Prepare SKlearn model with Isolation Forest to dep

Question

I'm new to machine learning world and working on a project in which I have trained a model for fraud detection using SKlearn.I'm training the model in this way as:

from sklearn.metrics import classification_report, accuracy_score
from sklearn.ensemble import IsolationForest
from sklearn.neighbors import LocalOutlierFactor

# define a random state
state = 1

# define the outlier detection method
classifiers = {
    "Isolation Forest": IsolationForest(max_samples=len(X),
                                       contamination=outlier_fraction,
                                       random_state=state),
    "Local Outlier Factor": LocalOutlierFactor(
    n_neighbors = 20,
    contamination = outlier_fraction)
}

# fit the model
n_outliers = len(Fraud)

for i, (clf_name, clf) in enumerate(classifiers.items()):

    # fit te data and tag outliers
    if clf_name == "Local Outlier Factor":
        y_pred = clf.fit_predict(X)
        scores_pred = clf.negative_outlier_factor_
    else:
        clf.fit(X)
        scores_pred = clf.decision_function(X)
        y_pred = clf.predict(X)

    # Reshape the prediction values to 0 for valid and 1 for fraudulent
    y_pred[y_pred == 1] = 0
    y_pred[y_pred == -1] = 1

    n_errors = (y_pred != Y).sum()

    # run classification metrics 
    print('{}:{}'.format(clf_name, n_errors))
    print(accuracy_score(Y, y_pred ))
    print(classification_report(Y, y_pred ))

And it returns the following output:

Isolation Forest:7
0.93
         precision    recall  f1-score   support

      0       0.97      0.96      0.96        95
      1       0.33      0.40      0.36         5

avg / total   0.94      0.93      0.93       100

Local Outlier Factor:9
0.91
             precision    recall  f1-score   support

          0       0.96      0.95      0.95        95
          1       0.17      0.20      0.18         5

avg / total       0.92      0.91      0.91       100

Now the confusing part is it's deployment, after struggling a lot I have decided to deploy and serve it with the help of a flask web service, but don't understand how I can pass the input to my predict method here to get a prediction?

Is there something have been done in a wrong way?

How can I pass the input to my predict method here?

Help me, please! Any resource for step by step guide to its deployment to google cloud will be very appreciated.

Thanks in advance!