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Endpoint
Makes fraud predictions on new transaction data using the trained model. This endpoint accepts data file paths and returns predictions in a CSV file.
Request Body
The endpoint accepts requests in two formats: JSON or form data .
The file system path to the folder containing prediction data files. This folder should contain properly formatted CSV files with transaction data to classify.
{
"filepath" : "/path/to/prediction/data"
}
The file system path to the folder containing prediction data files, sent as form data.
Prediction Workflow
When the /predict endpoint is called, the following process is executed:
Validation - The pred_validation object validates the prediction data structure
Data Processing - Prediction data is validated and prepared for inference
Prediction - The prediction object loads the trained model and generates predictions
Output - Predictions are saved to a CSV file
pred_val = pred_validation(path)
pred_val.prediction_validation()
pred = prediction(path)
path = pred.predictionFromModel()
Response
Returns the file path where the prediction results have been saved.
Success Response
Prediction File created at /path/to/predictions/Predictions.csv!!!
The response includes the full path to the generated predictions file.
Error Responses
Returns an error message if prediction fails.
Possible error responses:
Error Occurred! <class 'ValueError'>
Error Occurred! <class 'KeyError'>
Error Occurred! {exception_message}
Example Requests
cURL (JSON)
curl -X POST http://127.0.0.1:5001/predict \
-H "Content-Type: application/json" \
-d '{
"filepath": "/home/user/fraud_detection/prediction_data"
}'
curl -X POST http://127.0.0.1:5001/predict \
-F "filepath=/home/user/fraud_detection/prediction_data"
Python (requests - JSON)
import requests
url = "http://127.0.0.1:5001/predict"
payload = {
"filepath" : "/home/user/fraud_detection/prediction_data"
}
response = requests.post(url, json = payload)
print (response.text)
# Output: Prediction File created at /path/to/predictions/Predictions.csv!!!
import requests
url = "http://127.0.0.1:5001/predict"
data = {
"filepath" : "/home/user/fraud_detection/prediction_data"
}
response = requests.post(url, data = data)
print (response.text)
JavaScript (fetch - JSON)
fetch ( 'http://127.0.0.1:5001/predict' , {
method: 'POST' ,
headers: {
'Content-Type' : 'application/json' ,
},
body: JSON . stringify ({
filepath: '/home/user/fraud_detection/prediction_data'
})
})
. then ( response => response . text ())
. then ( data => console . log ( data ));
Error Handling
The endpoint handles the following error cases:
Raised when the provided file path or data format is invalid. Error Occurred! <class 'ValueError'>
Raised when the required filepath parameter is missing from the request. Error Occurred! <class 'KeyError'>
Catches any other exceptions during the prediction process and returns the specific error message. Error Occurred! {specific_error_message}
Implementation Details
The endpoint implementation from main.py:25-61:
@app.route ( "/predict" , methods = [ 'POST' ])
@cross_origin ()
def predictRouteClient ():
try :
if request.json is not None :
path = request.json[ 'filepath' ]
pred_val = pred_validation(path)
pred_val.prediction_validation()
pred = prediction(path)
path = pred.predictionFromModel()
return Response( "Prediction File created at %s !!!" % path)
elif request.form is not None :
path = request.form[ 'filepath' ]
pred_val = pred_validation(path)
pred_val.prediction_validation()
pred = prediction(path)
path = pred.predictionFromModel()
return Response( "Prediction File created at %s !!!" % path)
except ValueError :
return Response( "Error Occurred! %s " % ValueError )
except KeyError :
return Response( "Error Occurred! %s " % KeyError )
except Exception as e:
return Response( "Error Occurred! %s " % e)
Notes
The endpoint accepts both JSON and form data formats for flexibility. Both formats trigger the same prediction workflow and return identical results.
Prediction results are automatically saved to a CSV file. The exact location is determined by the prediction module configuration.