ml request customization

Pipfile

@@ -0,0 +1,11 @@
+[[source]]
+name = "pypi"
+url = "https://pypi.org/simple"
+verify_ssl = true
+
+[dev-packages]
+
+[packages]
+
+[requires]
+python_version = "3.6"

predict.py

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+import numpy as np
+import sys
+import pickle
+
+model = pickle.load(open('final_prediction.pickle', 'rb'))
+
+
+def predict(argv):
+
+    int_features = [int(x) for x in argv[1:]]
+    final_features = [np.array(int_features)]
+    prediction = model.predict(final_features)
+
+    output = prediction[0]
+
+    return output
+
+
+if __name__ == '__main__':
+    predict(sys.argv)
+
+
+# app = Flask(__name__)
+# from flask import Flask, request, jsonify, render_template
+
+
+# @app.route('/')
+# def home():
+#     return render_template('index.html')
+
+
+# @app.route('/predict', methods=['POST'])
+# def predict():
+
+#     int_features = [int(x) for x in request.form.values()]
+#     final_features = [np.array(int_features)]
+#     prediction = model.predict(final_features)
+
+#     output = round(prediction[0], 2)
+
+#     return render_template('index.html', prediction_text='This transaction is likely to be fraud in percentage by $ {}'.format(output))
+
+
+# @app.route('/results', methods=['POST'])
+# def results():
+
+#     data = request.get_json(force=True)
+#     prediction = model.predict([np.array(list(data.values()))])
+
+#     output = prediction[0]
+#     return jsonify(output)
+
+
+# if __name__ == "__main__":
+#     app.run(debug=True)