Added Streamlit loan approval app

bankloan.py

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+import streamlit as st
+import joblib
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+# Load the trained model from Hugging Face
+model_path = hf_hub_download(repo_id="ifiecas/LoanApproval-DT-v1.0", filename="best_pruned_dt.pkl")
+model = joblib.load(model_path)
+
+# Streamlit app title
+st.title("🏦 AI-Powered Loan Approval System")
+st.write("Enter your details to check your loan approval status.")
+
+# Input fields
+applicant_income = st.number_input("Applicant's Monthly Income ($)", min_value=0)
+coapplicant_income = st.number_input("Co-Applicant's Monthly Income ($)", min_value=0)
+loan_amount = st.number_input("Loan Amount Requested ($)", min_value=0)
+loan_term = st.number_input("Loan Term (days)", min_value=0, value=360)
+credit_history = st.selectbox("Credit History", [1, 0], format_func=lambda x: "Good (1)" if x == 1 else "Bad (0)")
+gender = st.selectbox("Gender", ["Male", "Female"])
+marital_status = st.selectbox("Marital Status", ["Married", "Not Married"])
+education = st.selectbox("Education Level", ["Graduate", "Under Graduate"])
+self_employed = st.selectbox("Self-Employed", ["Yes", "No"]) 
+location = st.selectbox("Property Location", ["Urban", "Semiurban", "Rural"])
+
+def preprocess_input():
+    # Convert categorical inputs to numerical format (you may need encoding based on your dataset)
+    gender_num = 1 if gender == "Male" else 0
+    marital_status_num = 1 if marital_status == "Married" else 0
+    education_num = 1 if education == "Graduate" else 0
+    self_employed_num = 1 if self_employed == "Yes" else 0
+    location_num = {"Urban": 2, "Semiurban": 1, "Rural": 0}[location]
+
+    return np.array([[
+        applicant_income, coapplicant_income, loan_amount, loan_term, credit_history,
+        gender_num, marital_status_num, education_num, self_employed_num, location_num
+    ]])
+
+# Predict button
+if st.button("Check Loan Approval"):
+    input_data = preprocess_input()
+    prediction = model.predict(input_data)
+    result = "✅ Approved" if prediction[0] == "Y" else "❌ Rejected"
+    st.subheader(f"Loan Status: {result}")
+
+st.write("📌 AI-driven decision-making for faster loan approvals.")

requirements.txt

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+streamlit
+joblib
+numpy
+scikit-learn
+huggingface_hub