def app_function():
try:
# Read account id and private from environment variables
account_id = account['ID']
private_key = ''
with open('einstein_platform.pem', 'r') as f:
for line in f:
private_key += line
# Set expiry time
expiry = int(time.time()) + (15 * 60)
# Generate an assertion using RSA private key
assertion = jwt_helper.generate_assertion(account_id, private_key,
expiry)
# Obtain oauth token
token = token_generator.generate_token(assertion)
response = token.json()
# If there is no token print the response
if 'access_token' not in response:
raise ValueError(
"Access token generation failed. Received reply: \"{}\"".
format(response))
else:
# Collect the access token from response
access_token = response['access_token']
# Make a prediction call using image url
prediction_url_response = prediction.predict_with_url(
access_token, 'GeneralImageClassifier',
'https://animalso.com/wp-content/uploads/2017/01/Siberian-Husky_7.jpg'
)
# Print prediction response
pprint.pprint(prediction_url_response.json())
# Make a prediction call using image file
# prediction_file_response = prediction.predict_with_image_file(access_token, 'GeneralImageClassifier',
# '/path/to/image/file/Siberian-Husky.jpg')
# Print prediction response
# pprint.pprint(prediction_file_response.json())
except Exception as e:
traceback.print_exc()
def main():
try:
print("Hello")
# Read account id and private from environment variables
account_id = os.environ['EINSTEIN_VISION_ACCOUNT_ID']
private_key = os.environ['EINSTEIN_VISION_PRIVATE_KEY'].decode(
'string_escape')
# Set expiry time
expiry = int(time.time()) + (15 * 60)
# Generate an assertion using RSA private key
assertion = jwt_helper.generate_assertion(account_id, private_key,
expiry)
# Obtain oauth token
token = token_generator.generate_token(assertion)
print("CC")
response = token.json()
print(response)
# If there is no token print the response
if 'access_token' not in response:
raise ValueError(
"Access token generation failed. Received reply: \"{}\"".
format(response))
else:
# Collect the access token from response
access_token = response['access_token']
# Make a prediction call using image url
prediction_url_response = prediction.predict_with_url(
access_token, 'GeneralImageClassifier',
'https://animalso.com/wp-content/uploads/2017/01/Siberian-Husky_7.jpg'
)
# Print prediction response
pprint.pprint(prediction_url_response.json())
# Make a prediction call using image file
# prediction_file_response = prediction.predict_with_image_file(access_token, 'GeneralImageClassifier',
# '/path/to/image/file/Siberian-Husky.jpg')
# Print prediction response
# pprint.pprint(prediction_file_response.json())
except Exception as e:
traceback.print_exc()
def main():
try:
account_id = '*****@*****.**' # Your Einstein Account ID
private_key = open("einstein_platform.pem",
"r").read() #Your Einstein Private Key file
# Set expiry time
expiry = int(time.time()) + (15 * 60)
# Generate an assertion using RSA private key
assertion = jwt_helper.generate_assertion(account_id, private_key,
expiry)
# Obtain oauth token
token = token_generator.generate_token(assertion)
response = token.json()
# If there is no token print the response
if 'access_token' not in response:
raise ValueError(
"Access token generation failed. Received reply: \"{}\"".
format(response))
else:
# Collect the access token from response
access_token = response['access_token']
#data_file='Rakesh_Sample.csv' #SFCC_Q1_2019_Recognition_Data.csv'
data_file = 'TextFileForSentimentAnalysis.csv'
data_file_with_sentiment = "Sentiment_of_" + data_file
cleandata(data_file)
df = pandas.read_csv(data_file)
#Add three additional column to update sentiment value for the text
df.insert(1, 'positive', '')
df.insert(2, 'neutral', '')
df.insert(3, 'negative', '')
#df.insert(4,'word_count','')
for index, row in df.iterrows():
# Make a Sentiment prediction call
print('Submited request for sentiment analysis of of text index #',
index)
prediction_url_response = predictionSentiment.predict(
access_token, row['citation'])
print('Recieved sentiment for text index #', index)
# Print prediction response
resp = prediction_url_response.json()
probabilities = resp['probabilities']
for sentiment in probabilities:
#pprint.pprint(sentiment)
if sentiment['label'] == 'positive':
row['positive'] = sentiment['probability']
elif sentiment['label'] == 'neutral':
row['neutral'] = sentiment['probability']
elif sentiment['label'] == 'negative':
row['negative'] = sentiment['probability']
df.to_csv(data_file_with_sentiment)
textStatistics(data_file_with_sentiment)
except Exception as e:
traceback.print_exc()
def main():
try:
# Read account id and private from environment variables
#account_id = os.environ['EINSTEIN_VISION_ACCOUNT_ID']
#private_key = os.environ['EINSTEIN_VISION_PRIVATE_KEY'].decode('string_escape')
account_id = '*****@*****.**'
private_key = """
-----BEGIN RSA PRIVATE KEY-----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-----END RSA PRIVATE KEY-----
"""
# Set expiry time
expiry = int(time.time()) + (15 * 60)
# Generate an assertion using RSA private key
assertion = jwt_helper.generate_assertion(account_id, private_key,
expiry)
# Obtain oauth token
token = token_generator.generate_token(assertion)
response = token.json()
# If there is no token print the response
if 'access_token' not in response:
raise ValueError(
"Access token generation failed. Received reply: \"{}\"".
format(response))
else:
# Collect the access token from response
access_token = response['access_token']
# Upload the dataset to einstein.ai
DS = dataset(access_token=access_token)
#path = 'https://raw.githubusercontent.com/kaul-vineet/socialstudio-ml/master/data/intent_tagging.csv'
#response = DS.create_intent_dataset(path)
#print(json.dumps(response, indent=4, sort_keys=True))
# Train the model on einstein.ai []
id = '1127772'
#DS = dataset(access_token=access_token)
response = DS.train_dataset(id)
#if('available' in response):
# print(json.dumps(response, indent=4, sort_keys=True))
#else:
# print('Response status ok?: ' + str(response.ok))
# print(json.dumps(response.text, indent=4, sort_keys=True))
# Check the model training status on einstein.ai []
id = 'YRVFEBIDWGX4I6EBKDOFU5KRQM'
#response = DS.get_train_status(id)
#print(json.dumps(response, indent=4, sort_keys=True))
#data = json.loads(json.dumps(response))
#print ('************ THE MODEL TRAINING IS IN PROGRESS ************')
#while data['status'] != 'SUCCEEDED':
# print ('THE MODEL STATUS IS :' + data['status'])
# time.sleep(30)
#else:
# print ('THE MODEL STATUS IS :' + data['status'] + ' WITH LEARNING RATE OF ' + str(data['learningRate']))
# Check the predictions on einstein.ai []
model_id = 'YRVFEBIDWGX4I6EBKDOFU5KRQM'
document = 'hey guys, im a black trans creative named wondy!! i make art, unfortunately my account was suspended and i lost my 3.5k following and clientele :( please retweet this post so i can get my product back out there as this is my income!! any support is phenomenal'
predict = prediction(access_token=access_token)
response = predict.predict_social_tag(document, model_id)
probabilities = response['probabilities']
max_prob = 0
max_tag = ''
for x in probabilities:
if max_prob < x['probability'] * 100:
max_prob = x['probability'] * 100
max_tag = str(x['label'])
print('There is ' + str(max_prob) + ' probability that this is ' +
max_tag + ' post.')
except Exception as e:
traceback.print_exc()