Create app.py

app.py

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+# importing libraries
+from datasets import load_dataset, load_dataset_builder
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import sklearn
+from sklearn.compose import ColumnTransformer
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import OneHotEncoder, LabelEncoder, StandardScaler
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, accuracy_score, precision_score, recall_score, classification_report
+from transformers import Trainer, TrainingArguments
+from skops import hub_utils
+import pickle
+from skops.card import Card, metadata_from_config
+from pathlib import Path
+from tempfile import mkdtemp, mkstemp
+import streamlit as st
+from PIL import Image
+
+# Loading the dataset
+dataset_name = "saifhmb/CreditCardRisk"
+dataset = load_dataset(dataset_name, split = 'train')
+dataset = pd.DataFrame(dataset)
+X = dataset.iloc[:, :-1].values
+y = dataset.iloc[:, -1].values
+
+# Encoding the Independent Variables
+ct = ColumnTransformer(transformers = [('encoder', OneHotEncoder(), [2, 3, 6, 7])], remainder = 'passthrough')
+X = np.array(ct.fit_transform(X))
+
+# Encoding the Dependent Variable
+le = LabelEncoder()
+y = le.fit_transform(y)
+
+# Spliting the datset into Training and Test set
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.15, random_state = 0)
+
+# Feature Scaling
+sc = StandardScaler()
+X_train = sc.fit_transform(X_train)
+X_test = sc.transform(X_test)
+
+# Training Logit Reg Model using the Training set
+model = LogisticRegression()
+model.fit(X_train, y_train)
+
+# Predicting the Test result
+y_pred = model.predict(X_test)
+
+# Making the Confusion Matrix and evaluating performance
+cm = confusion_matrix(y_pred, y_test, labels=model.classes_)
+display_labels = np.array(['bad loss', 'bad profit', 'good risk'])
+disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=display_labels)
+disp.plot()
+plt.show()
+acc = accuracy_score(y_test, y_pred)
+ps = precision_score(y_test, y_pred, average ='micro')
+rs = recall_score(y_test, y_pred, average ='micro')
+
+# Pickling the model
+pickle_out = open("model.pkl", "wb") 
+pickle.dump(model, pickle_out) 
+pickle_out.close()
+
+# Loading the model to predict on the data
+pickle_in = open('model.pkl', 'rb') 
+model = pickle.load(pickle_in) 
+
+def welcome(): 
+    return 'welcome all'
+
+# defining the function which will make the prediction using the data which the user inputs 
+def prediction(AGE, INCOME, GENDER, MARITAL, NUMKIDS, NUMCARDS, HOWPAID, MORTGAGE, STORECAR, LOANS):
+    prediction = model.predict(sc.transform([[AGE, INCOME, GENDER, MARITAL, NUMKIDS, NUMCARDS, HOWPAID, MORTGAGE, STORECAR, LOANS]]))
+    print(prediction)
+    return prediction
+  
+# this is the main function in which we define our webpage  
+def main(): 
+      # giving the webpage a title 
+    st.title("Credit Card Risk Assessment ML App") 
+    st.header("Model Description", divider = "gray")
+    multi = '''This is a logistic regression model trained on customers' credit card risk dataset in a bank using sklearn library. 
+    The model predicts whether a customer is worth issuing a credit card or not.
+    For more details on the model please refer to the model card at https://huggingface.co/saifhmb/Credit-Card-Risk-Model
+    '''
+    st.markdown(multi)
+    st.markdown("To determine whether a customer is worth issuing a credit card or not, please **ENTER** the AGE INCOME, GENDER, MARITAL, NUMKIDS, NUMCARDS, HOWPAID, MORTGAGE, STORECAR, and LOANS:")
+    col1, col2, col3 = st.columns(3)
+    with col1:
+    AGE = st.number_input("AGE")
+    with col2:
+    INCOME = st.number_input("INCOME")
+    with col3:
+    GENDER = st.text_input("GENDER (Please enter 'm' for male and 'f' for female)")
+    
+    col4, col5, col6 = st.columns(3)
+    with col4:
+    MARITAL = st.text_input("MARITAL STATUS (Please enter one of the following options: 'single', 'married', or 'divsepwid')")
+    with col5:
+    NUMKIDS = st.number_input("Number of dependent children")
+    with col6:
+    NUMCARDS = st.number_input("Number of credit cards excluding store credit cards")
+
+    col7, col8, col9 =st.columns(3)
+    with col7:
+    HOWPAID = st.text_input("How often is customer paid by employer (weekly or monthly)")
+    with col8:
+    MORTGAGE = st.text_input("Does customer have a mortgage? please enter 'y' for yes or 'n' for no")
+    with col9:
+    STORECAR = st.number_input("Number of store credit cards")
+
+    LOANS = st.number_input("Number of outstanding loans")  
+    result = ""
+    if st.button("Predict"):
+        result = prediction(AGE, INCOME, GENDER, MARITAL, NUMKIDS, NUMCARDS, HOWPAID, MORTGAGE, STORECAR, LOANS)
+        if result == 0:
+            st.success("The output is {}".format(result) + " which falls under 'bad loss' and thus the customer is NOT worth issuing a credit card")
+        if result == 1:
+            st.success("The output is {}".format(result) + " which falls under 'bad profit' and thus the customer MAYBE worth issuing a credit card")
+        if result == 2:
+            st.success("The output is {}".format(result) + " which falls under 'good risk' and thus the customer worth issuing a credit card")
+
+if __name__=='__main__': 
+    main() 
+
+