def predict():
"""
The second step is optional and it isn't necessary, you can comment
that lines of code, but if you want to have a robust control of
the data that people post in your API you have to include in a correct
format in the same way that the example in validation.py
"""
if request.method == 'POST':
# Step 1: Extract POST data from request body as JSON
json_data = request.get_json()
_logger.debug(f'Inputs: {json_data}')
# Step 2: Validate the input using marshmallow schema
input_data, errors = validate_inputs(input_data=json_data)
# Step 3: Model prediction
result = make_prediction(input_data=input_data)
_logger.debug(f'Outputs: {result}')
# Step 4: Convert numpy ndarray to list
predictions = result.get('predictions').tolist()
version = result.get('version')
# Step 5: Return the response as JSON
return jsonify({
'predictions': predictions,
'version': version,
'errors': errors
})
def predict():
if request.method == 'POST':
# Step 1: Extract POST data from request body as JSON
json_data = request.get_json()
_logger.debug(f'Inputs: {json_data}')
#Step 2: Validate the input using marshmallow schema
input_data, errors = validate_inputs(input_data=json_data)
# Step 3: Model prediction
result = make_prediction(input_data=json_data)
_logger.debug(f'Outputs: {result}')
# Step 4: Convert numpy ndarray to list
# This step is not happening because we
# already did that while converting probability into
# real values
prediction = result.get('predictions')
version = result.get('version')
# Step 5: Return the response as JSON
return jsonify({
'predictions': prediction,
'errors': errors,
'version': version
})
def predictForUser():
if request.method == 'POST':
# Step 1: Extract POST data from request body as JSON
json_data = {
'Airline': str(request.form['Airline']),
'Date_of_Journey': str(request.form['Date_of_Journey']),
'Source': str(request.form['Source']),
'Destination': str(request.form['Destination']),
'Dep_Time': str(request.form['Dep_Time']),
'Arrival_Time': str(request.form['Arrival_Time']),
'Duration': str(request.form['Duration']),
'Total_Stops': str(request.form['Total_Stops']),
'Additional_Info': str(request.form['Additional_Info']),
}
_logger.debug(f'Inputs: {json_data}')
# Step 2: Validate the input using marshmallow schema
input_data, errors = validate_inputs(input_data=json_data, many=False)
# Step 3: Model prediction
result = make_prediction(input_data=input_data, form_input=True)
_logger.debug(f'Outputs: {result}')
# Step 4: Convert numpy ndarray to list
predictions = result.get('predictions').tolist()
version = result.get('version')
# Step 5: Return the response as JSON
return jsonify({'predictions': predictions,
'version': version,
'errors': errors})
def predict():
if request.method == 'POST':
# Step 1: Extract POST data from request body as JSON
json_data = request.get_json()
_logger.debug(f'Inputs: {json_data}')
#with open('outY.txt', 'w') as f:
# print('Filename:',
# request, request.get_json(),
# '\n\nRESULT', request.__dict__,
# '\n\nHeaders',request.headers,
# file=f) # Python 3.x
# Step 2: Validate the input using marshmallow schema
input_data, errors = validate_inputs(input_data=json_data)
# Step 3: Model prediction
result = make_prediction(input_data=input_data)
_logger.debug(f'Outputs: {result}')
# Step 4: Convert numpy ndarray to list
predictions = result.get('predictions').tolist()
version = result.get('version')
# Step 5: Return the response as JSON
return jsonify({
'predictions': predictions,
'version': version,
'errors': errors
})
else:
return render_template('index.html')
def predict():
if request.method == 'POST':
# Step 1: Extract POST data from request body as JSON
json_data = request.get_json()
_logger.debug(f'Inputs: {json_data}')
# Step 2: Validate the input using marshmallow schema
input_data, errors = validate_inputs(input_data=json_data)
# Step 3: Model prediction
result = make_prediction(input_data=input_data)
_logger.debug(f'Outputs: {result}')
# Step 4: Convert numpy ndarray to list
predictions = result.get('predictions').tolist()
propensity = result.get('propensity').tolist()
version = result.get('version')
# Step 5: Return the response as JSON
return jsonify({
'predictions': predictions,
'propensity': propensity,
'version': version,
'errors': errors
})
def predict():
if request.method == 'POST':
# Step 1: Extract POST data from request body as JSON
json_data = request.get_json()
_logger.info(f'Inputs: {json_data}')
print(f"JSON DATA TYPE {type(json_data)}")
# Step 2: Validate the input using marshmallow schema
input_data, errors = validate_inputs(input_data=json_data)
# Step 3: Model Prediction
print(f"INPUT DATA TYPE {type(input_data)}")
result = make_prediction(input_data=json_data)
_logger.info(f'Outputs: {result}')
# Cast to int because np.array are not recognize by json
# predictions = int(result.get('predictions')[0])
# Step 4: Convert numpy ndarray to list
predictions = result.get("predictions").tolist()
version = result.get('version')
# Step 5: Return the response as JSON
return jsonify({
'predictions': predictions,
'version': version,
'errors': errors
})
def predict():
if request.method == 'POST':
# Step 1: Extract POST data from request body as JSON
json_data = request.get_json()
if (type(json_data) != str):
json_data = pd.DataFrame(json_data)
input_data = json_data.to_json(orient='records')
else:
input_data = json_data
_logger.info(f'Inputs in POST controller: {input_data}')
_logger.info(f'Type of input: {type(input_data)}')
# Step 2: Validate the input using marshmallow schema
validated_data, errors = validate_inputs(input_data=input_data)
_logger.info(f'Inputs: {input_data}')
_logger.info(f'Inputs: {errors}')
# Step 3: Model prediction
result = make_prediction(input_data=validated_data)
# Step 4: Get the prediction and model version
predictions = (result.get('predictions')['Label']).values[0]
version = result.get('version')
_logger.info(f'predictions: {predictions}')
# Step 5: Return the response as JSON
return jsonify({
'predictions': predictions,
'version': version,
'errors': errors
})
def prediction():
if request.method == 'POST':
data = request.form.to_dict(flat=False)
_logger.debug(f'Inputs: {data}')
input_data, errors = validate_inputs(input_data=data)
result = make_prediction(input_data=input_data)
_logger.debug(f'Outputs: {result}')
predictions = result.get('predictions').tolist()
return render_template('prediction.html',
pred='Вероятность ишемической болезни сердца ≈ {}%'\
.format(round(predictions[0][1]*100, 1)))
def inputformpredict():
input_features = [x for x in request.form.values()]
final = np.array(input_features)
data_unseen = pd.DataFrame([final], columns=cols)
input_data = data_unseen.to_json(orient='records')
_logger.info(f'Inputs: {input_data}')
validated_data, errors = validate_inputs(input_data=input_data)
result = make_prediction(input_data=validated_data)
prediction = (result.get('predictions')['Label']).values[0]
#prediction = predict_model(model, data=data_unseen, round = 0)
#prediction = int(prediction.Label[0])
return render_template('home.html',
pred='Expected Bill will be {}'.format(prediction))
def predict():
if request.method == 'POST':
json_data = request.get_json()
_logger.DEBUG(f'inputs:{json_data}')
input_data, errors = validate_inputs(json_data)
result = make_prediction(input_data=input_data)
_logger.DEBUG(f'output:{result}')
predictions = result.get('prediction').tolist()
version = result.get('version')
return (jsonify({
'predictions': predictions,
'version': version,
'errors': errors
}))
def predict():
if request.method == 'POST':
#Extract POST data from request body as JSON
json_data = request.get_json()
_logger.debug(f'Inputs: {json_data}')
#Validate input using marshmallow schema
input_data, errors = validate_inputs(input_data=json_data)
#Model prediction
result = make_prediction(input_data=input_data)
_logger.debug(f'Outputs: {result}')
predictions = result.get('predictions').tolist()
version = result.get('version')
return jsonify({'predictions': predictions,
'version': version,
'errors': errors})
def predict():
if request.method == "POST":
json_data = request.get_json()
_logger.info(f'Inputs: {json_data}')
input_data, errors = validate_inputs(input_json=json_data)
result = make_prediction(input_data=input_data)
_logger.info(f'Outputs: {result}')
predictions = result.get('predictions')[0]
version = result.get('version')
return jsonify({
'Predictions': predictions,
'version': version,
'errors': errors
})
def predict():
if request.method == 'POST':
json_data = request.get_json()
_logger.info(f'Inputs: {json_data}')
input_data, errors = validate_inputs(input_data=json_data)
_logger.info(f'\n!!!\nValidated inputs: {input_data}')
result = make_prediction(input_data=input_data)
_logger.info(f'Outputs: {result}')
predictions = result.get('predictions').tolist()
version = result.get('version')
return jsonify({
'predictions': predictions,
'errors': errors,
'version': version
})
def predict():
if request.method == 'POST':
not_validated_json_data = request.get_json()
#_logger.info(f'inputs: {not_validated_json_data}')
json_data, errors = validate_inputs(input_data=not_validated_json_data)
result = model_predict(input_data=json_data)
_logger.info(f'outputs: {result}')
prediction = result.get('predictions').tolist()
version = result.get('version')
_logger.info(f'predictions: {prediction}')
return jsonify({
'predictions': prediction,
'version': version,
'errors': errors
})
def predict():
if request.method == 'POST':
# Extract POST data from request body
input_data = request.files
# Validate the input data
validated_data, errors = validate_inputs(request.files)
_logger.debug(
f'Number of validated files: {len(validated_data.keys())}')
_logger.debug(f'Number of erroneous files: {len(errors.keys())}')
# Model prediction
audio_files = list(validated_data.values())
result = make_prediction(input_data=audio_files)
_logger.debug(f'Outputs: {result}')
return jsonify({
'predictions': list(result['predictions']),
'version': result['version'],
'errors': errors
})
def predict():
if request.method == 'POST':
# extract 'post' data from request body
json_data = request.get_json()
_logger.info(f'Inputs: {json_data}')
# validate input (using marshmallow schema)
input_data, errors = validate_inputs(input_data=json_data)
# model prediction
result = make_prediction(input_data=input_data)
_logger.info(f'Outputs: {result}')
# convert array to a list
predictions = result.get('predictions').tolist()
version = result.get('version')
# get the response as JSON
return jsonify({'predictions': predictions,
'version': version,
'errors': errors})
def predict():
if request.method == "POST":
# Step 1: Extract POST data from request body as JSON
json_data = request.get_json()
_logger.debug(f"Inputs: {json_data}")
# Step 2: Validate the input using marshmallow schema
input_data, errors = validate_inputs(input_data=json_data)
# Step 3: Model prediction
result = make_prediction(input_data=input_data)
_logger.debug(f"Outputs: {result}")
# Step 4: Convert numpy ndarray to list
predictions = result.get("predictions").tolist()
version = result.get("version")
# Step 5: Return the response as JSON
return jsonify(
{"predictions": predictions, "version": version, "errors": errors}
)
def predict():
if request.method == 'POST':
# Step1: Extract POST data from request body as JSON
json_data = request.get_json()
_logger.info(f'Inputs: {json_data}')
# Step 2: validate the input using marshmallow schema
input_data = json.loads (json_data)
input_data,errors = validate_inputs(input_data=input_data)
# _data = pd.read_json(json_data)
result = make_prediction(input_data=input_data)
_logger.info(f'Outputs: {result}')
predictions = result.get('prediction')[0].tolist()
version = result.get('version')
return jsonify({'predictions': predictions,
'version': version,
'errors': errors
})
def predict():
if request.method == 'POST':
# Step 1: リクエスト本文からJSONとしてPOSTデータを抽出
json_data = request.get_json()
_logger.debug(f'Inputs: {json_data}')
# Step 2: marshmallowスキーマを使用して入力を検証
input_data, errors = validate_inputs(input_data=json_data)
# Step 3: モデル予測
result = make_prediction(input_data=input_data)
_logger.debug(f'Outputs: {result}')
# Step 4: numpy ndarrayをリストに変換
predictions = result.get('predictions').tolist()
version = result.get('version')
# Step 5: 応答をJSONとして返す
return jsonify({
'predictions': predictions,
'version': version,
'errors': errors
})
def question_similarity():
if request.method == 'POST':
# Step 1: Extract POST data from request body as JSON
json_data = request.get_json()
#_logger.debug(f'Inputs: {json_data}')
# Step 2: Validate the input using marshmallow schema
sent1, sent2, errors = validate_inputs(input_data=json_data)
# Step 3: Model prediction
result = ft.question_similarity(sent1, sent2)
#_logger.debug(f'Outputs: {result}')
# Step 4: Convert numpy ndarray to list
predictions = result #.get('predictions').tolist()
version = '0' #result.get('version')
# Step 5: Return the response as JSON
return jsonify({
'predictions': predictions,
'version': version,
'errors': errors
})
def output_specific_model(model_id):
model_id = model_id
# lightgbm_output_v0.1-1588759220.335498
if request.method == 'POST':
# Step 1: Extract POST data from request body as JSON
data = request.get_json()
# Step 2: Validate the input using marshmallow schema
data, errors = validate_inputs(input_data=data)
# Step 3: Model prediction
result = make_prediction(input_data=data, id_model=model_id)
predictions = result.get('predictions').tolist()
version = result.get('version')
return jsonify({
'result': predictions,
'model': model_id,
'version': version,
'errors': errors
})
def version():
if request.method == 'GET':
return jsonify({'model_version': _version,
'api_version': api_version})
@prediction_app.route('/v1/predict/regression', methods=['POST'])
def predict():
if request.method == 'POST':
# Step 1: Extract POST data from request body as JSON
json_data = request.get_json()
_logger.debug(f'Inputs: {json_data}')
# Step 2: Validate the input using marshmallow schema
<<<<<<< HEAD
input_data, errors = validate_inputs(input_data=json_data)
=======
input_data, errors = validate_inputs(input_json=json_data)
>>>>>>> 6162c318b58b225e0061fccd6c64cd67fe205c1b
# Step 3: Model prediction
result = make_prediction(input_data=input_data)
_logger.debug(f'Outputs: {result}')
# Step 4: Convert numpy ndarray to list
predictions = result.get('predictions').tolist()
version = result.get('version')
# Step 5: Return the response as JSON
return jsonify({'predictions': predictions,
'version': version,
