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app.py

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+import gradio as gr
+import pandas as pd
+import pickle
+
+import os
+MAIN_FOLDER = os.path.dirname(__file__)
+
+# Define params names
+PARAMS_NAME = [
+    "orderAmount",
+    "orderState",
+    "paymentMethodRegistrationFailure",
+    "paymentMethodType",
+    "paymentMethodProvider",
+    "paymentMethodIssuer",
+    "transactionAmount",
+    "transactionFailed",
+    "emailDomain",
+    "emailProvider",
+    "customerIPAddressSimplified",
+    "sameCity"
+]
+
+# Load model
+with open("model/modelo_proyecto_final.pkl", "rb") as f:
+    model = pickle.load(f)
+
+# Columnas
+COLUMNS_PATH = "model/categories_ohe_without_fraudulent.pickle"
+with open(COLUMNS_PATH, 'rb') as handle:
+    ohe_tr = pickle.load(handle)
+
+BINS_ORDER = os.path.join(MAIN_FOLDER, "model/saved_bins_order.pickle")
+with open(BINS_ORDER, 'rb') as handle:
+    new_saved_bins_order = pickle.load(handle)
+
+BINS_TRANSACTION = os.path.join(MAIN_FOLDER, "model/saved_bins_transaction.pickle")
+with open(BINS_TRANSACTION, 'rb') as handle:
+    new_saved_bins_transaction = pickle.load(handle)
+
+def predict(*args):
+    answer_dict = {}
+
+    for i in range(len(PARAMS_NAME)):
+        answer_dict[PARAMS_NAME[i]] = [args[i]]
+
+    # Crear dataframe
+    single_instance = pd.DataFrame.from_dict(answer_dict)
+
+    # Manejar puntos de corte o bins
+    single_instance["orderAmount"] = single_instance["orderAmount"].astype(float)
+    single_instance["orderAmount"] = pd.cut(single_instance['orderAmount'],
+                                 bins=new_saved_bins_order, 
+                                 include_lowest=True)
+
+    single_instance["transactionAmount"] = single_instance["transactionAmount"].astype(int)
+    single_instance["transactionAmount"] = pd.cut(single_instance['transactionAmount'],
+                                 bins=new_saved_bins_order, 
+                                 include_lowest=True)
+
+    # One hot encoding
+    single_instance_ohe = pd.get_dummies(single_instance).reindex(columns = ohe_tr).fillna(0)
+
+    prediction = model.predict(single_instance_ohe)
+
+    # Cast numpy.int64 to just a int
+    type_of_fraud = int(prediction[0])
+
+    # Adaptación respuesta
+    response = "Error parsing value"
+    if type_of_fraud == 0:
+        response = "False"
+    if type_of_fraud == 1:
+        response = "True"
+    if type_of_fraud == 2:
+        response = "Warning"
+
+    return response
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # Prevención de Fraude 🔍 🔍 
+        """
+    )
+
+    with gr.Row():
+        with gr.Column():
+
+            gr.Markdown(
+                """
+                ## Predecir si un cliente es fraudulento o no.
+                """
+            )
+            
+            orderAmount = gr.Slider(label="Order amount", minimum=0, maximum=355, step=2, randomize=True)
+
+            orderState = gr.Radio(
+                label="Order state",
+                choices=["fulfilled", "failed", "pending"],
+                value="failed"
+                )
+
+            paymentMethodRegistrationFailure = gr.Radio(
+                label="Payment method registration failure",
+                choices=["False", "True"],
+                value="True"
+                )
+
+            paymentMethodType = gr.Radio(
+                label="Payment method type",
+                choices=["card", "apple pay ", "paypal", "bitcoin"],
+                value="bitcoin"
+                )
+
+            paymentMethodProvider = gr.Dropdown(
+                label="Payment method provider",
+                choices=["JCB 16 digit", "VISA 16 digit", "Voyager", "Diners Club / Carte Blanche", "Maestro", "VISA 13 digit", "Discover", "American Express", "JCB 15 digit", "Mastercard"],
+                multiselect=False,
+                value="American Express"
+                )
+            
+            paymentMethodIssuer = gr.Dropdown(
+                label="Payment method issuer",
+                choices=["Her Majesty Trust", "Vertex Bancorp", "Fountain Financial Inc.", "His Majesty Bank Corp.", "Bastion Banks", "Bulwark Trust Corp.", "weird", "Citizens First Banks", "Grand Credit Corporation", "Solace Banks", "Rose Bancshares"],
+                multiselect=False,
+                value="Bastion Banks"
+                )
+            
+            transactionAmount = gr.Slider(label="Transaction amount", minimum=0, maximum=355, step=2, randomize=True)
+
+            transactionFailed = gr.Radio(
+                label="Transaction failed",
+                choices=["False", "True"],
+                value="False"
+                )
+
+            emailDomain = gr.Radio(
+                label="Email domain",
+                choices=["com", "biz", "org", "net", "info", "weird"],
+                value="com"
+                )
+
+            emailProvider = gr.Radio(
+                label="Email provider",
+                choices=["gmail", "hotmail", "yahoo", "other", "weird"],
+                value="gmail"
+                )
+
+            customerIPAddressSimplified = gr.Radio(
+                label="Customer IP Address",
+                choices=["only_letters", "digits_and_letters"],
+                value="only_letter"
+                )
+
+            sameCity = gr.Radio(
+                label="Same city",
+                choices=["unknown", "no", "yes"],
+                value="unknown"
+                )
+
+        with gr.Column():
+
+            gr.Markdown(
+                """
+                ## Predicción
+                """
+            )
+
+            label = gr.Label(label="Score")
+            predict_btn = gr.Button(value="Evaluar")
+            predict_btn.click(
+            predict,
+            inputs=[
+                orderAmount,
+                orderState,
+                paymentMethodRegistrationFailure,
+                paymentMethodType,
+                paymentMethodProvider,
+                paymentMethodIssuer,
+                transactionAmount,
+                transactionFailed,
+                emailDomain,
+                emailProvider,
+                customerIPAddressSimplified,
+                sameCity,
+            ],
+            outputs=[label],
+            api_name="prediccion"
+            )
+    gr.Markdown(
+        """
+        <p style='text-align: center'>
+            <a >Proyecto Final Kari 
+            </a>
+        </p>
+        """
+    )
+
+demo.launch(share=True)

requirements.txt

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+gradio
+pandas
+scikit-learn
+