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Testing Azure Function
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Paul committed May 16, 2024
1 parent cf8b649 commit d1dbf22
Showing 1 changed file with 56 additions and 56 deletions.
112 changes: 56 additions & 56 deletions azure/function_app.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,16 +12,16 @@

flask_app = Flask(__name__)

with open('models/logistic_regression.pkl', 'rb') as f:
logistic_regression_model = pickle.load(f)
with open('models/naive_bayes.pkl', 'rb') as f:
naive_bayes_model = pickle.load(f)
with open('models/sklearn_vectorizer.pkl', 'rb') as f:
sklearn_vectorizer = pickle.load(f)
lstm_model = tf.keras.models.load_model('models/lstm.h5', compile=False)
lstm_vectorizer = tf.keras.models.load_model('models/tf_vectorizer')
transformers_vectorizer = tf.keras.models.load_model('models/tf_vectorizer_t')
transformers_model = tf.keras.models.load_model('models/transformers', compile=False)
# with open('./models/logistic_regression.pkl', 'rb') as f:
# logistic_regression_model = pickle.load(f)
# with open('./models/naive_bayes.pkl', 'rb') as f:
# naive_bayes_model = pickle.load(f)
# with open('./models/sklearn_vectorizer.pkl', 'rb') as f:
# sklearn_vectorizer = pickle.load(f)
# lstm_model = tf.keras.models.load_model('./models/lstm.h5', compile=False)
# lstm_vectorizer = tf.keras.models.load_model('./models/tf_vectorizer')
# transformers_vectorizer = tf.keras.models.load_model('./models/tf_vectorizer_t')
# transformers_model = tf.keras.models.load_model('./models/transformers', compile=False)

batch_size = 32

Expand All @@ -44,52 +44,52 @@ def tokenize_text(x):
def basic_test():
return [1], 200

@flask_app.post("/predict")
def run_ai():
if request.headers['aikey'] == os.environ['AIKEY']:
if request.is_json:
try:
json_body = request.get_json()
data = json_body['json_data']
model_choice = json_body['model_choice']
# logging.info('successfully retrieved data')
data = pd.DataFrame.from_dict(json.loads(data))
# data was reordered by string indexing as it was serialized and sent
data = data.loc[list(map(str, range(len(data.index))))]
# logging.info('successfully converted data to data frame')
if model_choice != 'Vote':
predictions = {model_choice: None}
else:
predictions = {'Transformers': None, 'LSTM': None, 'Logistic Regression': None, 'Naive Bayes': None}
data['0'] = data['0'].apply(filter_thai)
if 'Logistic Regression' in predictions.keys():
predictions['Logistic Regression'] = logistic_regression_model.predict_proba(
sklearn_vectorizer.transform(data['0']).toarray()
).tolist()
if 'Naive Bayes' in predictions.keys():
predictions['Naive Bayes'] = naive_bayes_model.predict_proba(
sklearn_vectorizer.transform(data['0']).toarray()
).tolist()
data['0'] = data['0'].apply(tokenize_text)
if 'LSTM' in predictions.keys():
predictions['LSTM'] = np.concatenate([lstm_model(lstm_vectorizer(
data.iloc[(batch_size * i):min((batch_size * (i + 1)), len(data.index))])).numpy() for i in
range(1 + len(data.index) // batch_size)], axis=0).tolist()
if 'Transformers' in predictions.keys():
predictions['Transformers'] = np.concatenate([transformers_model(transformers_vectorizer(
data.iloc[(batch_size * i):min((batch_size * (i + 1)), len(data.index))])).numpy() for i in
range(1 + len(data.index) // batch_size)],
axis=0).tolist()
# logging.info('successfully ran prediction models')
return {'predictions': predictions,
'message': tf.config.list_physical_devices('GPU')
}, 201
except Exception as e:
return {"error": str(e)}, 400
else:
return {"error": "Request must be JSON"}, 415
else:
return {'error': 'Wrong key'}, 401
# @flask_app.post("/predict")
# def run_ai():
# if request.headers['aikey'] == os.environ['AIKEY']:
# if request.is_json:
# try:
# json_body = request.get_json()
# data = json_body['json_data']
# model_choice = json_body['model_choice']
# # logging.info('successfully retrieved data')
# data = pd.DataFrame.from_dict(json.loads(data))
# # data was reordered by string indexing as it was serialized and sent
# data = data.loc[list(map(str, range(len(data.index))))]
# # logging.info('successfully converted data to data frame')
# if model_choice != 'Vote':
# predictions = {model_choice: None}
# else:
# predictions = {'Transformers': None, 'LSTM': None, 'Logistic Regression': None, 'Naive Bayes': None}
# data['0'] = data['0'].apply(filter_thai)
# if 'Logistic Regression' in predictions.keys():
# predictions['Logistic Regression'] = logistic_regression_model.predict_proba(
# sklearn_vectorizer.transform(data['0']).toarray()
# ).tolist()
# if 'Naive Bayes' in predictions.keys():
# predictions['Naive Bayes'] = naive_bayes_model.predict_proba(
# sklearn_vectorizer.transform(data['0']).toarray()
# ).tolist()
# data['0'] = data['0'].apply(tokenize_text)
# if 'LSTM' in predictions.keys():
# predictions['LSTM'] = np.concatenate([lstm_model(lstm_vectorizer(
# data.iloc[(batch_size * i):min((batch_size * (i + 1)), len(data.index))])).numpy() for i in
# range(1 + len(data.index) // batch_size)], axis=0).tolist()
# if 'Transformers' in predictions.keys():
# predictions['Transformers'] = np.concatenate([transformers_model(transformers_vectorizer(
# data.iloc[(batch_size * i):min((batch_size * (i + 1)), len(data.index))])).numpy() for i in
# range(1 + len(data.index) // batch_size)],
# axis=0).tolist()
# # logging.info('successfully ran prediction models')
# return {'predictions': predictions,
# 'message': tf.config.list_physical_devices('GPU')
# }, 201
# except Exception as e:
# return {"error": str(e)}, 400
# else:
# return {"error": "Request must be JSON"}, 415
# else:
# return {'error': 'Wrong key'}, 401


app = func.WsgiFunctionApp(app=flask_app.wsgi_app,
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