Delete app.py

app.py

@@ -1,62 +0,0 @@
-import streamlit as st
-import pandas as pd
-import pickle
-
-st.image('images.jpeg')
-# Load the pickled model
-loaded_pickle_model = pickle.load(open("random_forest_model.pkl", "rb"))
-
-def predict_loan_approval(data):
-    # Use the loaded model to make predictions
-    prediction = loaded_pickle_model.predict(data)
-    return prediction
-
-def main():
-    st.title("Loan Approval Prediction")
-
-    # Input form for user to enter data
-    st.header("Input Data")
-    gender = st.selectbox("Gender", ["Male", "Female"])
-    married = st.selectbox("Married", ["Yes", "No"])
-    dependents = st.number_input("Dependents", min_value=0, max_value=10, value=0)
-    education = st.selectbox("Education", ["Graduate", "Not Graduate"])
-    self_employed = st.selectbox("Self Employed", ["Yes", "No"])
-    applicant_income = st.number_input("Applicant Income", value=0)
-    coapplicant_income = st.number_input("Coapplicant Income", value=0)
-    loan_amount = st.number_input("Loan Amount", value=0)
-    loan_amount_term = st.number_input("Loan Amount Term", value=0)
-    credit_history = st.selectbox("Credit History", [0.0, 1.0])
-    property_area = st.selectbox("Property Area", ["Urban", "Semiurban", "Rural"])
-
-    # Mapping input values to numerical values
-    gender_map = {'Male': 1, 'Female': 0}
-    married_map = {'Yes': 1, 'No': 0}
-    education_map = {'Graduate': 1, 'Not Graduate': 0}
-    self_employed_map = {'Yes': 1, 'No': 0}
-    property_area_map = {'Urban': 0, 'Semiurban': 1, 'Rural': 2}
-
-    # Create a DataFrame from the input data
-    new_data = pd.DataFrame({
-        'Gender': [gender_map[gender]],
-        'Married': [married_map[married]],
-        'Dependents': [dependents],
-        'Education': [education_map[education]],
-        'Self_Employed': [self_employed_map[self_employed]],
-        'ApplicantIncome': [applicant_income],
-        'CoapplicantIncome': [coapplicant_income],
-        'LoanAmount': [loan_amount],
-        'Loan_Amount_Term': [loan_amount_term],
-        'Credit_History': [credit_history],
-        'Property_Area': [property_area_map[property_area]]
-    })
-
-    # Button to predict loan approval
-    if st.button("Predict Loan Approval"):
-        prediction = predict_loan_approval(new_data)
-        if prediction[0] == 1:
-            st.success("Loan is Approved 👍")
-        else:
-            st.error("Loan is Rejected 👎")
-
-if __name__ == "__main__":
-    main()