: {}".format(result.display_name))
print("Predicted class score: {}".format(result.classification.score))
return (results)
def predict(project_id, model_id, file_path):
"""Predict."""
# [START automl_vision_classification_predict]
from google.cloud import automl
# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# model_id = "YOUR_MODEL_ID"
# file_path = "path_to_local_file.jpg"
prediction_client = automl.PredictionServiceClient()
# Get the full path of the model.
model_full_id = automl.AutoMlClient.model_path(project_id, "us-central1", model_id)
# Read the file.
with open(file_path, "rb") as content_file:
content = content_file.read()
image = automl.Image(image_bytes=content)
payload = automl.ExamplePayload(image=image)
# params is additional domain-specific parameters.
# score_threshold is used to filter the result
# https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#predictrequest
params = {"score_threshold": "0.8"}
request = automl.PredictRequest(name=model_full_id, payload=payload, params=params)
response = prediction_client.predict(request=request)
print("Prediction results:")
for result in response.payload:
print("Predicted class name: {}".format(result.display_name))
print("Predicted class score: {}".format(result.classification.score))
def predict(project_id, model_id, file_path):
"""Predict."""
# [START automl_translate_predict]
from google.cloud import automl
# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# model_id = "YOUR_MODEL_ID"
# file_path = "path_to_local_file.txt"
prediction_client = automl.PredictionServiceClient()
# Get the full path of the model.
model_full_id = automl.AutoMlClient.model_path(project_id, "us-central1",
model_id)
# Read the file content for translation.
with open(file_path, "rb") as content_file:
content = content_file.read()
content.decode("utf-8")
text_snippet = automl.TextSnippet(content=content)
payload = automl.ExamplePayload(text_snippet=text_snippet)
response = prediction_client.predict(name=model_full_id, payload=payload)
translated_content = response.payload[0].translation.translated_content
print(u"Translated content: {}".format(translated_content.content))
def input_symptoms():
if request.method == 'POST':
try:
travel = int(request.form['travel'])
except:
travel = 0
try:
tiredcough = int(request.form['commonsym'])
except:
tiredcough = 0
try:
breath = int(request.form['majorsym'])
except:
breath = 0
try:
exposure = int(request.form['exposure'])
except:
exposure = 0
try:
image = request.files['image']
image.save('beach1.bmp')
except:
return render_template('index.html', results="No File Found")
file_path = "beach1.bmp"
with open(file_path, "rb") as content_file:
content = content_file.read()
image = automl.Image(image_bytes=content)
payload = automl.ExamplePayload(image=image)
# params is additional domain-specific parameters.
# score_threshold is used to filter the result
# https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#predictrequest
params = {"score_threshold": "0.8"}
request2 = automl.PredictRequest(name=model_full_id,
payload=payload,
params=params)
# 'content' is base-64-encoded image data.
response = prediction_client.predict(request=request2)
for result in response.payload:
var1 = result.display_name
var2 = result.classification
result2 = 'Disease: ', var1, ' Probability of disease: ', var2
result = travel + tiredcough + breath + exposure
if result >= 3:
result = 'You have high chances of being covid positive'
else:
result = 'Please take care of yourself(wear a mask :D)'
return render_template('index.html', results=result, result2=result2)
return None
async def process(req: models.Request):
"""
Analyze a given piece of text to find the bias. Any given piece of text will be checked against the database to
check if it has already been processed. If it has not yet been processed, it will be sent for inference on
a GCP NLP trained model.
"""
# Either fetch the content from the site or use the provided text
if req.url != "":
try:
article = Article(req.url)
article.download()
article.parse()
if article.text != "" and article.text is not None:
text = article.text
else:
text = req.text
except ArticleException:
text = req.text
else:
text = req.text
# Calculate hash of id and text
job_hash = compute_job_hash(req.id, text)
# Ensure job has not been computed or is being computed
job_ref = db.collection("text").document(job_hash)
job = job_ref.get()
if job.exists:
job_data = job.to_dict()
return {
"success": True,
"bias": job_data.get("bias"),
"extent": job_data.get("extent")
}
# Process the data
snippet = automl.TextSnippet(content=text, mime_type="text/plain")
payload = automl.ExamplePayload(text_snippet=snippet)
response = predictor.predict(name=MODEL_PATH, payload=payload)
bias, extent = extract_from_categories(response.payload)
# Set the processed data to the database
job_ref.set({
"hash": job_hash,
"bias": bias,
"extent": extent
})
return {
"success": True,
"bias": bias,
"extent": extent
}
def predictionCall(file_path_image):
#returns faces detected in image
faces = detectFaces(file_path_image)
count = 0
for face in faces:
count = count + 1
im = Image.open(file_path_image)
xTopLeft = face[0]
yTopLeft = face[1]
xBotRight = face[2]
yBotRight = face[3]
im1 = im.crop((xTopLeft, yTopLeft, xBotRight, yBotRight))
img_path = "Predict" + str(count) + ".jpg"
im1 = im1.save(img_path)
# Read the file.
with open(img_path, "rb") as content_file:
content = content_file.read()
image = automl.Image(image_bytes=content)
payload = automl.ExamplePayload(image=image)
params = {"score_threshold": "0.8"}
request = automl.PredictRequest(
name=model_full_id,
payload=payload,
params=params
)
response = prediction_client.predict(request=request)
mask = False
unmask = False
both = False
print("Prediction results:")
for result in response.payload:
if "UnmaskedPeople" == result.display_name:
unmask = True
if "MaskedPeople" == result.display_name:
mask = True
#print("Predicted class name: {}".format(result.display_name))
#print("Predicted class score: {}".format(result.classification.score))
if mask == True and unmask == True:
both = True
if both == True:
print("Person " + str(count) + ": Error. Both Mask and Unmask detected")
elif mask == True:
print("Person " + str(count) + " is wearing mask :)")
else:
print("Person " + str(count) + " is NOT wearing a mask :(")
def predict():
if request.method == 'POST':
comment = request.form['comment']
#data = [comment]
text_snippet = automl.TextSnippet(content=comment,
mime_type="text/plain")
payload = automl.ExamplePayload(text_snippet=text_snippet)
my_prediction = prediction_client.predict(name=model_full_id,
payload=payload)
return render_template('results.html',
prediction=my_prediction,
comment=comment)
def get_prediction(img):
with open(img, "rb") as content_file:
content = content_file.read()
image = automl.Image(image_bytes=content)
payload = automl.ExamplePayload(image=image)
request = automl.PredictRequest(name=model_full_id, payload=payload, params={})
response = prediction_client.predict(request=request)
t = response.payload[0]
if t.display_name == 'negative':
return 1-t.classification.score
else:
return t.classification.score
def prediction():
if request.method == 'POST':
commentInput = request.form
for key, item in commentInput.items():
text_snippet = automl.TextSnippet(content=item,
mime_type="text/plain")
payload = automl.ExamplePayload(text_snippet=text_snippet)
response = prediction_client.predict(name=model_full_id,
payload=payload)
for annotation_payload in response.payload:
sentPred = annotation_payload.display_name
break
return render_template('results.html',
sentPred=sentPred,
content=content)
def predict(filepath):
threshold = "0.1"
with open(filepath, "rb") as content_file:
content = content_file.read()
image = automl.Image(image_bytes=content)
payload = automl.ExamplePayload(image=image)
params = {"score_threshold": threshold}
request = automl.PredictRequest(
name=model_full_id,
payload=payload,
params=params
)
response = prediction_client.predict(request=request)
return {**{result.display_name.capitalize(): result.classification.score for result in response.payload},
**{'Name': os.path.basename(filepath)}}
def predict(content):
# Supported mime_types: 'text/plain', 'text/html'
# https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#textsnippet
text_snippet = automl.TextSnippet(content=content, mime_type="text/plain")
payload = automl.ExamplePayload(text_snippet=text_snippet)
response = prediction_client.predict(name=model_full_id, payload=payload)
for annotation_payload in response.payload:
# print(
# u"Predicted class name: {}".format(annotation_payload.display_name)
# )
# print(
# u"Predicted class score: {}".format(
# annotation_payload.classification.score
# )
# )
return annotation_payload.display_name
break
def predict(content):
prediction_client = automl.PredictionServiceClient()
# Get the full path of the model.
model_full_id = automl.AutoMlClient.model_path(project_id, "us-central1",
model_id)
# Supported mime_types: 'text/plain', 'text/html'
# https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#textsnippet
text_snippet = automl.TextSnippet(content=content, mime_type="text/plain")
payload = automl.ExamplePayload(text_snippet=text_snippet)
response = prediction_client.predict(name=model_full_id, payload=payload)
for annotation_payload in response.payload:
print("Predicted class name: {}".format(
annotation_payload.display_name))
print("Predicted sentiment score: {}".format(
annotation_payload.text_sentiment.sentiment))
return annotation_payload.text_sentiment.sentiment
def predict(content):
from google.cloud import automl
# You must first create a dataset, using the `eu` endpoint, before you can
# call other operations such as: list, get, import, delete, etc.
client_options = {'api_endpoint': 'eu-automl.googleapis.com:443'}
project_id = '685330484131'
# (model AI Crowd) model_id = 'TCN3300918624537018368'
model_id = 'TCN4629621252099670016'
prediction_client = automl.PredictionServiceClient(
client_options=client_options)
# Get the full path of the model.
model_full_id = automl.AutoMlClient.model_path(project_id, "eu", model_id)
text_snippet = automl.TextSnippet(content=content, mime_type="text/plain")
payload = automl.ExamplePayload(text_snippet=text_snippet)
response = prediction_client.predict(name=model_full_id, payload=payload)
return response
def get_prediction(file_path):
project_id = "msds-434-final"
model_id = "ICN7344581542892535808"
# file_path = "uploads/city.png" ## for local image testing
credentials = service_account.Credentials.from_service_account_file("/home/jesse_lybianto/msds-434-final/msds-434-final-f32a0ccc78d5.json")
prediction_client = automl.PredictionServiceClient(credentials=credentials)
# Get the full path of the model.
model_full_id = automl.AutoMlClient.model_path(
project_id, "us-central1", model_id
)
# Read the file.
with open(file_path, "rb") as content_file:
content = content_file.read()
image = automl.Image(image_bytes=content)
payload = automl.ExamplePayload(image=image)
# params is additional domain-specific parameters.
# score_threshold is used to filter the result
# https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#predictrequest
params = {"score_threshold": "0.0"}
request = automl.PredictRequest(
name=model_full_id,
payload=payload,
params=params
)
response = prediction_client.predict(request=request)
print(response.payload)
return response.payload
# For non-modular console output
# print("Prediction results:")
# for result in response.payload:
# print("Predicted class name: {}".format(result.display_name))
# print("Predicted class score: {}".format(result.classification.score))
def home_page():
# from flask import request
if request.method == 'GET':
context = {"done": False}
return render_template('site.html', **context)
else:
# get image content from POST request inputs
file = request.files["myFile"]
image_content = b''
for data in file.stream: # read image as a stream
image_content += data
# google api variables
project_id = "alpine-infinity-290102"
prediction_client = automl.PredictionServiceClient()
# Get the full path of the model.
model_full_id = automl.AutoMlClient.model_path(project_id,
"us-central1", model_id)
image = automl.Image(image_bytes=image_content)
payload = automl.ExamplePayload(image=image)
req = automl.PredictRequest(
name=model_full_id,
payload=payload,
)
response = prediction_client.predict(request=req)
class_ = response.payload[0].display_name
measure = response.payload[0].classification.score
context = {
"done": True,
"guess_mal": class_ == "Malignant",
"class_": class_,
"measure": '{:.2%}'.format(measure)
}
return render_template('site.html', **context)
def nlp_predict(self, model_id: (str, 'the id of the deployed nlp model'),
content: (str,
'the text to submit to the prediction model')):
response_payload = None
try:
model_full_id = self.automl_client.model_path(
self.project_id, self.region, model_id)
text_snippet = automl.TextSnippet(content=content,
mime_type="text/plain")
payload = automl.ExamplePayload(text_snippet=text_snippet)
#payload = {'text_snippet': {'content': content, 'mime_type': 'text/plain' }}
response = self.prediction_client.predict(name=model_full_id,
payload=payload)
response_payload = response.payload
except Exception:
logger.exception("")
return response_payload
def produce_automl_results(dir_img_path: str = "",
dir_destination_path: str = ""):
from google.cloud import automl
project_id = "hidden"
model_id = "hidden"
imgs = os.listdir(dir_img_path)
relevant_images = [
os.path.join(dir_img_path, i) for i in imgs if "png" in i
]
prediction_client = automl.PredictionServiceClient()
project_id = "hidden"
model_id = "hidden"
# Get the full path of the model.
model_full_id = automl.AutoMlClient.model_path(project_id, "us-central1",
model_id)
file_path = "../../data/005.png"
with open(file_path, "rb") as content_file:
content = content_file.read()
image = automl.Image(image_bytes=content)
payload = automl.ExamplePayload(image=image)
params = {}
request = automl.PredictRequest(name=model_full_id,
payload=payload,
params=params)
response = prediction_client.predict(request=request)
print("Prediction results:")
for result in response.payload:
print("Predicted class name: {}".format(result.display_name))
print("Predicted class score: {}".format(result.classification.score))
import os
from google.cloud import automl
from dotenv import load_dotenv
load_dotenv()
project_id = os.getenv("PROJECT_ID")
model_id = os.getenv("MODEL_ID")
content = "How does the len() function work?"
prediction_client = automl.PredictionServiceClient()
# Get the full path of the model.
model_full_id = automl.AutoMlClient.model_path(project_id, "us-central1",
model_id)
text_snippet = automl.TextSnippet(content=content, mime_type="text/plain")
payload = automl.ExamplePayload(text_snippet=text_snippet)
response = prediction_client.predict(name=model_full_id, payload=payload)
for annotation_payload in response.payload:
print(u"Predicted question tag: {}".format(
annotation_payload.display_name))
print(u"Predicted score: {}".format(
annotation_payload.classification.score))
def automl_vision(file_path):
"""Predict."""
# [START automl_vision_classification_predict]
#print(file_path)
# autoML JSon 키 설정
#credential_path = r"키.json"
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = credential_path
project_id = "수정"
model_id = "수정"
# 원본 이미지
img = cv2.imread(file_path)
# 원본 이미지 크기
img_height = img.shape[0]
img_width = img.shape[1]
# RGB 색상으로 변환
orig = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
prediction_client = automl.PredictionServiceClient()
# Get the full path of the model.
model_full_id = automl.AutoMlClient.model_path(project_id, "us-central1",
model_id)
# 파일 읽어들이기
with open(file_path, "rb") as content_file:
content = content_file.read()
image = automl.Image(image_bytes=content)
payload = automl.ExamplePayload(image=image)
# params is additional domain-specific parameters.
# score_threshold is used to filter the result
# https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#predictrequest
params = {"score_threshold": "0.8"}
request = automl.PredictRequest(name=model_full_id,
payload=payload,
params=params)
response = prediction_client.predict(request=request)
tags = []
for result in response.payload:
# 태그로 잡힌 박스 몇 개인지
bounding_box = result.image_object_detection.bounding_box
# normalized vertices로 되어있는 좌표를 실사이즈로 변환 및 직사각형의 네 꼭짓점 좌표를 구함
lu = bounding_box.normalized_vertices[0] # left/up x, y 좌표
rd = bounding_box.normalized_vertices[1] # right/down x, y 좌표
w = rd.x - lu.x
h = rd.y - lu.y
# cropping
cropped_img = orig[int(lu.y * img_height):int((lu.y + h) * img_height),
int(lu.x * img_width):int((lu.x + w) * img_width)]
texts = my_detect_text_mat(
cropped_img) #잘린 이미지들 속 글자 추출하기 위해 my_detect_text_mat() 호출
tags.append(texts[0].description)
return tags
prediction_client = automl.PredictionServiceClient()
# Get the full path of the model.
model_full_id = automl.AutoMlClient.model_path(
project_id, "us-central1", model_id
)
# Read the file.
with open(file_path, "rb") as content_file:
content = content_file.read()
image = automl.Image(image_bytes=content)
payload = automl.ExamplePayload(image=image)
request = automl.PredictRequest(
name=model_full_id,
payload=payload
)
response = prediction_client.predict(request=request)
print("Prediction results:")
for result in response.payload:
print("Predicted class name: {}".format(result.display_name))
print("Predicted class score: {}".format(result.classification.score))
def request_prediction(file):
# model path
project_id = "241661365506"
model_id = "IOD6459342741137522688"
name = "projects/{}/locations/us-central1/models/{}".format(
project_id, model_id)
prediction_client = automl.PredictionServiceClient.from_service_account_json(
"GCPkeys.json")
# open image file and convert to base64
image = automl.Image(image_bytes=file.read())
payload = automl.ExamplePayload(image=image)
params = {}
request = automl.PredictRequest(name=name, payload=payload, params=params)
# request a prediction
try:
response = prediction_client.predict(request=request)
except:
card = "The Eye is shut. Please try again later."
score = 0
# now translate the prediction to nicer looking text
try:
score = round(response.payload[0].image_object_detection.score, 4)
if response.payload[0].display_name == "chariot":
card = "The Chariot"
elif response.payload[0].display_name == "death":
card = "Death"
elif response.payload[0].display_name == "devil":
card = "The Devil"
elif response.payload[0].display_name == "emperor":
card = "The Emperor"
elif response.payload[0].display_name == "empress":
card = "The Empress"
elif response.payload[0].display_name == "fool":
card = "The Fool"
elif response.payload[0].display_name == "Fortune":
card = "Fortune"
elif response.payload[0].display_name == "hanged":
card = "The Hanged Man"
elif response.payload[0].display_name == "hermit":
card = "The Hermit"
elif response.payload[0].display_name == "hierophant":
card = "The Hierophant"
elif response.payload[0].display_name == "judgment":
card = "Judgment"
elif response.payload[0].display_name == "justice":
card = "Justice"
elif response.payload[0].display_name == "lovers":
card = "The Lovers"
elif response.payload[0].display_name == "magician":
card = "The Magician"
elif response.payload[0].display_name == "moon":
card = "The Moon"
elif response.payload[0].display_name == "priestess":
card = "The Priestess"
elif response.payload[0].display_name == "star":
card = "The Star"
elif response.payload[0].display_name == "strength":
card = "Strength"
elif response.payload[0].display_name == "sun":
card = "The Sun"
elif response.payload[0].display_name == "temperance":
card = "Temperance"
elif response.payload[0].display_name == "tower":
card = "The Tower"
elif response.payload[0].display_name == "world":
card = "The World"
else:
card = "Your future is murky. Try another card."
score = 666
except:
card = "Your future is murky. Try another card."
score = 666
return card, score
