CODEWITHSUNDEEP
pythonpipelinefastapi
Anastasia_data
Anastasia_data

Reputation: 37

FastAPI: Internal Server Error when returning ML model prediction

I would like to test my pipeline in FastAPI, but I can't find the mistake in my code. When I test it using the Visual Studio Code (using a print() statement), it works. However, when I try accessing the endpoint through the browser, I get Internal Server Error. It works when I return something else as the prediction results (e.g., some string), instead of the actual prediction results.

Here is my code:

class FraudDetection333(BaseModel):
    """
    Input features validation for the ML model
    """
    user_id: int
    signup_day: int
    signup_month: int
    signup_year: int
    purchase_day: int
    purchase_month: int
    purchase_year: int
    purchase_value: float
    source: str
    browser: str
    sex: str
    age: int

@api.post("/predictions_test",tags=['DecisionTreeClassifier'])
def predictions_test(fraud:FraudDetection333):
    """
    :param:input data from the post request
    :return predicted type
    """
    features = [[
    fraud.user_id,
    fraud.signup_day,
    fraud.signup_month,
    fraud.signup_year,
    fraud.purchase_day,
    fraud.purchase_month,
    fraud.purchase_year,
    fraud.purchase_value,
    fraud.source,
    fraud.browser,
    fraud.sex,
    fraud.age
    ]]
    rf_model = joblib.load('./rf_model.pkl')
    new = (pd.DataFrame(features, index = ['0'], columns = ['user_id','signup_day',         
'signup_month', 'signup_year', 
        'purchase_day', 'purchase_month', 'purchase_year','purchase_value',
        'source','browser','sex','age']))
    new_prediction = rf_model.predict(new)
    return {
        "Predicted transaction(1 - fraud, 0 - not fraud)": new_prediction
    }

If I try the below (using a print() statement), it prints out the expected outcome:

featuress={
  "user_id": 22058,
  "signup_day": 24,
  "signup_month": 2,
  "signup_year": 2015,
  "purchase_day": 18,
  "purchase_month": 4,
  "purchase_year": 2015,
  "purchase_value": 34,
  "source": "SEO",
  "browser": "Chrome",
  "sex": "M",
  "age": 39
}
rf_model = joblib.load('./rf_model.pkl')
new = (pd.DataFrame(featuress, index = ['0'], columns = ['user_id','signup_day',     
 'signup_month', 'signup_year', 
        'purchase_day', 'purchase_month', 'purchase_year','purchase_value',
        'source','browser','sex','age']))
new_prediction = rf_model.predict(new)
print(new)
print(new_prediction)

If I type return {"Predicted transaction(1 - fraud, 0 - not fraud)": 'Hi'}, it also works. Image here.

Upvotes: 1

Views: 1348

Answers (1)

Chris
Chris

Reputation: 34045

The model.predict() function returns a numpy.ndarray (you could verify that using print(type(new_prediction))). You can't just return it in that format; hence, the Internal Server Error.

Option 1 is to simply retireve and return the first element of that numpy array:

return {'prediction': new_prediction[0]}

Option 2 is to convert the numpy array into a Python list using the .tolist() method.

return {'prediction': new_prediction.tolist()}

Related answers can be found here and here.

Upvotes: 2

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