Update app.py

app.py

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+import streamlit as st
+import numpy as np
+import pickle
+import streamlit.components.v1 as components
+from sklearn.preprocessing import LabelEncoder
+le = LabelEncoder()
+
+# Load the pickled model
+def load_model():
+    return pickle.load(open('Credit_Card_Classification_LogisticRegression.pkl','rb'))
+
+# Function for model prediction
+def model_prediction(model, features):
+    predicted = str(model.predict(features)[0])
+    return predicted
+    
+def transform(text):
+    text = le.fit_transform(text)
+    return text[0]
+
+def app_design():
+    # Add input fields for High, Open, and Low values
+    image = 'credit.png'
+    st.image(image, use_column_width=True)
+    
+    st.subheader("Enter the following values:")
+
+    Gender= st.selectbox("Gender",('Yes','No'))
+        if Gender == 'Yes':
+            Gender = 1
+        else:
+            Gender = 0
+    Age= st.number_input("Age")
+    Debt= st.number_input("Debt")
+    Married= st.selectbox("Married",('Yes','No'))
+        if Married == 'Yes':
+            Married = 1
+        else:
+            Married = 0
+    BankCustomer= st.number_input("Bank Customer")
+    Industry= st.text_input("Industry")
+    Industry = transform([Industry])
+    Ethnicity= st.text_input("Ethnicity")
+    Ethnicity = transform([Ethnicity])
+    YearsEmployed = st.number_input("Years Employed")
+    PriorDefault= st.selectbox("Prior Default",('Yes','No'))
+        if PriorDefault == 'Yes':
+            PriorDefault = 1
+        else:
+            PriorDefault = 0
+    Employed= st.selectbox("Employed",('Yes','No'))
+        if Employed == 'Yes':
+            Employed = 1
+        else:
+            Employed = 0
+    CreditScore = st.number_input("Credit Score")
+    DriversLicense= st.selectbox("Drivers License",('Yes','No'))
+        if DriversLicense == 'Yes':
+            DriversLicense = 1
+        else:
+            DriversLicense = 0
+    Citizen= st.selectbox("Citizen",('ByBirth','ByOtherMeans'))
+        if Citizen == 'ByBirth':
+            Citizen = 1
+        else:
+            Citizen = 0
+    ZipCode= st.number_input("ZipCode")
+    Income= st.number_input("Income")
+ 
+    # Create a feature list from the user inputs
+    features = [[Gender, Age,Debt,Married,BankCustomer,Industry,Ethnicity,YearsEmployed,PriorDefault,Employed,CreditScore,DriversLicense,Citizen,ZipCode,Income]]
+    
+    # Load the model
+    model = load_model()
+    
+    # Make a prediction when the user clicks the "Predict" button
+    if st.button('Predict Status'):
+        predicted_value = model_prediction(model, features)
+        if(predicted_value==1):
+            st.success(f"The credit card is approved") 
+
+        else:
+            st.success(f"The credit card is not approved") 
+        
+
+
+
+def main():
+
+        # Set the app title and add your website name and logo
+        st.set_page_config(
+        page_title="Credit Card Classification Model",
+        page_icon=":chart_with_upwards_trend:",
+        )
+
+        
+        st.title("Welcome to our Credit Card Classification Model!")
+        app_design()
+
+    
+if __name__ == '__main__':
+        main()